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Climate change is one of the greatest problems society has ever faced, with increasingly severe consequences for humanity as natural disasters multiply, sea levels rise, and ecosystems falter. Since climate change is a complex issue, action takes many forms, from designing smart electric grids to tracking greenhouse gas emissions through satellite imagery. While no silver bullet, machine learning can be an invaluable tool in fighting climate change via a wide array of applications and techniques. These applications require algorithmic innovations in machine learning and close collaboration with diverse fields and practitioners. This workshop is intended as a forum for those in the machine learning community who wish to help tackle climate change. Building on our past workshops on this topic, this workshop aims to especially emphasize the pipeline to impact, through conversations about machine learning with decision-makers and other global leaders in implementing climate change strategies. The all-virtual format of NeurIPS 2020 provides a special opportunity to foster cross-pollination between researchers in machine learning and experts in complementary fields.
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Fri 3:00 a.m. - 3:35 a.m.
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Welcome and opening remarks
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Introductory remarks
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Fri 4:00 a.m. - 4:05 a.m.
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Introduction to Spotlights
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Live introduction
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Fri 4:05 a.m. - 4:15 a.m.
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Spotlight: Deep Learning for Climate Model Output Statistics
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Spotlight talk
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SlidesLive Video » Climate models are an important tool for the assessment of prospective climate change effects but they suffer from systematic and representation errors, especially for precipitation. Model output statistics (MOS) reduce these errors by fitting the model output to observational data with machine learning. In this work, we explore the feasibility and potential of deep learning with convolutional neural networks (CNNs) for MOS. We propose the CNN architecture ConvMOS specifically designed for reducing errors in climate model outputs and apply it to the climate model REMO. Our results show a considerable reduction of errors and mostly improved performance compared to three commonly used MOS approaches. |
Michael Steininger 🔗 |
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Fri 4:15 a.m. - 4:22 a.m.
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Spotlight: An Enriched Automated PV Registry: Combining Image Recognition and 3D Building Data
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Spotlight talk
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While photovoltaic (PV) systems are installed at an unprecedented rate, reliable information on an installation level remains scarce. As a result, automatically created PV registries are a timely contribution to optimize grid planning and operations. This paper demonstrates how aerial imagery and three-dimensional building data can be combined to create an address-level PV registry, specifying area, tilt, and orientation angles. We demonstrate the benefits of this approach for PV capacity estimation. In addition, this work presents, for the first time, a comparison between automated and officially-created PV registries. Our results indicate that our enriched automated registry proves to be useful to validate, update, and complement official registries. |
Kevin Mayer 🔗 |
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Fri 4:22 a.m. - 4:32 a.m.
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Spotlight: Interpretability in Convolutional Neural Networks for Building Damage Classification in Satellite Imagery
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Spotlight talk
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SlidesLive Video » Natural disasters ravage the world's cities, valleys, and shores on a monthly basis. Having precise and efficient mechanisms for assessing infrastructure damage is essential to channel resources and minimize the loss of life. Using a dataset that includes labeled pre- and post- disaster satellite imagery, we train multiple convolutional neural networks to assess building damage on a per-building basis. In order to investigate how to best classify building damage, we present a highly interpretable deep-learning methodology that seeks to explicitly convey the most useful information required to train an accurate classification model. We also delve into which loss functions best optimize these models. Our findings include that ordinal-cross entropy loss is the most optimal loss function to use and that including the type of disaster that caused the damage in combination with a pre- and post-disaster image best predicts the level of damage caused. Our research seeks to computationally contribute to aiding in this ongoing and growing humanitarian crisis, heightened by climate change. |
Thomas Chen 🔗 |
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Fri 4:32 a.m. - 4:42 a.m.
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Spotlight: A Machine Learning Approach to Methane Emissions Mitigation in the Oil and Gas Industry
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Spotlight talk
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SlidesLive Video »
Reducing methane emissions from the oil and gas sector is a key component of climate policy in the United States. Methane leaks across the supply chain are stochastic and intermittent, with a small number of sites (‘super-emitters’) responsible for a majority of emissions. Thus, cost-effective emissions reduction critically relies on effectively identifying the super-emitters from thousands of well-sites and millions of miles of pipelines. Conventional approaches such as walking surveys using optical gas imaging technology are slow and time-consuming. In addition, several variables contribute to the formation of leaks such as infrastructure age, production, weather conditions, and maintenance practices. Here, we develop a machine learning algorithm to predict high-emitting sites that can be prioritized for follow-up repair. Such prioritization can significantly reduce the cost of surveys and increase emissions reductions compared to conventional approaches. Our results show that the algorithm using logistic regression performs the best out of several algorithms. The model achieved a 70% accuracy rate with a 57% recall and a 66% balanced accuracy rate. Compared to the conventional approach, the machine learning model reduced the time to achieve a 50% emissions mitigation target by 42%. Correspondingly, the mitigation cost reduced from $85/t CO2e to $49/t CO2e.
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Jiayang Wang 🔗 |
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Fri 4:42 a.m. - 4:52 a.m.
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Spotlight: RainBench: Enabling Data-Driven Precipitation Forecasting on a Global Scale
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Spotlight talk
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SlidesLive Video » Climate change is expected to aggravate extreme precipitation events, directly impacting the livelihood of millions. Without a global precipitation forecasting system in place, many regions -- especially those constrained in resources to collect expensive groundstation data -- are left behind. To mitigate such unequal reach of climate change, a solution is to alleviate the reliance on numerical models (and by extension groundstation data) by enabling machine-learning-based global forecasts from satellite imagery. Though prior works exist in regional precipitation nowcasting, there lacks work in global, medium-term precipitation forecasting. Importantly, a common, accessible baseline for meaningful comparison is absent. In this work, we present RainBench, a multi-modal benchmark dataset dedicated to advancing global precipitation forecasting. We establish baseline tasks and release PyRain, a data-handling pipeline to enable efficient processing of decades-worth of data by any modeling framework. Whilst our work serves as a basis for a new chapter on global precipitation forecast from satellite imagery, the greater promise lies in the community joining forces to use our released datasets and tools in developing machine learning approaches to tackle this important challenge. |
Catherine Tong 🔗 |
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Fri 4:52 a.m. - 5:00 a.m.
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Introduction to first poster session
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Live introduction
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Fri 5:00 a.m. - 6:00 a.m.
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Poster session 1
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Poster session
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Fri 6:00 a.m. - 6:09 a.m.
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Introduction to Spotlights
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Live introduction
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Fri 6:09 a.m. - 6:19 a.m.
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Spotlight: The Peruvian Amazon Forestry Dataset: A Leaf Image Classification Corpus
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Spotlight talk
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This paper introduces the Peruvian Amazon Forestry Dataset, which includes 59,441 leaves samples from ten of the most profitable and endangered Amazon timber-tree species. Besides, the proposal includes a background removal algorithm to feed a fine-tuned CNN. We evaluate the quantitative (accuracy metric) and qualitative (visual interpretation) impacts of each stage by ablation experiments. The results show a 96.64 % training accuracy and 96.52 % testing accuracy on the VGG-19 model. Furthermore, the visual interpretation of the model evidences that leaf venations have the highest correlation in the plant recognition task. |
Gerson Waldyr Vizcarra Aguilar 🔗 |
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Fri 6:19 a.m. - 6:25 a.m.
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Spotlight: Data-driven modeling of cooling demand in a commercial building
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Spotlight talk
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Heating, ventilation, and air conditioning (HVAC) systems account for 30% of the total energy consumption in buildings. Design and implementation of energy-efficient schemes can play a pivotal role in minimizing energy usage. As an important first step towards improved HVAC system controls, this study proposes a new framework for modeling the thermal response of buildings by leveraging data measurements and formulating a data-driven system identification model. The proposed method combines principal component analysis (PCA) to identify the most significant predictors that influence the cooling demand of a building with an auto-regressive integrated moving average with exogenous variables (ARIMAX) model. The performance of the developed model was evaluated both analytically and visually. It was found that our PCA-based ARIMAX (2-0-5) model was able to accurately forecast the cooling demand for the prediction horizon of 7 days. In this work, the actual measurements from a university campus building are used for model development and validation. |
Aqsa Naeem 🔗 |
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Fri 6:25 a.m. - 6:37 a.m.
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Spotlight: Structural Forecasting for Tropical Cyclone Intensity Prediction: Providing Insight with Deep Learning
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Spotlight talk
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Tropical cyclone (TC) intensity forecasts are ultimately issued by human forecasters. The human in-the-loop pipeline requires that any forecasting guidance must be easily digestible by TC experts if it is to be adopted at operational centers like the National Hurricane Center. Our proposed framework leverages deep learning to provide forecasters with something neither end-to-end prediction models nor traditional intensity guidance does: a powerful tool for monitoring high-dimensional time series of key physically relevant predictors and the means to understand how the predictors relate to one another and to short-term intensity changes. |
Trey McNeely 🔗 |
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Fri 6:37 a.m. - 6:47 a.m.
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Spotlight: FireSRnet: Geoscience-driven super-resolution of future fire risk from climate change
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Spotlight talk
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SlidesLive Video » With fires becoming increasingly frequent and severe across the globe in recent years, understanding climate change’s role in fire behavior is critical for quantifying current and future fire risk. However, global climate models typically simulate fire behavior at spatial scales too coarse for local risk assessments. Therefore, we propose a novel approach towards super-resolution (SR) enhancement of fire risk exposure maps that incorporates not only 2000 to 2020 monthly satellite observations of active fires but also local information on land cover and temperature. Inspired by SR architectures, we propose an efficient deep learning model trained for SR on fire risk exposure maps. We evaluate this model on resolution enhancement and find it outperforms standard image interpolation techniques at both 4x and 8x enhancement while having comparable performance at 2x enhancement. We then demonstrate the generalizability of this SR model over northern California and New South Wales, Australia. We conclude with a discussion and application of our proposed model to climate model simulations of fire risk in 2040 and 2100, illustrating the potential for SR enhancement of fire risk maps from the latest state-of-the-art climate models. |
Tristan Ballard 🔗 |
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Fri 6:47 a.m. - 6:57 a.m.
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Spotlight: Spatiotemporal Features Improve Fine-Grained Butterfly Image Classification
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Spotlight talk
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SlidesLive Video » Understanding the changing distributions of butterflies gives insight into the impacts of climate change across ecosystems and is a prerequisite for conservation efforts. eButterfly is a citizen science website created to allow people to track the butterfly species around them and use these observations to contribute to research. However, correctly identifying butterfly species is a challenging task for non-specialists and currently requires the involvement of entomologists to verify the labels of novice users on the website. We have developed a computer vision model to label butterfly images from eButterfly automatically, decreasing the need for human experts. We employ a model that incorporates geographic and temporal information of where and when the image was taken, in addition to the image itself. We show that we can successfully apply this spatiotemporal model for fine-grained image recognition, significantly improving the accuracy of our classification model compared to a baseline image recognition system trained on the same dataset. |
Marta Skreta 🔗 |
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Fri 7:00 a.m. - 8:00 a.m.
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Climate Change and ML for Policy
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Discussion Panel
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Angel Hsu · Dava Newman · James Rattling Leaf, Sr. · Mouhamadou M Cisse 🔗 |
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Fri 8:00 a.m. - 9:00 a.m.
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Poster session 2
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Poster session
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Fri 9:00 a.m. - 9:10 a.m.
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Introduction to Zico Kolter
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Live introduction
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Fri 9:40 a.m. - 10:00 a.m.
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Q&A with Zico Kolter
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Live Q&A
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Fri 10:00 a.m. - 11:00 a.m.
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Climate Change and ML in the Private Sector
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Discussion Panel
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Aisha Walcott-Bryant · Lea Boche · Anima Anandkumar 🔗 |
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Fri 11:00 a.m. - 11:05 a.m.
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Introduction to Spotlights
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Live introduction
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Fri 11:05 a.m. - 11:15 a.m.
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Spotlight: Machine Learning for Glacier Monitoring in the Hindu Kush Himalaya
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Spotlight talk
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SlidesLive Video » Glacier mapping is key to ecological monitoring in the Hindu Kush Himalaya region. Climate change poses a risk to individuals whose livelihoods depend on the health of glacier ecosystems. In this work, we present a machine learning based approach to support ecological monitoring, with a focus on glaciers. Our approach is based on semi-automated mapping from satellite images. We utilize readily available remote sensing data to create a model to identify and outline both clean ice and debris-covered glaciers from satellite imagery. We also release data and develop a web tool that allows experts to visualize and correct model predictions, with the ultimate aim of accelerating the glacier mapping process. |
Kris Sankaran 🔗 |
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Fri 11:15 a.m. - 11:25 a.m.
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Spotlight: Wildfire Smoke and Air Quality: How Machine Learning Can Guide Forest Management
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Spotlight talk
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Prescribed burns are currently the most effective method of reducing the risk of widespread wildfires, but a largely missing component in forest management is knowing which fuels one can safely burn to minimize exposure to toxic smoke. Here we show how machine learning, such as spectral clustering and manifold learning, can provide interpretable representations and powerful tools for differentiating between smoke types, hence providing forest managers with vital information on effective strategies to reduce climate-induced wildfires while minimizing production of harmful smoke. |
Lorenzo Tomaselli 🔗 |
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Fri 11:25 a.m. - 11:36 a.m.
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Spotlight: OGNet: Towards a Global Oil and Gas Infrastructure Database using Deep Learning on Remotely Sensed Imagery
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Spotlight talk
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SlidesLive Video » At least a quarter of the warming that the Earth is experiencing today is due to anthropogenic methane emissions. There are multiple satellites in orbit and planned for launch in the next few years which can detect and quantify these emissions; however, to attribute methane emissions to their sources on the ground, a comprehensive database of the locations and characteristics of emission sources worldwide is essential. In this work, we develop deep learning algorithms that leverage freely available high-resolution aerial imagery to automatically detect oil and gas infrastructure, one of the largest contributors to global methane emissions. We use the best algorithm, which we call OGNet, together with expert review to identify the locations of oil refineries and petroleum terminals in the U.S. We show that OGNet detects many facilities which are not present in four standard public datasets of oil and gas infrastructure. All detected facilities are associated with characteristics critical to quantifying and attributing methane emissions, including the types of infrastructure and number of storage tanks. The data curated and produced in this study is freely available at https://link/provided/in/camera/ready/version. |
Hao Sheng 🔗 |
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Fri 11:36 a.m. - 11:45 a.m.
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Spotlight: Climate Change Driven Crop Yield Failures
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Spotlight talk
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SlidesLive Video » The effect of extreme temperatures, precipitation and variations in other meteorological factors affect crop yields, and hence climate change jeopardizes the entire food supply chain and dependent economic activities. We utilize Deep Neural Networks and Gaussian Processes for understanding crop yields as functions of climatological variables, and use change detection techniques to identify climatological thresholds where yield drops significantly. |
Somya Sharma 🔗 |
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Fri 11:45 a.m. - 11:55 a.m.
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Spotlight: Towards Tracking the Emissions of Every Power Plant on the Planet
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Spotlight talk
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Greenhouse gases emitted from fossil-fuel-burning power plants are a major contributor to climate change. Current methods to track emissions from individual sources are expensive and only used in a few countries. While carbon dioxide concentrations can be measured globally using remote sensing, direct methods do not provide sufficient spatial resolution to distinguish emissions from different sources. We use machine learning to infer power generation and emissions from visible and thermal power plant signatures in satellite images. By training on a data set of power plants for which we know the generation or emissions, we are able to apply our models globally. This paper demonstrates initial progress on this project by predicting whether a power plant is on or off from a single satellite image. |
Heather Couture 🔗 |
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Fri 12:00 p.m. - 1:00 p.m.
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Poster session 3
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Poster session
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Fri 1:00 p.m. - 1:10 p.m.
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Introduction to Jennifer Chayes
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Live introduction
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Fri 1:40 p.m. - 2:00 p.m.
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Q&A with Jennifer Chayes
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Live Q&A
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Fri 2:50 p.m. - 3:15 p.m.
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Closing remarks
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Fri 3:15 p.m. - 4:00 p.m.
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Poster reception
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Poster session
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Electric Vehicle Range Improvement by Utilizing Deep Learning to Optimize Occupant Thermal Comfort
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Poster
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SlidesLive Video » |
Alok Warey 🔗 |
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Explaining Complex Energy Systems: A Challenge
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Poster
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SlidesLive Video » |
Jonas Hülsmann 🔗 |
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Is Africa leapfrogging to renewables or heading for carbon lock-in? A machine-learning-based approach to predicting success of power-generation projects
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Poster
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SlidesLive Video » |
Galina Alova 🔗 |
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pymgrid: An Open-Source Python Microgrid Simulator for Applied Artificial Intelligence Research
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Poster
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SlidesLive Video » |
Gonzague Henri 🔗 |
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Optimal District Heating in China with Deep Reinforcement Learning
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Poster
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SlidesLive Video » |
Adrien Le Coz 🔗 |
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Deep Reinforcement Learning in Electricity Generation Investment for the Minimization of Long-Term Carbon Emissions and Electricity Costs
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Poster
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SlidesLive Video » |
Alexander Kell 🔗 |
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Short-Term Solar Irradiance Forecasting Using Calibrated Probabilistic Models
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Poster
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SlidesLive Video » |
Eric Zelikman 🔗 |
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The Human Effect Requires Affect: Addressing Social-Psychological Factors of Climate Change with Machine Learning
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Poster
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Kyle Tilbury 🔗 |
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A Temporally Consistent Image-based Sun Tracking Algorithm for Solar Energy Forecasting Applications
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Poster
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SlidesLive Video » |
Quentin Paletta 🔗 |
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Characterization of Industrial Smoke Plumes from Remote Sensing Data
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Poster
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SlidesLive Video » |
Michael Mommert 🔗 |
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A Way Toward Low-Carbon Shipping: Improving Port Operations Planning using Machine Learning
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Poster
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SlidesLive Video » |
Sara El Mekkaoui 🔗 |
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Learning the distribution of extreme precipitation from atmospheric general circulation model variables
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Poster
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SlidesLive Video » |
Philipp Hess 🔗 |
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Spatio-Temporal Learning for Feature Extraction inTime-Series Images
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Poster
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Gael Kamdem De Teyou 🔗 |
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Meta-modeling strategy for data-driven forecasting
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Poster
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SlidesLive Video » |
Dominic Skinner 🔗 |
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Privacy Preserving Demand Forecasting to Encourage Consumer Acceptance of Smart Energy Meters
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Poster
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Chris Briggs 🔗 |
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Short-term prediction of photovoltaic power generation using Gaussian process regression
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Poster
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SlidesLive Video » |
Yahya Al Lawati 🔗 |
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Leveraging Machine learning for Sustainable and Self-sufficient Energy Communities
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Poster
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SlidesLive Video » |
Anthony Faustine 🔗 |
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Formatting the Landscape: Spatial conditional GAN for varying population in satellite imagery
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Poster
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SlidesLive Video » |
Tomas Langer 🔗 |
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Storing Energy with Organic Molecules: Towards a Metric for Improving Molecular Performance for Redox Flow Batteries
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Poster
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SlidesLive Video » |
Luis Martin Mejia Mendoza 🔗 |
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Predicting Landsat Reflectance with Deep Generative Fusion
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Poster
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SlidesLive Video » |
Shahine Bouabid 🔗 |
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Quantitative Assessment of Drought Impacts Using XGBoost based on the Drought Impact Reporter
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Poster
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SlidesLive Video » |
Beichen Zhang 🔗 |
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Estimating Forest Ground Vegetation Cover From Nadir Photographs Using Deep Convolutional Neural Networks
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Poster
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SlidesLive Video » |
Martin Barczyk 🔗 |
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Hyperspectral Remote Sensing of Aquatic Microbes to Support Water Resource Management
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Poster
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SlidesLive Video » |
Grace Kim 🔗 |
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HECT: High-Dimensional Ensemble Consistency Testing for Climate Models
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Poster
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SlidesLive Video » |
Niccolo Dalmasso 🔗 |
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Towards DeepSentinel: An extensible corpus of labelled Sentinel-1 and -2 imagery and a proposed general purpose sensor-fusion semantic embedding model
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Poster
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SlidesLive Video » |
Lucas Kruitwagen 🔗 |
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Monitoring the Impact of Wildfires on Tree Species with Deep Learning
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Poster
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SlidesLive Video » |
WANG ZHOU 🔗 |
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ForestNet: Classifying Drivers of Deforestation in Indonesia using Deep Learning on Satellite Imagery
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Poster
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SlidesLive Video » |
Jeremy Irvin 🔗 |
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Monitoring Shorelines via High-Resolution Satellite Imagery and Deep Learning
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Poster
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Venkatesh Ramesh 🔗 |
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Mangrove Ecosystem Detection using Mixed-Resolution Imagery with a Hybrid-Convolutional Neural Network
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Poster
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SlidesLive Video » |
Dillon Hicks 🔗 |
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Context-Aware Urban Energy Efficiency Optimization Using Hybrid Physical Models
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Poster
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SlidesLive Video » |
Benjamin Choi 🔗 |
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Deep learning architectures for inference of AC-OPF solutions
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Poster
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SlidesLive Video » |
Thomas Falconer 🔗 |
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Predicting the Solar Potential of Rooftops using Image Segmentation and Structured Data
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Poster
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SlidesLive Video » |
Daniel de Barros Soares 🔗 |
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Revealing the Oil Majors' Adaptive Capacity to the Energy Transition with Deep Multi-Agent Reinforcement Learning
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Poster
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SlidesLive Video » |
Dylan Radovic 🔗 |
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Quantifying the presence of air pollutants over a road network in high spatio-temporal resolution
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Poster
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SlidesLive Video » |
Matteo Bohm 🔗 |
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Annual and in-season mapping of cropland at field scale with sparse labels
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Poster
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SlidesLive Video » |
Gabriel Tseng 🔗 |
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NightVision: Generating Nighttime Satellite Imagery from Infra-Red Observations
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Poster
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SlidesLive Video » |
Paula Harder 🔗 |
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Analyzing Sustainability Reports Using Natural Language Processing
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Poster
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SlidesLive Video » |
Alexandra Luccioni 🔗 |
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Automated Identification of Oil Field Features using CNNs
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Poster
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SONU DILEEP 🔗 |
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Using attention to model long-term dependencies in occupancy behavior
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Poster
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SlidesLive Video » |
Max Kleinebrahm 🔗 |
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Street to Cloud: Improving Flood Maps With Crowdsourcing and Semantic Segmentation
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Poster
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SlidesLive Video » |
Veda Sunkara 🔗 |
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Accurate river level predictions using a Wavenet-like model
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Poster
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SlidesLive Video » |
Shannon Doyle 🔗 |
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Graph Neural Networks for Improved El Niño Forecasting
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Poster
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SlidesLive Video » |
Salva Rühling Cachay 🔗 |
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Movement Tracks for the Automatic Detection of Fish Behavior in Videos
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Poster
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SlidesLive Video » |
Declan GD McIntosh 🔗 |
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Residue Density Segmentation for Monitoring and Optimizing Tillage Practices
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Poster
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SlidesLive Video » |
Jennifer Hobbs 🔗 |
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Counting Cows: Tracking Illegal Cattle Ranching From High-Resolution Satellite Imagery
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Poster
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SlidesLive Video » |
Issam Hadj Laradji 🔗 |
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Machine learning for advanced solar cell production: adversarial denoising, sub-pixel alignment and the digital twin
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Poster
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SlidesLive Video » |
Matthias Demant 🔗 |
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Physics-constrained Deep Recurrent Neural Models of Building Thermal Dynamics
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Poster
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SlidesLive Video » |
Jan Drgona 🔗 |
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Machine Learning Informed Policy for Environmental Justice in Atlanta with Climate Justice Implications
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Poster
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Lelia Hampton 🔗 |
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Narratives and Needs: Analyzing Experiences of Cyclone Amphan Using Twitter Discourse
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Poster
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Ancil Crayton 🔗 |
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FlowDB: A new large scale river flow, flash flood, and precipitation dataset
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Poster
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SlidesLive Video » |
Isaac Godfried 🔗 |
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Can Federated Learning Save The Planet ?
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Poster
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SlidesLive Video » |
Xinchi Qiu 🔗 |
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A Multi-source, End-to-End Solution for Tracking Climate Change Adaptation in Agriculture
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Poster
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Alejandro Coca-Castro 🔗 |
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Satellite imagery analysis for Land Use, Land Use Change and Forestry: A pilot study in Kigali, Rwanda
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Poster
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SlidesLive Video » |
Bright Aboh 🔗 |
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EarthNet2021: A novel large-scale dataset and challenge for forecasting localized climate impacts
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Poster
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SlidesLive Video » |
Christian Requena-Mesa 🔗 |
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DeepWaste: Applying Deep Learning to Waste Classification for a Sustainable Planet
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Poster
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SlidesLive Video » |
Yash Narayan 🔗 |
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Machine Learning Climate Model Dynamics: Offline versus Online Performance
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Poster
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SlidesLive Video » |
Noah Brenowitz 🔗 |
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Expert-in-the-loop Systems Towards Safety-critical Machine Learning Technology in Wildfire Intelligence
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Poster
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SlidesLive Video » |
Maria João Sousa 🔗 |
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VConstruct: Filling Gaps in Chl-a Data Using a Variational Autoencoder
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Poster
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SlidesLive Video » |
Matthew Ehrler 🔗 |
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A Comparison of Data-Driven Models for Predicting Stream Water Temperature
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Poster
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SlidesLive Video » |
Helen Weierbach 🔗 |
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Automated Salmonid Counting in Sonar Data
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Poster
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SlidesLive Video » |
Peter Kulits 🔗 |
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ACED: Accelerated Computational Electrochemical systems Discovery
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Poster
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SlidesLive Video » |
Rachel C Kurchin 🔗 |
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Forecasting Marginal Emissions Factors in PJM
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Poster
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SlidesLive Video » |
Amy Wang 🔗 |
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Short-term PV output prediction using convolutional neural network: learning from an imbalanced sky images dataset via sampling and data augmentation
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Poster
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SlidesLive Video » |
Yuhao Nie 🔗 |
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OfficeLearn: An OpenAI Gym Environment for Building Level Energy Demand Response
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Poster
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Lucas Spangher 🔗 |
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Investigating two super-resolution methods for downscaling precipitation: ESRGAN and CAR
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Poster
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Author Information
David Dao (ETH Zurich)
David Dao is a PhD student at ETH Zurich and the founder of GainForest, a non-profit working on decentralized technology to prevent deforestation. His research focuses on the deployment of novel machine learning systems for sustainable development and ecosystem monitoring. David served as a workshop co-organizer at ICLR, ICML and NeurIPS, and is a core member at Climate Change AI, a Global Shaper at World Economic Forum and a Climate Leader at Climate Reality. He is a research intern with Microsoft and was a former researcher at UC Berkeley and Stanford University.
Evan Sherwin (Stanford University)
I have devoted my professional career to evaluation of pathways toward a very low-carbon global energy system, developing expertise in energy modeling, statistics, machine learning, econometrics, and numerous engineering disciplines, economics, and policy domains as needed.
Priya Donti (Carnegie Mellon University)
Lauren Kuntz (Gaiascope)
Lynn Kaack (ETH Zurich)
Yumna Yusuf (City University London)
David Rolnick (McGill / Mila)
Catherine Nakalembe (University of Maryland)
Claire Monteleoni (University of Colorado Boulder)
Yoshua Bengio (Mila / U. Montreal)
Yoshua Bengio is Full Professor in the computer science and operations research department at U. Montreal, scientific director and founder of Mila and of IVADO, Turing Award 2018 recipient, Canada Research Chair in Statistical Learning Algorithms, as well as a Canada AI CIFAR Chair. He pioneered deep learning and has been getting the most citations per day in 2018 among all computer scientists, worldwide. He is an officer of the Order of Canada, member of the Royal Society of Canada, was awarded the Killam Prize, the Marie-Victorin Prize and the Radio-Canada Scientist of the year in 2017, and he is a member of the NeurIPS advisory board and co-founder of the ICLR conference, as well as program director of the CIFAR program on Learning in Machines and Brains. His goal is to contribute to uncover the principles giving rise to intelligence through learning, as well as favour the development of AI for the benefit of all.
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Yoshua Bengio -
2018 Workshop: AI for social good »
Margaux Luck · Tristan Sylvain · Joseph Paul Cohen · Arsene Fansi Tchango · Valentine Goddard · Aurelie Helouis · Yoshua Bengio · Sam Greydanus · Cody Wild · Taras Kucherenko · Arya Farahi · Jonathan Penn · Sean McGregor · Mark Crowley · Abhishek Gupta · Kenny Chen · Myriam Côté · Rediet Abebe -
2018 Poster: Image-to-image translation for cross-domain disentanglement »
Abel Gonzalez-Garcia · Joost van de Weijer · Yoshua Bengio -
2018 Poster: MetaGAN: An Adversarial Approach to Few-Shot Learning »
Ruixiang ZHANG · Tong Che · Zoubin Ghahramani · Yoshua Bengio · Yangqiu Song -
2018 Poster: Bayesian Model-Agnostic Meta-Learning »
Jaesik Yoon · Taesup Kim · Ousmane Dia · Sungwoong Kim · Yoshua Bengio · Sungjin Ahn -
2018 Poster: Sparse Attentive Backtracking: Temporal Credit Assignment Through Reminding »
Nan Rosemary Ke · Anirudh Goyal · Olexa Bilaniuk · Jonathan Binas · Michael Mozer · Chris Pal · Yoshua Bengio -
2018 Spotlight: Sparse Attentive Backtracking: Temporal Credit Assignment Through Reminding »
Nan Rosemary Ke · Anirudh Goyal · Olexa Bilaniuk · Jonathan Binas · Michael Mozer · Chris Pal · Yoshua Bengio -
2018 Spotlight: Bayesian Model-Agnostic Meta-Learning »
Jaesik Yoon · Taesup Kim · Ousmane Dia · Sungwoong Kim · Yoshua Bengio · Sungjin Ahn -
2018 Poster: Dendritic cortical microcircuits approximate the backpropagation algorithm »
João Sacramento · Rui Ponte Costa · Yoshua Bengio · Walter Senn -
2018 Oral: Dendritic cortical microcircuits approximate the backpropagation algorithm »
João Sacramento · Rui Ponte Costa · Yoshua Bengio · Walter Senn -
2017 : Yoshua Bengio »
Yoshua Bengio -
2017 : From deep learning of disentangled representations to higher-level cognition »
Yoshua Bengio -
2017 : More Steps towards Biologically Plausible Backprop »
Yoshua Bengio -
2017 : A3T: Adversarially Augmented Adversarial Training »
Aristide Baratin · Simon Lacoste-Julien · Yoshua Bengio · Akram Erraqabi -
2017 : Competition III: The Conversational Intelligence Challenge »
Mikhail Burtsev · Ryan Lowe · Iulian Vlad Serban · Yoshua Bengio · Alexander Rudnicky · Alan W Black · Shrimai Prabhumoye · Artem Rodichev · Nikita Smetanin · Denis Fedorenko · CheongAn Lee · EUNMI HONG · Hwaran Lee · Geonmin Kim · Nicolas Gontier · Atsushi Saito · Andrey Gershfeld · Artem Burachenok -
2017 Poster: Variational Walkback: Learning a Transition Operator as a Stochastic Recurrent Net »
Anirudh Goyal · Nan Rosemary Ke · Surya Ganguli · Yoshua Bengio -
2017 Demonstration: A Deep Reinforcement Learning Chatbot »
Iulian Vlad Serban · Chinnadhurai Sankar · Mathieu Germain · Saizheng Zhang · Zhouhan Lin · Sandeep Subramanian · Taesup Kim · Michael Pieper · Sarath Chandar · Nan Rosemary Ke · Sai Rajeswar Mudumba · Alexandre de Brébisson · Jose Sotelo · Dendi A Suhubdy · Vincent Michalski · Joelle Pineau · Yoshua Bengio -
2017 Poster: GibbsNet: Iterative Adversarial Inference for Deep Graphical Models »
Alex Lamb · R Devon Hjelm · Yaroslav Ganin · Joseph Paul Cohen · Aaron Courville · Yoshua Bengio -
2017 Poster: Plan, Attend, Generate: Planning for Sequence-to-Sequence Models »
Caglar Gulcehre · Francis Dutil · Adam Trischler · Yoshua Bengio -
2017 Poster: Task-based End-to-end Model Learning in Stochastic Optimization »
Priya Donti · J. Zico Kolter · Brandon Amos -
2017 Poster: Z-Forcing: Training Stochastic Recurrent Networks »
Anirudh Goyal · Alessandro Sordoni · Marc-Alexandre Côté · Nan Rosemary Ke · Yoshua Bengio -
2016 : Yoshua Bengio – Credit assignment: beyond backpropagation »
Yoshua Bengio -
2016 : From Brains to Bits and Back Again »
Yoshua Bengio · Terrence Sejnowski · Christos H Papadimitriou · Jakob H Macke · Demis Hassabis · Alyson Fletcher · Andreas Tolias · Jascha Sohl-Dickstein · Konrad P Koerding -
2016 : Yoshua Bengio : Toward Biologically Plausible Deep Learning »
Yoshua Bengio -
2016 : Panel on "Explainable AI" (Yoshua Bengio, Alessio Lomuscio, Gary Marcus, Stephen Muggleton, Michael Witbrock) »
Yoshua Bengio · Alessio Lomuscio · Gary Marcus · Stephen H Muggleton · Michael Witbrock -
2016 : Yoshua Bengio: From Training Low Precision Neural Nets to Training Analog Continuous-Time Machines »
Yoshua Bengio -
2016 Symposium: Deep Learning Symposium »
Yoshua Bengio · Yann LeCun · Navdeep Jaitly · Roger Grosse -
2016 Poster: Architectural Complexity Measures of Recurrent Neural Networks »
Saizheng Zhang · Yuhuai Wu · Tong Che · Zhouhan Lin · Roland Memisevic · Russ Salakhutdinov · Yoshua Bengio -
2016 Poster: Professor Forcing: A New Algorithm for Training Recurrent Networks »
Alex M Lamb · Anirudh Goyal · Ying Zhang · Saizheng Zhang · Aaron Courville · Yoshua Bengio -
2016 Poster: On Multiplicative Integration with Recurrent Neural Networks »
Yuhuai Wu · Saizheng Zhang · Ying Zhang · Yoshua Bengio · Russ Salakhutdinov -
2016 Poster: Binarized Neural Networks »
Itay Hubara · Matthieu Courbariaux · Daniel Soudry · Ran El-Yaniv · Yoshua Bengio -
2015 : RL for DL »
Yoshua Bengio -
2015 : Learning Representations for Unsupervised and Transfer Learning »
Yoshua Bengio -
2015 Symposium: Deep Learning Symposium »
Yoshua Bengio · Marc'Aurelio Ranzato · Honglak Lee · Max Welling · Andrew Y Ng -
2015 Poster: Attention-Based Models for Speech Recognition »
Jan K Chorowski · Dzmitry Bahdanau · Dmitriy Serdyuk · Kyunghyun Cho · Yoshua Bengio -
2015 Poster: Equilibrated adaptive learning rates for non-convex optimization »
Yann Dauphin · Harm de Vries · Yoshua Bengio -
2015 Spotlight: Equilibrated adaptive learning rates for non-convex optimization »
Yann Dauphin · Harm de Vries · Yoshua Bengio -
2015 Spotlight: Attention-Based Models for Speech Recognition »
Jan K Chorowski · Dzmitry Bahdanau · Dmitriy Serdyuk · Kyunghyun Cho · Yoshua Bengio -
2015 Poster: A Recurrent Latent Variable Model for Sequential Data »
Junyoung Chung · Kyle Kastner · Laurent Dinh · Kratarth Goel · Aaron Courville · Yoshua Bengio -
2015 Poster: BinaryConnect: Training Deep Neural Networks with binary weights during propagations »
Matthieu Courbariaux · Yoshua Bengio · Jean-Pierre David -
2015 Tutorial: Deep Learning »
Geoffrey E Hinton · Yoshua Bengio · Yann LeCun -
2014 Workshop: Second Workshop on Transfer and Multi-Task Learning: Theory meets Practice »
Urun Dogan · Tatiana Tommasi · Yoshua Bengio · Francesco Orabona · Marius Kloft · Andres Munoz · Gunnar Rätsch · Hal Daumé III · Mehryar Mohri · Xuezhi Wang · Daniel Hernández-lobato · Song Liu · Thomas Unterthiner · Pascal Germain · Vinay P Namboodiri · Michael Goetz · Christopher Berlind · Sigurd Spieckermann · Marta Soare · Yujia Li · Vitaly Kuznetsov · Wenzhao Lian · Daniele Calandriello · Emilie Morvant -
2014 Workshop: Deep Learning and Representation Learning »
Andrew Y Ng · Yoshua Bengio · Adam Coates · Roland Memisevic · Sharanyan Chetlur · Geoffrey E Hinton · Shamim Nemati · Bryan Catanzaro · Surya Ganguli · Herbert Jaeger · Phil Blunsom · Leon Bottou · Volodymyr Mnih · Chen-Yu Lee · Rich M Schwartz -
2014 Workshop: OPT2014: Optimization for Machine Learning »
Zaid Harchaoui · Suvrit Sra · Alekh Agarwal · Martin Jaggi · Miro Dudik · Aaditya Ramdas · Jean Lasserre · Yoshua Bengio · Amir Beck -
2014 Poster: How transferable are features in deep neural networks? »
Jason Yosinski · Jeff Clune · Yoshua Bengio · Hod Lipson -
2014 Poster: Identifying and attacking the saddle point problem in high-dimensional non-convex optimization »
Yann N Dauphin · Razvan Pascanu · Caglar Gulcehre · Kyunghyun Cho · Surya Ganguli · Yoshua Bengio -
2014 Poster: Generative Adversarial Nets »
Ian Goodfellow · Jean Pouget-Abadie · Mehdi Mirza · Bing Xu · David Warde-Farley · Sherjil Ozair · Aaron Courville · Yoshua Bengio -
2014 Poster: On the Number of Linear Regions of Deep Neural Networks »
Guido F Montufar · Razvan Pascanu · Kyunghyun Cho · Yoshua Bengio -
2014 Demonstration: Neural Machine Translation »
Bart van Merriënboer · Kyunghyun Cho · Dzmitry Bahdanau · Yoshua Bengio -
2014 Oral: How transferable are features in deep neural networks? »
Jason Yosinski · Jeff Clune · Yoshua Bengio · Hod Lipson -
2014 Poster: Iterative Neural Autoregressive Distribution Estimator NADE-k »
Tapani Raiko · Yao Li · Kyunghyun Cho · Yoshua Bengio -
2014 Tutorial: Climate Change: Challenges for Machine Learning »
Arindam Banerjee · Claire Monteleoni -
2013 Workshop: Deep Learning »
Yoshua Bengio · Hugo Larochelle · Russ Salakhutdinov · Tomas Mikolov · Matthew D Zeiler · David Mcallester · Nando de Freitas · Josh Tenenbaum · Jian Zhou · Volodymyr Mnih -
2013 Workshop: Output Representation Learning »
Yuhong Guo · Dale Schuurmans · Richard Zemel · Samy Bengio · Yoshua Bengio · Li Deng · Dan Roth · Kilian Q Weinberger · Jason Weston · Kihyuk Sohn · Florent Perronnin · Gabriel Synnaeve · Pablo R Strasser · julien audiffren · Carlo Ciliberto · Dan Goldwasser -
2013 Poster: Multi-Prediction Deep Boltzmann Machines »
Ian Goodfellow · Mehdi Mirza · Aaron Courville · Yoshua Bengio -
2013 Poster: Generalized Denoising Auto-Encoders as Generative Models »
Yoshua Bengio · Li Yao · Guillaume Alain · Pascal Vincent -
2013 Poster: Stochastic Ratio Matching of RBMs for Sparse High-Dimensional Inputs »
Yann Dauphin · Yoshua Bengio -
2012 Workshop: Deep Learning and Unsupervised Feature Learning »
Yoshua Bengio · James Bergstra · Quoc V. Le -
2011 Workshop: Big Learning: Algorithms, Systems, and Tools for Learning at Scale »
Joseph E Gonzalez · Sameer Singh · Graham Taylor · James Bergstra · Alice Zheng · Misha Bilenko · Yucheng Low · Yoshua Bengio · Michael Franklin · Carlos Guestrin · Andrew McCallum · Alexander Smola · Michael Jordan · Sugato Basu -
2011 Workshop: Deep Learning and Unsupervised Feature Learning »
Yoshua Bengio · Adam Coates · Yann LeCun · Nicolas Le Roux · Andrew Y Ng -
2011 Oral: The Manifold Tangent Classifier »
Salah Rifai · Yann N Dauphin · Pascal Vincent · Yoshua Bengio · Xavier Muller -
2011 Poster: Shallow vs. Deep Sum-Product Networks »
Olivier Delalleau · Yoshua Bengio -
2011 Poster: The Manifold Tangent Classifier »
Salah Rifai · Yann N Dauphin · Pascal Vincent · Yoshua Bengio · Xavier Muller -
2011 Poster: Algorithms for Hyper-Parameter Optimization »
James Bergstra · Rémi Bardenet · Yoshua Bengio · Balázs Kégl -
2011 Poster: On Tracking The Partition Function »
Guillaume Desjardins · Aaron Courville · Yoshua Bengio -
2010 Workshop: Deep Learning and Unsupervised Feature Learning »
Honglak Lee · Marc'Aurelio Ranzato · Yoshua Bengio · Geoffrey E Hinton · Yann LeCun · Andrew Y Ng -
2009 Poster: Slow, Decorrelated Features for Pretraining Complex Cell-like Networks »
James Bergstra · Yoshua Bengio -
2009 Poster: An Infinite Factor Model Hierarchy Via a Noisy-Or Mechanism »
Aaron Courville · Douglas Eck · Yoshua Bengio -
2009 Session: Debate on Future Publication Models for the NIPS Community »
Yoshua Bengio -
2009 Poster: Streaming k-means approximation »
Nir Ailon · Ragesh Jaiswal · Claire Monteleoni -
2008 Poster: Privacy-preserving logistic regression »
Kamalika Chaudhuri · Claire Monteleoni -
2007 Poster: Augmented Functional Time Series Representation and Forecasting with Gaussian Processes »
Nicolas Chapados · Yoshua Bengio -
2007 Poster: Learning the 2-D Topology of Images »
Nicolas Le Roux · Yoshua Bengio · Pascal Lamblin · Marc Joliveau · Balázs Kégl -
2007 Spotlight: Augmented Functional Time Series Representation and Forecasting with Gaussian Processes »
Nicolas Chapados · Yoshua Bengio -
2007 Spotlight: A general agnostic active learning algorithm »
Sanjoy Dasgupta · Daniel Hsu · Claire Monteleoni -
2007 Poster: A general agnostic active learning algorithm »
Sanjoy Dasgupta · Daniel Hsu · Claire Monteleoni -
2007 Poster: Topmoumoute Online Natural Gradient Algorithm »
Nicolas Le Roux · Pierre-Antoine Manzagol · Yoshua Bengio -
2006 Poster: Greedy Layer-Wise Training of Deep Networks »
Yoshua Bengio · Pascal Lamblin · Dan Popovici · Hugo Larochelle -
2006 Talk: Greedy Layer-Wise Training of Deep Networks »
Yoshua Bengio · Pascal Lamblin · Dan Popovici · Hugo Larochelle