Dataset and Benchmark Track 3
The Datasets and Benchmarks track serves as a novel venue for high-quality publications, talks, and posters on highly valuable machine learning datasets and benchmarks, as well as a forum for discussions on how to improve dataset development. Datasets and benchmarks are crucial for the development of machine learning methods, but also require their own publishing and reviewing guidelines. For instance, datasets can often not be reviewed in a double-blind fashion, and hence full anonymization will not be required. On the other hand, they do require additional specific checks, such as a proper description of how the data was collected, whether they show intrinsic bias, and whether they will remain accessible.
Competition Track Day 4: Overviews + Breakout Sessions
The program includes a wide variety of exciting competitions in different domains, with some focusing more on applications and others trying to unify fields, focusing on technical challenges or directly tackling important problems in the world. The aim is for the broad program to make it so that anyone who wants to work on or learn from a competition can find something to their liking.
In this session, we have the following competitions:
* The Image Similarity Challenge
* Enhanced Zero-Resource Speech Challenge 2021: Language Modelling from Speech and Images
* The BEETL Competition: Benchmarks for EEG Transfer Learning
* Multimodal Single-Cell Data Integration
* The AI Driving Olympics
WiML Workshop 3
WiML’s purpose is to enhance the experience of women in machine learning. Our flagship event is the annual Women in Machine Learning (WiML) Workshop, typically co-located with NeurIPS. We also organize an “un-workshop” at ICML, as well as small events at other machine learning conferences such as AISTATS, ICLR, etc.
Our mission is to enhance the experience of women in machine learning, and thereby
Increase the number of women in machine learning
Help women in machine learning succeed professionally
Increase the impact of women in machine learning in the community
Toward this goal, we create opportunities for women to engage in substantive technical and professional conversations in a positive, supportive environment (e.g. annual workshop, small events, mentoring program). We also work to increase awareness and appreciation of the achievements of women in machine learning (e.g. directory and profiles of women in machine learning). Our programs help women build their technical confidence and their voice, and our publicity efforts help ensure that women in machine learning and their achievements are known in the community.
Black in AI Workshop
Black in AI exists to create a space for sharing ideas, foster collaborations, and discuss initiatives to increase the presence of Black individuals in the field of AI. To this end, we hold an annual technical workshop series, run mentoring programs, and maintain various fora for fostering partnerships and collaborations with and among black AI researchers. The 5th Black in AI workshop and 2nd virtual Black in AI workshop will consist of selected oral presentations, invited keynote speakers, a joint poster session with other affinity groups, sponsorship sessions, and startups showcases. Our workshop exists to amplify the voices of black researchers at NeurIPS.
In nature, groups of thousands of individuals cooperate to create complex structure purely through local interactions – from cells that form complex organisms, to social insects like termites that build meter-high mounds and army ants that self-assemble entire nests, to the complex and mesmerizing motion of fish schools and bird flocks. What makes these systems so fascinating to scientists and engineers alike, is that even though each individual has limited ability, as a collective they achieve tremendous complexity.
What would it take to create our own artificial collectives of the scale and complexity that nature achieves? My lab investigates this question by using inspiration from biological collectives to create robotic systems, e.g. the Kilobot thousand robot swarm inspired by cells, and the Termes robots inspired by mound-building termites. In this talk, I will discuss a recent project in my group – Eciton robotica - to create a self-assembling swarm of soft climbing robots inspired by the living architectures of army ants. Our work spans soft robotics, new theoretical models of self-organized self-assembly, and new field experiments in biology. Most critically, our work derives from the collective intelligence of engineers and scientists working together.
Demonstrations 4
Demonstrations must show novel technology and must run online during the conference. Unlike poster presentations or slide shows, interaction with the audience is a critical element. Therefore, the creativity of demonstrators to propose new ways in which interaction and engagement can fully leverage this year’s virtual conference format will be particularly relevant for selection. This session has the following demonstrations:
- Protopia AI: Taking on the Missing Link in AI Privacy and Data Protection
- MEWS: Real-time Social Media Manipulation Detection and Analysis
- An Interactive Visual Demo of Bias Mitigation Techniques for Word Representations
- TripleBlind: A Privacy Preserving Framework for Decentralized Data and Algorithms
Dataset and Benchmark Poster Session 4
The Datasets and Benchmarks track serves as a novel venue for high-quality publications, talks, and posters on highly valuable machine learning datasets and benchmarks, as well as a forum for discussions on how to improve dataset development. Datasets and benchmarks are crucial for the development of machine learning methods, but also require their own publishing and reviewing guidelines. For instance, datasets can often not be reviewed in a double-blind fashion, and hence full anonymization will not be required. On the other hand, they do require additional specific checks, such as a proper description of how the data was collected, whether they show intrinsic bias, and whether they will remain accessible.
Un-bookclub Algorithms of Oppression
Let’s come together for an Un-Bookclub book Algorithms of Oppression social at NeurIPS. We’ve been learning a lot from the discussions in the cross-continental book club that we’ve been running out of this book by the celebrated UCLA Professor Dr. Safyia Umoja Noble. We’d love to give you the gift of connection, conversation, and reflection Dr. Noble gave us. We’ll briefly introduce the author’s ideas and do hands-on exercises to seed discussions about the human impact of AI and search engines in our lives. There is no prework for this social and you are not expected to have read the book to participate in the exercises and discussions.
WiML Workshop 4
WiML’s purpose is to enhance the experience of women in machine learning. Our flagship event is the annual Women in Machine Learning (WiML) Workshop, typically co-located with NeurIPS. We also organize an “un-workshop” at ICML, as well as small events at other machine learning conferences such as AISTATS, ICLR, etc.
Our mission is to enhance the experience of women in machine learning, and thereby
Increase the number of women in machine learning
Help women in machine learning succeed professionally
Increase the impact of women in machine learning in the community
Toward this goal, we create opportunities for women to engage in substantive technical and professional conversations in a positive, supportive environment (e.g. annual workshop, small events, mentoring program). We also work to increase awareness and appreciation of the achievements of women in machine learning (e.g. directory and profiles of women in machine learning). Our programs help women build their technical confidence and their voice, and our publicity efforts help ensure that women in machine learning and their achievements are known in the community.
Roundtable Chatroom
Roundtable Chatroom is a community that fosters communication and sharing of great ideas dedicated to AI and ML practitioners. Our main event is inspired by the roundtable chats back in the physical conference era, where participants discuss a topic for some time before moving on to the next one.
Constrained by the pandemic, we will be using virtual breakout rooms (e.g Zoom) to simulate the ‘roundtables’. To ensure the quality and experience of our discussions, we have invited ‘mentors’ who are passionate about the topic with profound knowledge in their field to lead each breakout room.
The topics we will discuss at NeurIPS 2021 include: The next big thing in AI AI in finance How to develop a research idea? Do we publish too much?
Check out our previous event at ICLR 2021 https://www.roundtable-chatroom.com/ A list of mentors will be finalized soon on the homepage.
How Should a Machine Learning Researcher Think About AI Ethics?
As machine learning becomes increasingly widespread in the real world, there has been a growing set of well-documented potential harms that need to be acknowledged and addressed. In particular, valid concerns about data privacy, algorithmic bias, automation risk, potential malicious uses, and more have highlighted the need for the active consideration of critical ethical issues in the field. In the light of this, there have been calls for machine learning researchers to actively consider not only the potential benefits of their research but also its potential negative societal impact, and adopt measures that enable positive trajectories to unfold while mitigating risk of harm. However, grappling with ethics is still a difficult and unfamiliar problem for many in the field. A common difficulty with assessing ethical impact is its indirectness: most papers focus on general-purpose methodologies (e.g., optimization algorithms), whereas ethical concerns are more apparent when considering downstream applications (e.g., surveillance systems). Also, real-world impact (both positive and negative) often emerges from the cumulative progress of many papers, so it is difficult to attribute the impact to an individual paper. Furthermore, regular research ethics mechanisms such as an Institutional Review Board (IRB) are not always a good fit for machine learning and problematic research practices involving extensive environmental and labor costs or inappropriate data use are so ingrained in community norms that it can be difficult to articulate where to draw the line as expectations evolve. How should machine learning researchers wrestle with these topics in their own research? In this panel, we invite the NeurIPS community to contribute questions stemming from their own research and other experiences, so that we can develop community norms around AI ethics and provide concrete guidance to individual researchers.
Online Learning for Latent Dirichlet Allocation
This paper introduces a stochastic variational gradient based inference procedure for training Latent Dirichlet Allocation (LDA) models on very large text corpora. On the theoretical side it is shown that the training procedure converges to a local optimum and that, surprisingly, the simple stochastic gradient updates correspond to a stochastic natural gradient of the evidence lower bound (ELBO) objective. On the empirical side the authors show that for the first time LDA can be comfortably trained on text corpora of several hundreds of thousands of documents, making it a practical technique for “big data” problems. The idea has made a large impact in the ML community because it represented the first stepping stone for general stochastic gradient variational inference procedures for a much broader class of models. After this paper, there would be no good reason to ever use full batch training procedures for variational inference anymore.