Workshop
AI for Science: from Theory to Practice
Yuanqi Du · Max Welling · Yoshua Bengio · Marinka Zitnik · Carla Gomes · Jure Leskovec · Maria Brbic · Wenhao Gao · Kexin Huang · Ziming Liu · Rocío Mercado · Miles Cranmer · Shengchao Liu · Lijing Wang
Hall C2 (level 1 gate 9 south of food court)
Sat 16 Dec, 6:15 a.m. PST
AI is being increasingly integrated into scientific discovery to augment and accelerate research, helping scientists to generate hypotheses, design experiments, collect and interpret large datasets, and gain new insights that might not have been possible using traditional scientific methods alone. It has solved scientific challenges that were unimaginable before, e.g., predicting 3D protein structures, simulating molecular systems, forecasting global climate, and discovering new scientific laws. Despite this promise, several critical gaps stifle algorithmic and scientific innovation in "AI for Science," and the overarching goal of this workshop is to grow AI for Science by closing these gaps: * Gap 1: Science of science. The principles of scientific methods have remained unchanged since the 17th century. How AI can facilitate the practice of scientific discovery itself often remains undiscussed. For example, instead of the numerous hypothesis-experiment cycles to make sense of a scientific phenomenon, can AI reason and output natural laws directly?* Gap 2: Limited exploration at the intersections of multiple disciplines. Solutions to grand challenges stretch across various disciplines. For example, protein structure prediction requires collaboration across physics, chemistry, and biology, and single-cell imaging of whole tumors can be approached by cosmology algorithms that connect cells as stars.* Gap 3: Unified ecosystems of datasets, models, and scientific hypotheses. Comprehensive ecosystems and engagements of the research community, e.g., accumulation of datasets, open-source platforms, and benchmarks, are needed to reliably evaluate AI tools and integrate them into scientific workflows and instruments so that they can contribute to scientific understanding or acquire it autonomously. The workshop will emphasize this indispensable ingredient to the success of AI for Science and engage in discussions around it.* Gap 4: Responsible use and development of AI for science. Interest in AI across scientific disciplines has grown, but very few AI models have progressed to routine use in practice. We plan to present a roadmap and guidelines for accelerating the translation of AI in science. To be successful, translation will require a team of engaged stakeholders and a systematic process from beginning (problem formulation) to end (widespread deployment).* Gap 5: Lack of educational resources. A critical element to increase the adoption of AI for scientific discovery across disciplines is to create accessible education materials and AI-lab protocols for both AI researchers and scientists with different areas of expertise, seniority, and level of interest.* Gap 6: Unrealistic methodological assumptions or directions. While AI researchers strive for methodological advances, they can make unrealistic assumptions that can limit the applicability of new algorithms, their adoption in real-world settings, and transition into implementation (e.g., at a particle accelerator, genome sequencing lab, or quantum chemistry lab). For example, while state-of-the-art molecule generation AI models perform well on benchmarks, they often generate molecules that can't be synthesized in a lab.
Schedule
Sat 6:15 a.m. - 6:25 a.m.
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Opening Remark
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Opening Remark
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Sat 6:25 a.m. - 6:55 a.m.
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Invited Talk (Stven Brunton)
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Sat 6:55 a.m. - 7:25 a.m.
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Invited Talk (Kyle Cranmer)
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Sat 7:25 a.m. - 7:55 a.m.
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Invited Talk (Fabian Theis)
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Sat 7:55 a.m. - 8:05 a.m.
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Coffee Break
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Coffee Break
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Sat 8:05 a.m. - 8:25 a.m.
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Open Catalyst Project Introduction
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Sat 8:25 a.m. - 8:35 a.m.
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Open Catalyst Winner Talk
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Sat 8:35 a.m. - 8:45 a.m.
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Open Catalyst Runner-up Talk
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Sat 8:45 a.m. - 8:50 a.m.
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Open Catalyst Spotlight Talk
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Sat 8:50 a.m. - 9:10 a.m.
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Open Catalyst Discussion
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Q&A
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Sat 9:10 a.m. - 10:00 a.m.
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Poster Session A
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Poster Session
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Sat 10:00 a.m. - 10:30 a.m.
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Lunch Break
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Sat 10:30 a.m. - 11:00 a.m.
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Invited Talk (Sara Beery)
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Sat 11:00 a.m. - 12:00 p.m.
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Panel: Using AI to Accelerate Scientific Discovery
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Panel Discussion
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Sat 12:00 p.m. - 12:25 p.m.
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Contributed Talk
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Sat 12:25 p.m. - 12:35 p.m.
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Coffee Break
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Sat 12:35 p.m. - 1:05 p.m.
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Invited Talk (Sherrie Wang)
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Sat 1:05 p.m. - 1:35 p.m.
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Invited Talk (Su-In Lee)
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Sat 1:35 p.m. - 2:05 p.m.
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Invited Talk (Alán Aspuru-Guzik)
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Sat 2:05 p.m. - 2:30 p.m.
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Contributed Talk
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Sat 2:30 p.m. - 2:35 p.m.
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Closing Remark
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Sat 2:35 p.m. - 3:30 p.m.
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Poster Session B
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Poster Session
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Gradient Estimation For Exactly-$k$ Constraints ( Poster ) > link | Ruoyan Li · Dipti Ranjan Sahu · Guy Van den Broeck · Zhe Zeng 🔗 |
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Learning Expert-Interpretable Programs for Myocardial Infarction Localization ( Poster ) > link | Joshua Flashner · Jennifer J Sun · David Ouyang · Yisong Yue 🔗 |
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Predicting the Initial Conditions of the Universe using a Deterministic Neural Network ( Poster ) > link | Vaibhav Jindal · Albert Liang · Aarti Singh · Shirley Ho · Drew Jamieson 🔗 |
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Holistic chemical evaluation reveals pitfalls in reaction prediction models ( Oral ) > link | Victor Sabanza Gil · Andres M Bran · Malte Franke · Jeremy Luterbacher · Philippe Schwaller 🔗 |
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Towards LLMs as Operational Copilots for Fusion Reactors ( Poster ) > link | Viraj Mehta · Joseph Abbate · Allen Wang · Andrew Rothstein · Ian Char · Jeff Schneider · Egemen Kolemen · Cristina Rea · Darren Garnier 🔗 |
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On Modelability and Generalizability: Are Machine Learning Models for Drug Synergy Exploiting Artefacts and Biases in Available Data? ( Poster ) > link | Arushi Gandhi · Andreas Bender · Ian Stott 🔗 |
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LenSiam: Self-Supervised Learning on Strong Gravitational Lens Images ( Poster ) > link | Po-Wen Chang · Kuan-Wei Huang · Joshua Fagin · James Chan · Joshua Yao-Yu Lin 🔗 |
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Surrogate Modeling for Computationally Expensive Simulations of Supernovae in High-Resolution Galaxy Simulations ( Poster ) > link | Keiya Hirashima · Kana Moriwaki · Michiko Fujii · Yutaka Hirai · Takayuki Saitoh · Junichiro Makino · Shirley Ho 🔗 |
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Seismic hazard analysis with a Factorized Fourier Neural Operator (F-FNO) surrogate model enhanced by transfer learning ( Poster ) > link | Fanny Lehmann · Filippo Gatti · Michaël Bertin · Didier Clouteau 🔗 |
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BENO: Boundary-embedded Neural Operators for Elliptic PDEs ( Oral ) > link | Haixin Wang · Jiaxin LI · Anubhav Dwivedi · Kentaro Hara · Tailin Wu 🔗 |
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Machine Learning for Practical Quantum Error Mitigation ( Poster ) > link | Haoran Liao · Derek Wang · Iskandar Sitdikov · Ciro Salcedo · Alireza Seif · Zlatko Minev 🔗 |
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Distilling human decision-making dynamics: a comparative analysis of low-dimensional architectures ( Poster ) > link | Huadong Xiong · Li Ji-An · Marcelo G Mattar · Robert Wilson 🔗 |
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Genomic language model predicts protein co-regulation and function ( Poster ) > link | Yunha Hwang · Andre Cornman · Elizabeth Kellogg · Sergey Ovchinnikov · Peter Girguis 🔗 |
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Learning to Scale Logits for Temperature-Conditional GFlowNets ( Poster ) > link | Minsu Kim · Joohwan Ko · Dinghuai Zhang · Ling Pan · Yun TaeYoung · Woochang Kim · Jinkyoo Park · Yoshua Bengio 🔗 |
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Auto-PINN: Understanding and Optimizing Physics-Informed Neural Architecture ( Poster ) > link | Yicheng Wang · Xiaotian Han · Chia-Yuan Chang · Daochen Zha · Ulisses M. Braga-Neto · Xia Hu 🔗 |
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Augmenting large language models with chemistry tools ( Poster ) > link | Andres M Bran · Sam Cox · Oliver Schilter · Carlo Baldassari · Andrew White · Philippe Schwaller 🔗 |
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Scalable Particle Generation for Granular Shape Study ( Poster ) > link | Yifeng Zhao · Jinxin Liu · Xiangbo Gao · Pei Zhang · Sergio Andres Galindo Torres · Stan Z. Li 🔗 |
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AdsGT: Graph Transformer for Predicting Global Minimum Adsorption Energy ( Poster ) > link | Junwu Chen · Xu Huang · Cheng Hua · Yulian He · Philippe Schwaller 🔗 |
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Generation of 3D Realistic Soil Particles with Metaball Descriptor ( Poster ) > link | Yifeng Zhao · Jinxin Liu · Xiangbo Gao · Pei Zhang · Stan Z. Li · Sergio Andres Galindo Torres 🔗 |
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AI for Open Science: A Multi-Agent Perspective for Ethically Translating Data to Knowledge ( Poster ) > link | Chase Yakaboski · Gregory Hyde · Clement Nyanhongo · Eugene Santos 🔗 |
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Testing Assumptions Underlying a Unified Theory for the Origin of Grid Cells ( Poster ) > link | Rylan Schaeffer · Mikail Khona · Adrian Bertagnoli · Sanmi Koyejo · Ila Fiete 🔗 |
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arXiVeri: Automatic table verification with GPT ( Poster ) > link | Gyungin Shin · Gyungin Shin · Weidi Xie · Samuel Albanie 🔗 |
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Exploring the Properties and Structure of Real Knowledge Graphs across Scientific Disciplines ( Poster ) > link | Nedelina Teneva · Estevam Hruschka 🔗 |
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Lineax: unified linear solves and linear least-squares in JAX and Equinox ( Poster ) > link | Jason Rader · Terry Lyons 🔗 |
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SE(3) Equivariant Augmented Coupling Flows ( Poster ) > link | Laurence Midgley · Vincent Stimper · Vincent Stimper · Javier Antorán · Emile Mathieu · Emile Mathieu · Bernhard Schölkopf · Bernhard Schölkopf · José Miguel Hernández-Lobato 🔗 |
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Randomized Benchmarking of Local Zeroth-Order Optimizers for Variational Quantum Systems ( Poster ) > link | Lucas Tecot · Cho-Jui Hsieh 🔗 |
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Using the Transformer Model for Physical Simulation: An application on Transient Thermal Analysis for 3D Printing Process Simulation ( Poster ) > link | Qian Chen · Luyang Kong · Florian Dugast · Albert To 🔗 |
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PGraphDTA: Improving Drug Target Interaction Prediction using Protein Language Models and Contact Maps ( Poster ) > link | Rakesh Bal · Yijia Xiao · Wei Wang 🔗 |
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Discovery of Novel Reticular Materials for Carbon Dioxide Capture using GFlowNets ( Poster ) > link | Flaviu Cipcigan · Flaviu Cipcigan · Jonathan Booth · Jonathan Booth · Rodrigo Neumann Barros Ferreira · Carine Dos Santos · Carine Dos Santos · Mathias Steiner · Mathias Steiner 🔗 |
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Extracting Nonlinear Symmetries From Trained Neural Networks on Dynamics Data ( Poster ) > link | Yoh-ichi Mototake 🔗 |
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Modelling single-cell RNA-seq trajectories on a flat statistical manifold ( Oral ) > link | Alessandro Palma · Alessandro Palma · Sergei Rybakov · Leon Hetzel · Fabian Theis · Fabian Theis 🔗 |
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GeoMFormer: A General Architecture for Geometric Molecular Representation Learning ( Poster ) > link | Tianlang Chen · Shengjie Luo · Di He · Shuxin Zheng · Tie-Yan Liu · Liwei Wang 🔗 |
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Scalable Deep Potentials as Implicit Hierarchical Semi-Separable Operators ( Poster ) > link | Michael Poli · Stefano Massaroli · Christopher Ré · Stefano Ermon 🔗 |
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Predictive Uncertainty Quantification for Graph Neural Network Driven Relaxed Energy Calculations ( Poster ) > link | Joseph Musielewicz · Janice Lan · Matt Uyttendaele 🔗 |
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GFN-SR: Symbolic Regression with Generative Flow Networks ( Poster ) > link | Sida Li · Ioana Marinescu · Sebastian Musslick 🔗 |
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Machine Learning Force Fields with Data Cost Aware Training ( Poster ) > link | Alexander Bukharin · Tianyi Liu · Shengjie Wang · Simiao Zuo · Weihao Gao · Wen Yan · Tuo Zhao 🔗 |
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Learning Interatomic Potentials at Multiple Scales ( Poster ) > link | Xiang Fu · Albert Musaelian · Anders Johansson · Tommi Jaakkola · Boris Kozinsky 🔗 |
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DynamicsDiffusion: Generating and Rare Event Sampling of Molecular Dynamic Trajectories Using Diffusion Models ( Poster ) > link | Magnus Petersen · Gemma Roig · Gemma Roig · Roberto Covino · Roberto Covino 🔗 |
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FSscore: A Machine Learning-based Synthetic Feasibility Score Leveraging Human Expertise ( Poster ) > link | Rebecca Neeser · Bruno Correia · Philippe Schwaller 🔗 |
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Bi-level Graphs for Cellular Pattern Discovery ( Poster ) > link | Zhenzhen Wang · Aleksander Popel · Jeremias Sulam 🔗 |
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Retro-fallback: retrosynthetic planning in an uncertain world ( Poster ) > link | Austin Tripp · Krzysztof Maziarz · Sarah Lewis · Marwin Segler · José Miguel Hernández-Lobato 🔗 |
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A Framework for Toxic PFAS Replacement based on GFlowNet and Chemical Foundation Model ( Poster ) > link | Eduardo Soares · Flaviu Cipcigan · Dmitry Zubarev · Emilio Vital Brazil 🔗 |
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Fast and Scalable Inference of Dynamical Systems via Integral Matching ( Poster ) > link | Baptiste Rossi · Dimitris Bertsimas 🔗 |
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MOFDiff: Coarse-grained Diffusion for Metal-Organic Framework Design ( Poster ) > link | Xiang Fu · Tian Xie · Andrew Rosen · Tommi Jaakkola · Jake Smith 🔗 |
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Rapid Prediction of Two-dimensional Airflow in an Operating Room using Scientific Machine Learning ( Poster ) > link | Gary Collins · Alexander New · Ryan Darragh · Ryan Darragh · Brian Damit · Christopher Stiles 🔗 |
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Latent Task-Specific Graph Network Simulators ( Poster ) > link | Philipp Dahlinger · Niklas Freymuth · Tai Hoang · Michael Volpp · Gerhard Neumann 🔗 |
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Citation-Similarity Relationships in Astrophysics Literature ( Poster ) > link | Nathaniel Imel · Zachary Hafen 🔗 |
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Electron-Derived Molecular Representation Learning for Real-World Molecular Physics ( Poster ) > link | Gyoung S. Na · Chanyoung Park 🔗 |
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Coupling Semi-supervised Learning with Reinforcement Learning for Better Decision Making --- An application to Cryo-EM Data Collection ( Poster ) > link | Ziping Xu · Quanfu Fan · Yilai Li · Emma Lee · john cohn · Ambuj Tewari · Seychelle Vos · Michael Cianfrocco 🔗 |
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Beam Enumeration: Probabilistic Explainability For Sample Efficient Self-conditioned Molecular Design ( Poster ) > link | Jeff Guo · Philippe Schwaller 🔗 |
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DeepSpeed4Science Initiative: Enabling Large-Scale Scientific Discovery through Sophisticated AI System Technologies ( Poster ) > link |
18 presentersShuaiwen Song · Bonnie Kruft · Minjia Zhang · Conglong Li · Shiyang Chen · Chengming Zhang · Masahiro Tanaka · Xiaoxia Wu · Mohammed AlQuraishi · Gustaf Ahdritz · Christina Floristean · Rick Stevens · Venkatram Vishwanath · Arvind Ramanathan · Sam Foreman · Kyle Hippe · Prasanna Balaprakash · Yuxiong He |
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Seeking Truth and Beauty in Flavor Physics with Machine Learning ( Poster ) > link | Konstantin Matchev · Katia Matcheva · Pierre Ramond · Sarunas Verner 🔗 |
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Learning Over Molecular Conformer Ensembles: Datasets and Benchmarks ( Poster ) > link |
13 presentersYanqiao Zhu · Jeehyun Hwang · Keir Adams · Zhen Liu · Bozhao Nan · Brock Stenfors · Yuanqi Du · Jatin Chauhan · Olaf Wiest · Olexandr Isayev · Connor Coley · Yizhou Sun · Wei Wang |
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Towards stable real-world equation discovery with assessing differentiating quality influence ( Poster ) > link | Mikhail Masliaev · Ilya Markov · Ilya Markov · Alexander Hvatov · Alexander Hvatov 🔗 |
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Optimizing Markov Chain Monte Carlo Convergence with Normalizing Flows and Gibbs Sampling ( Poster ) > link | Christoph Schönle · Marylou Gabrié 🔗 |
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Latent Neural PDE Solver for Time-dependent Systems ( Poster ) > link | Zijie Li · Saurabh Patil · Dule Shu · Amir Barati Farimani 🔗 |
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Learning Temporal Higher-order Patterns to Detect Anomalous Brain Activity ( Poster ) > link | Ali Behrouz · Farnoosh Hashemi 🔗 |
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Expression Sampler as a Dynamic Benchmark for Symbolic Regression ( Poster ) > link | Ioana Marinescu · Younes Strittmatter · Chad Williams · Sebastian Musslick 🔗 |
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What a Scientific Language Model Knows and Doesn't Know about Chemistry ( Poster ) > link | Lawrence Zhao · Carl Edwards · Heng Ji 🔗 |
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Beyond MD17: The xxMD Dataset as a Chemically Meaningful Benchmark for Neural Force Fields Development ( Poster ) > link | Zihan Pengmei · Junyu Liu · Yinan Shu 🔗 |
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MUBen: Benchmarking the Uncertainty of Molecular Representation Models ( Poster ) > link | Yinghao Li · Yinghao Li · Lingkai Kong · Lingkai Kong · Yuanqi Du · Yuanqi Du · Yue Yu · Yuchen Zhuang · Yuchen Zhuang · Wenhao Mu · Wenhao Mu · Chao Zhang · Chao Zhang 🔗 |
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Transformers are efficient hierarchical chemical graph learners ( Poster ) > link | Zihan Pengmei · Zimu Li · Chih-chan Tien · Risi Kondor · Aaron Dinner 🔗 |
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Contrasting Sequence with Structure: Pre-training Graph Representations with PLMs ( Poster ) > link | Louis Robinson · Timothy Atkinson · Liviu Copoiu · Patrick Bordes · Thomas PIERROT · Tom Barrett 🔗 |
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Text2Decision: Decoding Latent Variables in Risky Decision Making from Think Aloud Text ( Poster ) > link | Hanbo Xie · Huadong Xiong · Robert Wilson 🔗 |
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SBMLtoODEjax: Efficient Simulation and Optimization of Biological Network Models in JAX ( Poster ) > link | Mayalen Etcheverry · Michael Levin · Clément Moulin-Frier · Pierre-Yves Oudeyer 🔗 |
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Reinforcement Learning-Enabled Environmentally Friendly and Multi-functional Chrome-looking Plating ( Oral ) > link | Taigao Ma · Anwesha Saha · L. Jay Guo · Haozhu Wang 🔗 |
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CHARM: Creating Halos with Auto-Regressive Multi-stage networks ( Poster ) > link | Shivam Pandey · Chirag Modi · Benjamin Wandelt · Guilhem Lavaux 🔗 |
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Unleashing the Autoconversion Rates Forecasting: Evidential Regression from Satellite Data ( Poster ) > link | Maria Carolina Novitasari · Johannes Quaas · Miguel Rodrigues 🔗 |
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Role of Structural and Conformational Diversity for Machine Learning Potentials ( Poster ) > link | Nikhil Shenoy · Prudencio Tossou · Emmanuel Noutahi · Hadrien Mary · Dominique Beaini · Jiarui Ding 🔗 |
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Immunology Meets Artificial Intelligence: Expanding Our Scientific Toolbox ( Poster ) > link | Van Q. Truong · Matthew Lee · Dokyoon Kim · John Wherry · Marylyn Ritchie 🔗 |
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SpatialSSL: Whole-Brain Spatial Transcriptomics in the Mouse Brain with Self-Supervised Learning ( Poster ) > link | Till Richter · Anna Schaar · Francesca Drummer · Cheng-Wei Liao · Leopold Endres · Fabian Theis 🔗 |
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Learning to Relax: Setting Solver Parameters Across a Sequence of Linear System Instances ( Poster ) > link | Misha Khodak · Edmond Chow · Maria-Florina Balcan · Ameet Talwalkar 🔗 |
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AstroCLIP: Cross-Modal Pre-Training for Astronomical Foundation Models ( Poster ) > link |
14 presentersFrancois Lanusse · Liam Parker · Siavash Golkar · Alberto Bietti · Miles Cranmer · Michael Eickenberg · Geraud Krawezik · John McCabe · Ruben Ohana · Mariel Pettee · Bruno Régaldo-Saint Blancard · Tiberiu Tesileanu · Kyunghyun Cho · Shirley Ho |
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Modelling biology in novel ways - an AI-first course in Structural Bioinformatics ( Poster ) > link | Kieran Didi · Charles Harris · Pietro Lió · Rainer Beck 🔗 |
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ChemGymRL: An Interactive Framework for Reinforcement Learning for Digital Chemistry ( Poster ) > link | Chris Beeler · Sriram Ganapathi · Colin Bellinger · Mark Crowley · Isaac Tamblyn 🔗 |
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ExPT: Synthetic Pretraining for Few-Shot Experimental Design ( Poster ) > link | Tung Nguyen · Sudhanshu Agrawal · Sudhanshu Agrawal · Aditya Grover 🔗 |
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SE(3)-Invariant Multiparameter Persistent Homology for Chiral-Sensitive Molecular Property Prediction ( Poster ) > link | Andaç Demir · Francis Prael III · Bulent Kiziltan 🔗 |
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Self-supervised Learning to Discover Physical Objects and Predict Their Interactions from Raw Videos ( Poster ) > link | Sheng Cheng · 'YZ' Yezhou Yang · Yang Jiao · Yi Ren 🔗 |
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Deep Bayesian Experimental Design for Quantum Many-Body Systems ( Poster ) > link | Leopoldo Sarra · Florian Marquardt 🔗 |
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Prot2Text: Multimodal Protein's Function Generation with GNNs and Transformers ( Poster ) > link | Hadi Abdine · Michail Chatzianastasis · Costas Bouyioukos · Michalis Vazirgiannis 🔗 |
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Evaluating the structure of cognitive tasks with transfer learning ( Poster ) > link | Bruno Aristimunha · Raphael de Camargo · Walter Lopez Pinaya · Sylvain Chevallier · Alexandre Gramfort · Cédric ROMMEL 🔗 |
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Latent Space Simulator for Unveiling Molecular Free Energy Landscapes and Predicting Transition Dynamics ( Poster ) > link | Simon Dobers · Simon Dobers · Hannes Stärk · Xiang Fu · Dominique Beaini · Stephan Günnemann 🔗 |
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Bayesian Machine Scientist for Model Discovery in Psychology ( Poster ) > link | Joshua Hewson · Younes Strittmatter · Ioana Marinescu · Chad Williams · Sebastian Musslick 🔗 |
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Representing Core-collapse Supernova Light Curves Analytically with Symbolic Regression ( Poster ) > link | Kaylee de Soto · V Villar 🔗 |
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Re-evaluating Retrosynthesis Algorithms with Syntheseus ( Poster ) > link | Krzysztof Maziarz · Austin Tripp · Austin Tripp · Guoqing Liu · Guoqing Liu · Megan J Stanley · Megan J Stanley · Shufang Xie · Shufang Xie · Piotr Gaiński · Piotr Gaiński · Philipp Seidl · Philipp Seidl · Marwin Segler · Marwin Segler 🔗 |
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Sensitivity Analysis of Simulation-Based Inference for Galaxy Clustering ( Poster ) > link | Shivam Pandey · Chirag Modi · Benjamin Wandelt · Matthew Ho · ChangHoon Hahn · Bruno Régaldo-Saint Blancard 🔗 |
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Learning Scalar Fields for Molecular Docking with Fast Fourier Transforms ( Poster ) > link | Bowen Jing · Bowen Jing · Tommi Jaakkola · Tommi Jaakkola · Bonnie Berger 🔗 |
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Hypothesis Tests for Distributional Group Symmetry with Applications to Particle Physics ( Poster ) > link | Kenny Chiu · Kenny Chiu · Benjamin Bloem-Reddy · Benjamin Bloem-Reddy 🔗 |
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Unveiling the Secrets of $^1$H-NMR Spectroscopy: A Novel Approach Utilizing Attention Mechanisms ( Poster ) > link | Oliver Schilter · Marvin Alberts · Federico Zipoli · Alain C. Vaucher · Philippe Schwaller · Teodoro Laino 🔗 |
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Vertical AI-driven Scientific Discovery ( Poster ) > link | Yexiang Xue · Yexiang Xue 🔗 |
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Scalable Multimer Structure Prediction using Diffusion Models ( Poster ) > link | Peter Pao-Huang · Bowen Jing · Bowen Jing · Bonnie Berger 🔗 |
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AlphaFold Meets Flow Matching for Generating Protein Ensembles ( Poster ) > link | Bowen Jing · Bowen Jing · Bonnie Berger · Bonnie Berger · Tommi Jaakkola 🔗 |
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ComboPath: A model for predicting drug combination effects ( Poster ) > link | Duminda Ranasinghe · Changchang Liu · Daniel Spitz · Hok Hei Tam · Nathan Sanders 🔗 |
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ORDerly: Datasets and benchmarks for chemical reaction data ( Poster ) > link | Daniel Wigh · Daniel Wigh · Joe Arrowsmith · Joe Arrowsmith · Alexander Pomberger · Kobi Felton · Kobi Felton · Alexei Lapkin · Alexei Lapkin 🔗 |
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Stochastic force inference via density estimation ( Poster ) > link | Victor Chardès · Suryanarayana Maddu · Michael Shelley 🔗 |
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Mitigating Bias in Scientific Data: a Materials Science Case Study ( Poster ) > link | Hengrui Zhang · Wei Chen · James Rondinelli · Wei Chen 🔗 |
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Mapping the intermolecular interaction universe through self-supervised learning on molecular crystals ( Poster ) > link | Ada Fang · ZAIXI ZHANG · Marinka Zitnik 🔗 |
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ATAT: Automated Tissue Alignment and Traversal ( Poster ) > link | Steven Song · Steven Song · Emaan Mohsin · Andrey Kuznetsov · Andrey Kuznetsov · Christopher Weber · Robert Grossman · Robert Grossman · Aly Khan 🔗 |
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Van der Pol-informed Neural Networks for Multi-step-ahead Forecasting of Extreme Climatic Events ( Poster ) > link | Anurag Dutta · Anurag Dutta · Madhurima Panja · Madhurima Panja · Uttam Kumar · Uttam Kumar · Chittaranjan Hens · Tanujit Chakraborty · Tanujit Chakraborty 🔗 |
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Deep Learning with Physics Priors as Generalized Regularizers ( Poster ) > link | Frank Liu · Agniva Chowdhury 🔗 |
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SNIP: Bridging Mathematical Symbolic and Numeric Realms with Unified Pre-training ( Poster ) > link | Kazem Meidani · Kazem Meidani · Parshin Shojaee · Parshin Shojaee · Chandan Reddy · Chandan Reddy · Amir Barati Farimani 🔗 |
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Interpretable Neural PDE Solvers using Symbolic Frameworks ( Poster ) > link | Yolanne Lee · Yolanne Lee 🔗 |
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Infusing Spatial Knowledge into Deep Learning for Earth Science: A Hydrological Application ( Poster ) > link | Zelin Xu · Tingsong Xiao · Wenchong He · Yu Wang · Zhe Jiang 🔗 |
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Protein Language Model-Powered 3-Dimensional Ligand Binding Site Prediction from Protein Sequence ( Poster ) > link | Shuo Zhang · Lei Xie 🔗 |
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Multiple Physics Pretraining for Physical Surrogate Models ( Oral ) > link |
14 presentersJohn McCabe · Bruno Régaldo-Saint Blancard · Liam Parker · Ruben Ohana · Miles Cranmer · Alberto Bietti · Michael Eickenberg · Siavash Golkar · Geraud Krawezik · Francois Lanusse · Mariel Pettee · Tiberiu Tesileanu · Kyunghyun Cho · Shirley Ho |
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Transition Path Sampling with Boltzmann Generator-based MCMC Moves ( Poster ) > link | Michael Plainer · Hannes Stärk · Charlotte Bunne · Stephan Günnemann 🔗 |
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scCLIP: Multi-modal Single-cell Contrastive Learning Integration Pre-training ( Poster ) > link | Lei Xiong · Tianlong Chen · Manolis Kellis 🔗 |
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xVal: A Continuous Number Encoding for Large Language Models ( Poster ) > link |
14 presentersSiavash Golkar · Mariel Pettee · Michael Eickenberg · Alberto Bietti · Miles Cranmer · Geraud Krawezik · Francois Lanusse · John McCabe · Ruben Ohana · Liam Parker · Bruno Régaldo-Saint Blancard · Tiberiu Tesileanu · Kyunghyun Cho · Shirley Ho |
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XLuminA: An Auto-differentiating Discovery Framework for Super-Resolution Microscopy ( Oral ) > link | Carla Rodríguez · Sören Arlt · Leonhard Möckl · Mario Krenn 🔗 |
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Latent Diffusion Model for DNA Sequence Generation ( Poster ) > link | Zehui Li · Yuhao Ni · Tim Huygelen · Akashaditya Das · Guoxuan Xia · Guy-Bart Stan · Yiren Zhao 🔗 |
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Exploring the applications of Neural Cellular Automata in molecular sciences ( Poster ) > link | Sebastian Pagel · Lee Cronin 🔗 |
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Koopman-Assisted Reinforcement Learning ( Oral ) > link | Preston Rozwood · Edward Mehrez · Ludger Paehler · Wen Sun · Steven Brunton 🔗 |
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Automated distillation of genomic equations governing single cell gene expression ( Poster ) > link | Edouardo Honig · Frederique Ruf Zamojski · Stuart Sealfon · Ying Nian Wu · Zijun Frank Zhang 🔗 |
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RetroBridge: Modeling Retrosynthesis with Markov Bridges ( Poster ) > link | Ilia Igashov · Arne Schneuing · Arne Schneuing · Marwin Segler · Marwin Segler · Michael Bronstein · Michael Bronstein · Bruno Correia · Bruno Correia 🔗 |
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Adaptive learning acceleration for nonlinear PDE solvers ( Poster ) > link | Vinicius L S Silva · Vinicius L S Silva · Pablo Salinas · Pablo Salinas · Claire E Heaney · Claire E Heaney · Matthew Jackson · Matthew Jackson · Christopher C Pain · Christopher C Pain 🔗 |
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Unsupervised Representation Learning of Brain Activity via Bridging Voxel Activity and Functional Connectivity ( Poster ) > link | Ali Behrouz · Parsa Delavari · Farnoosh Hashemi 🔗 |
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Representation Learning for Spatial Multimodal Data Integration with Optimal Transport ( Poster ) > link | Xinhao Liu · Benjamin Raphael 🔗 |
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ALAS: Active Learning for Autoconversion Rates Prediction from Satellite Data ( Poster ) > link | Maria Carolina Novitasari · Maria Carolina Novitasari · Johannes Quaas · Miguel Rodrigues · Miguel Rodrigues 🔗 |
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Large Language Models in Molecular Discovery ( Poster ) > link | Nikita Janakarajan · Tim Erdmann · Sarathkrishna Swaminathan · Teodoro Laino · Jannis Born 🔗 |
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Virtual Receptors for Efficient Molecular Diffusion ( Poster ) > link | Matan Halfon · Eyal Rozenberg · Ehud Rivlin · Daniel Freedman 🔗 |
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Insight Miner: A Large-scale Multimodal Model for Insight Mining from Time Series ( Poster ) > link |
13 presentersYunkai Zhang · Yawen Zhang · Ming Zheng · Kezhen Chen · Chongyang Gao · Ruian Ge · Siyuan Teng · Amine Jelloul · Jinmeng Rao · Xiaoyuan Guo · Chiang-Wei Fang · Zeyu Zheng · Jie Yang |
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STRIDE: Structure-guided Generation for Inverse Design of Molecules ( Poster ) > link | Shehtab Zaman · Denis Akhiyarov · Mauricio Araya-Polo · Kenneth Chiu 🔗 |
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Stoichiometry Representation Learning with Polymorphic Crystal Structures ( Poster ) > link | Namkyeong Lee · Heewoong Noh · Gyoung S. Na · Tianfan Fu · Jimeng Sun · Chanyoung Park 🔗 |
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Exciton-Polariton Condensates: A Fourier Neural Operator Approach ( Poster ) > link | Surya Sathujoda · Yuan Wang · Kanishk Gandhi 🔗 |
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AI, Robot Neuroscientist: Reimagining Hypothesis Generation ( Poster ) > link | Jiaqi Shang · Jiaqi Shang · Will Xiao · Will Xiao 🔗 |
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Baking Symmetry into GFlowNets ( Oral ) > link | George Ma · Emmanuel Bengio · Yoshua Bengio · Dinghuai Zhang 🔗 |
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Shape Arithmetic Expressions ( Poster ) > link | Krzysztof Kacprzyk · Mihaela van der Schaar 🔗 |
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Evaluating Uncertainty Quantification approaches for Neural PDEs in scientific application ( Poster ) > link | Vardhan Dongre · Gurpreet Singh Hora 🔗 |
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Relaxed Octahedral Group Convolution for Learning Symmetry Breaking in 3D Physical Systems ( Poster ) > link | Rui Wang · Robin Walters · Tess Smidt 🔗 |
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Higher Order Equivariant Graph Neural Networks for Charge Density Prediction ( Poster ) > link | Teddy Koker · Keegan Quigley · Lin Li 🔗 |
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ChatPathway: Conversational Large Language Models for Biology Pathway Detection ( Poster ) > link | Yanjing Li · Hannan Xu · Haiteng Zhao · Hongyu Guo · Shengchao Liu 🔗 |
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A Transformer Model for Symbolic Regression towards Scientific Discovery ( Oral ) > link | Florian Lalande · Yoshitomo Matsubara · Naoya Chiba · Tatsunori Taniai · Ryo Igarashi · Yoshitaka Ushiku 🔗 |
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Using Foundation Models to Promote Digitization and Reproducibility in Scientific Experimentation ( Poster ) > link |
15 presentersAmol Thakkar · Andrea Giovannini · Antonio Foncubierta-Rodriguez · Carlo Baldassari · Dimitrios Christofidellis · Federico Zipoli · Gianmarco Gabrieli · Jannis Born · Mara Graziani · Marvin Alberts · Matteo Manica · Michael Stiefel · Oliver Schilter · Teodoro Laino · Patrick Ruch |
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Bad Exoplanet! Explaining Degraded Performance when Reconstructing Exoplanets Atmospheric Parameters ( Poster ) > link | Alkis Koudounas · Flavio Giobergia · Elena Baralis 🔗 |
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XBrainLab: An Open-Source Software for Explainable Artificial Intelligence-Based EEG Analysis ( Poster ) > link | Chia-ying Hsieh · Jing-Lun Chou · Yu-Hsin Chang · Chun-Shu Wei 🔗 |
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Learning Inter-Graph Interactions Between Heterogeneous Substructures of Chemical Systems ( Poster ) > link | Gyoung S. Na 🔗 |
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Compositional Generative Inverse Design ( Poster ) > link | Tailin Wu · Takashi Maruyama · Long Wei · Tao Zhang · Yilun Du · Yilun Du · Gianluca Iaccarino · Jure Leskovec · Jure Leskovec 🔗 |
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MoleCLUEs: Molecular Conformers Maximally In-Distribution for Predictive Models ( Poster ) > link | Michael Maser · Nataša Tagasovska · Jae Hyeon Lee · Andrew Watkins 🔗 |
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scBiGNN: Bilevel Graph Representation Learning for Cell Type Classification from Single-cell RNA Sequencing Data ( Poster ) > link | Rui Yang · Wenrui Dai · Chenglin Li · Junni Zou · Dapeng Wu · Hongkai Xiong 🔗 |
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Rethinking Bayesian Optimization with Gaussian Processes: Insights from Hyperspectral Trait Search ( Poster ) > link | Ruhana Azam · Sanmi Koyejo · Samuel Fernandes · Mohammed Kebir · Andrew Leakey · Alexander Lipka 🔗 |
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PreDiff: Precipitation Nowcasting with Latent Diffusion Models ( Poster ) > link | Zhihan Gao · Xingjian Shi · Boran Han · Hao Wang · Xiaoyong Jin · Danielle Maddix · Yi Zhu · Mu Li · Yuyang (Bernie) Wang 🔗 |
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Explaining Drug Repositioning: A Case-Based Reasoning Graph Neural Network Approach ( Poster ) > link | Adriana Carolina Gonzalez Cavazos 🔗 |
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AI Ethics Education for Scientists ( Poster ) > link | Savannah Thais 🔗 |
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Harmonic Prior Self-conditioned Flow Matching for Multi-Ligand Docking and Binding Site Design ( Oral ) > link | Hannes Stärk · Bowen Jing · Regina Barzilay · Tommi Jaakkola 🔗 |
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Modelling Microbial Communities with Graph Neural Networks ( Poster ) > link | Albane Ruaud · Cansu Sancaktar · Marco Bagatella · Christoph Ratzke · Georg Martius 🔗 |
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Scalable Diffusion for Materials Generation ( Poster ) > link | Sherry Yang · KwangHwan Cho · Amil Merchant · Pieter Abbeel · Dale Schuurmans · Igor Mordatch · Ekin Dogus Cubuk 🔗 |
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Towards out-of-distribution generalizable predictions of chemical kinetics properties ( Poster ) > link | Zihao Wang · Yongqiang Chen · Yang Duan · Weijiang Li · Bo Han · James Cheng · Hanghang Tong · Hanghang Tong 🔗 |
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Single-cell Masked Autoencoder: An Accurate and Interpretable Automated Immunophenotyper ( Poster ) > link |
20 presentersJaesik Kim · Matei Ionita · Matthew Lee · Michelle McKeague · Ajinkya Pattekar · Mark Painter · Joost Wagenaar · Van Q. Truong · Dylan Norton · Divij Mathew · Yonghyun Nam · Sokratis Apostolidis · Patryk Orzechowski · Sang-Hyuk Jung · Jakob Woerner · Yidi Huang · Nuala Meyer · Allison Greenplate · Dokyoon Kim · John Wherry |
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AI Framework for Generative Design of Computational Experiments with Structures in Physical Environment ( Poster ) > link | Gleb Solovev · Anna Kalyuzhnaya · Alexander Hvatov · Nikita Starodubcev · Oleg Petrov · Nikolay Nikitin 🔗 |
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Molecule-edit templates for efficient and accurate retrosynthesis prediction ( Poster ) > link | Mikołaj Sacha · Michał Sadowski · Piotr Kozakowski · Ruard van Workum · Stanislaw Jastrzebski 🔗 |
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Machine Learning for Blockchain ( Poster ) > link | Luyao Zhang · Luyao Zhang 🔗 |
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Sample-efficient Antibody Design through Protein Language Model for Risk-aware Batch Bayesian Optimization ( Poster ) > link | Yanzheng Wang · TIANYU SHI · Jie Fu 🔗 |
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Easy to learn hard to master - how to solve an arbitrary equation with PINN ( Poster ) > link | Alexander Hvatov · Damir Aminev · Nikita Demyanchuk 🔗 |
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Molecule Design by Latent Prompt Transformer ( Poster ) > link | Deqian Kong · Yuhao Huang · Jianwen Xie · Ying Nian Wu 🔗 |