Publications (109)
Data-Driven Simulator for Mechanical Circulatory Support with Domain Adversarial Neural Process
Sophia Sun, Wenyuan Chen, Zihao Zhou +3
Mechanical Circulatory Support (MCS) devices, implemented as a probabilistic deep sequence model. Existing mechanical simulators for MCS rely on oversimplifying assumptions and are…
Manifold-Guided Attention Steering
Ian Li, Kapilesh Guruprasad, Raunak Sengupta +3
Large language models frequently produce errors in reasoning tasks despite possessing the underlying knowledge required for correct reasoning. One possible approach to improve reas…
Neural Point Process for Learning Spatiotemporal Event Dynamics
Zihao Zhou, Xingyi Yang, Ryan Rossi +2
Learning the dynamics of spatiotemporal events is a fundamental problem. Neural point processes enhance the expressivity of point process models with deep neural networks. However,…
Multi-Fidelity Residual Neural Processes for Scalable Surrogate Modeling
Ruijia Niu, Dongxia Wu, Kai Kim +3
Multi-fidelity surrogate modeling aims to learn an accurate surrogate at the highest fidelity level by combining data from multiple sources. Traditional methods relying on Gaussian…
SimulCost: A Cost-Aware Benchmark and Toolkit for Automating Physics Simulations with LLMs
Yadi Cao, Sicheng Lai, Jiahe Huang +12
Evaluating LLM agents for scientific tasks has focused on token costs while ignoring tool-use costs like simulation time and experimental resources. As a result, metrics like pass@…
Technical report: Improving the properties of molecules generated by LIMO
Vineet Thumuluri, Peter Eckmann, Michael K. Gilson +1
This technical report investigates variants of the Latent Inceptionism on Molecules (LIMO) framework to improve the properties of generated molecules. We conduct ablative studies o…
VICON: Vision In-Context Operator Networks for Multi-Physics Fluid Dynamics Prediction
Yadi Cao, Yuxuan Liu, Liu Yang +3
In-Context Operator Networks (ICONs) have demonstrated the ability to learn operators across diverse partial differential equations using few-shot, in-context learning. However, ex…
MORL-Prompt: An Empirical Analysis of Multi-Objective Reinforcement Learning for Discrete Prompt Optimization
Yasaman Jafari, Dheeraj Mekala, Rose Yu +1
RL-based techniques can be employed to search for prompts that, when fed into a target language model, maximize a set of user-specified reward functions. However, in many target ap…
Trajectory Prediction using Equivariant Continuous Convolution
Robin Walters, Jinxi Li, Rose Yu
Trajectory prediction is a critical part of many AI applications, for example, the safe operation of autonomous vehicles. However, current methods are prone to making inconsistent…
Latent Space Symmetry Discovery
Jianke Yang, Nima Dehmamy, Robin Walters +1
Equivariant neural networks require explicit knowledge of the symmetry group. Automatic symmetry discovery methods aim to relax this constraint and learn invariance and equivarianc…
Aortic Pressure Forecasting with Deep Sequence Learning
Eliza Huang, Rui Wang, Uma Chandrasekaran +1
Mean aortic pressure (MAP) is a major determinant of perfusion in all organs systems. The ability to forecast MAP would enhance the ability of physicians to estimate prognosis of t…
Think like a Scientist: Physics-guided LLM Agent for Equation Discovery
Jianke Yang, Ohm Venkatachalam, Mohammad Kianezhad +2
Explaining observed phenomena through symbolic, interpretable formulas is a fundamental goal of science. Recently, large language models (LLMs) have emerged as promising tools for…
ClimaQA: An Automated Evaluation Framework for Climate Question Answering Models
Veeramakali Vignesh Manivannan, Yasaman Jafari, Srikar Eranky +6
The use of Large Language Models (LLMs) in climate science has recently gained significant attention. However, a critical issue remains: the lack of a comprehensive evaluation fram…
Efficient Tensor Decomposition with Boolean Factors
Sung-En Chang, Xun Zheng, Ian E. H. Yen +2
Tensor decomposition has been extensively used as a tool for exploratory analysis. Motivated by neuroscience applications, we study tensor decomposition with Boolean factors. The r…
Dynamic Relational Inference in Multi-Agent Trajectories
Ruichao Xiao, Manish Kumar Singh, Rose Yu
Inferring interactions from multi-agent trajectories has broad applications in physics, vision and robotics. Neural relational inference (NRI) is a deep generative model that can r…
Predicting the Future of AI with AI: High-quality link prediction in an exponentially growing knowledge network
Mario Krenn, Lorenzo Buffoni, Bruno Coutinho +13
A tool that could suggest new personalized research directions and ideas by taking insights from the scientific literature could significantly accelerate the progress of science. A…
Socratic Learning: Augmenting Generative Models to Incorporate Latent Subsets in Training Data
Paroma Varma, Bryan He, Dan Iter +4
A challenge in training discriminative models like neural networks is obtaining enough labeled training data. Recent approaches use generative models to combine weak supervision so…
Breaking the Factorization Barrier in Diffusion Language Models
Ian Li, Zilei Shao, Benjie Wang +3
Diffusion language models theoretically allow for efficient parallel generation but are practically hindered by the ``factorization barrier'': the assumption that simultaneously pr…
SimulRAG: Simulator-based RAG for Grounding LLMs in Long-form Scientific QA
Haozhou Xu, Dongxia Wu, Matteo Chinazzi +3
Large Language Models (LLMs) show promise in generating long-form scientific explanations that synthesize evidence and connect multiple factors. However, in long-form scientific qu…
AtlasD: Automatic Local Symmetry Discovery
Manu Bhat, Jonghyun Park, Jianke Yang +3
Existing symmetry discovery methods predominantly focus on global transformations across the entire system or space, but they fail to consider the symmetries in local neighborhoods…
Deep Bayesian Active Learning for Accelerating Stochastic Simulation
Dongxia Wu, Ruijia Niu, Matteo Chinazzi +3
Stochastic simulations such as large-scale, spatiotemporal, age-structured epidemic models are computationally expensive at fine-grained resolution. While deep surrogate models can…
Symmetries, flat minima, and the conserved quantities of gradient flow
Bo Zhao, Iordan Ganev, Robin Walters +2
Empirical studies of the loss landscape of deep networks have revealed that many local minima are connected through low-loss valleys. Yet, little is known about the theoretical ori…
ToolMol: Evolutionary Agentic Framework for Multi-objective Drug Discovery
Andrew Y. Zhou, Sharvaree Vadgama, Sumanth Varambally +3
Advances in large language models (LLMs) have recently opened new and promising avenues for small-molecule drug discovery. Yet existing LLM-based approaches for molecular generatio…
Disentangled Multi-Fidelity Deep Bayesian Active Learning
Dongxia Wu, Ruijia Niu, Matteo Chinazzi +2
To balance quality and cost, various domain areas of science and engineering run simulations at multiple levels of sophistication. Multi-fidelity active learning aims to learn a di…
Understanding Mode Connectivity via Parameter Space Symmetry
Bo Zhao, Nima Dehmamy, Robin Walters +1
Neural network minima are often connected by curves along which train and test loss remain nearly constant, a phenomenon known as mode connectivity. While this property has enabled…
MF-LAL: Drug Compound Generation Using Multi-Fidelity Latent Space Active Learning
Peter Eckmann, Dongxia Wu, Germano Heinzelmann +2
Current generative models for drug discovery primarily use molecular docking as an oracle to guide the generation of active compounds. However, such models are often not useful in…
Symmetry in Neural Network Parameter Spaces
Bo Zhao, Robin Walters, Rose Yu
Modern deep learning models are highly overparameterized, resulting in large sets of parameter configurations that yield the same outputs. A significant portion of this redundancy…
Discovering Latent Causal Graphs from Spatiotemporal Data
Kun Wang, Sumanth Varambally, Duncan Watson-Parris +2
Many important phenomena in scientific fields like climate, neuroscience, and epidemiology are naturally represented as spatiotemporal gridded data with complex interactions. Infer…
Koopman Neural Forecaster for Time Series with Temporal Distribution Shifts
Rui Wang, Yihe Dong, Sercan Ã. Arik +1
Temporal distributional shifts, with underlying dynamics changing over time, frequently occur in real-world time series and pose a fundamental challenge for deep neural networks (D…
Tensor Regression Meets Gaussian Processes
Rose Yu, Guangyu Li, Yan Liu
Low-rank tensor regression, a new model class that learns high-order correlation from data, has recently received considerable attention. At the same time, Gaussian processes (GP)…
Traffic Forecasting using Vehicle-to-Vehicle Communication
Steven Wong, Lejun Jiang, Robin Walters +3
We take the first step in using vehicle-to-vehicle (V2V) communication to provide real-time on-board traffic predictions. In order to best utilize real-world V2V communication data…
DeepGLEAM: A hybrid mechanistic and deep learning model for COVID-19 forecasting
Dongxia Wu, Liyao Gao, Xinyue Xiong +4
We introduce DeepGLEAM, a hybrid model for COVID-19 forecasting. DeepGLEAM combines a mechanistic stochastic simulation model GLEAM with deep learning. It uses deep learning to lea…
Calibrating LLMs with Semantic-level Reward
Fengfei Yu, Ruijia Niu, Dongxia Wu +2
As large language models (LLMs) are deployed in consequential settings such as medical question answering and legal reasoning, the ability to estimate when their outputs are likely…
Divide and Learn: Multi-Objective Combinatorial Optimization at Scale
Esha Singh, Dongxia Wu, Chien-Yi Yang +3
Multi-objective combinatorial optimization seeks Pareto-optimal solutions over exponentially large discrete spaces, yet existing methods sacrifice generality, scalability, or theor…
Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems
Xuan Zhang, Limei Wang, Jacob Helwig +60
Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural sciences. Today, AI has started to advance natural sciences by improving, accelerating…
TGLF-WINN: Data-Efficient Deep Learning Surrogate for Turbulent Transport Modeling in Fusion
Yadi Cao, Futian Zhang, Wesley Liu +7
The Trapped Gyro-Landau Fluid (TGLF) model provides fast, accurate predictions of turbulent transport in tokamaks, but whole device simulations requiring thousands of evaluations r…
Long-term Forecasting with TiDE: Time-series Dense Encoder
Abhimanyu Das, Weihao Kong, Andrew Leach +3
Recent work has shown that simple linear models can outperform several Transformer based approaches in long term time-series forecasting. Motivated by this, we propose a Multi-laye…
Incorporating Symmetry into Deep Dynamics Models for Improved Generalization
Rui Wang, Robin Walters, Rose Yu
Recent work has shown deep learning can accelerate the prediction of physical dynamics relative to numerical solvers. However, limited physical accuracy and an inability to general…
Probabilistic Symmetry for Multi-Agent Dynamics
Sophia Sun, Robin Walters, Jinxi Li +1
Learning multi-agent dynamics is a core AI problem with broad applications in robotics and autonomous driving. While most existing works focus on deterministic prediction, producin…
Time Series, Vision, and Language: Exploring the Limits of Alignment in Contrastive Representation Spaces
Pratham Yashwante, Rose Yu
The Platonic Representation Hypothesis posits that learned representations from models trained on different modalities converge to a shared latent structure of the world. However,…
Probabilistic Emulation of a Global Climate Model with Spherical DYffusion
Salva Rühling Cachay, Brian Henn, Oliver Watt-Meyer +2
Data-driven deep learning models are transforming global weather forecasting. It is an open question if this success can extend to climate modeling, where the complexity of the dat…
Learning Granger Causality from Instance-wise Self-attentive Hawkes Processes
Dongxia Wu, Tsuyoshi Idé, Aurélie Lozano +5
We address the problem of learning Granger causality from asynchronous, interdependent, multi-type event sequences. In particular, we are interested in discovering instance-level c…
Target-Free Compound Activity Prediction via Few-Shot Learning
Peter Eckmann, Jake Anderson, Michael K. Gilson +1
Predicting the activities of compounds against protein-based or phenotypic assays using only a few known compounds and their activities is a common task in target-free drug discove…
Hilbert: Recursively Building Formal Proofs with Informal Reasoning
Sumanth Varambally, Thomas Voice, Yanchao Sun +3
Large Language Models (LLMs) demonstrate impressive mathematical reasoning abilities, but their solutions frequently contain errors that cannot be automatically checked. Formal the…
Back to Bayesics: Uncovering Human Mobility Distributions and Anomalies with an Integrated Statistical and Neural Framework
Minxuan Duan, Yinlong Qian, Lingyi Zhao +4
Existing methods for anomaly detection often fall short due to their inability to handle the complexity, heterogeneity, and high dimensionality inherent in real-world mobility data…
Functional-level Uncertainty Quantification for Calibrated Fine-tuning on LLMs
Ruijia Niu, Dongxia Wu, Rose Yu +1
Accurate uncertainty quantification in large language models (LLMs) is essential for reliable confidence estimation, yet fine-tuned LLMs often become overconfident under limited ad…
AI-Bind: Improving Binding Predictions for Novel Protein Targets and Ligands
Ayan Chatterjee, Robin Walters, Zohair Shafi +7
Identifying novel drug-target interactions (DTI) is a critical and rate limiting step in drug discovery. While deep learning models have been proposed to accelerate the identificat…
Bridging Physics-based and Data-driven modeling for Learning Dynamical Systems
Rui Wang, Danielle Maddix, Christos Faloutsos +2
How can we learn a dynamical system to make forecasts, when some variables are unobserved? For instance, in COVID-19, we want to forecast the number of infected and death cases but…
Eluna: An Agentic LLM System for Automating Warehouse Operations with Reasoning and Task Execution
Ning Liu, Kalle Kujanpää, Zhaoxuan Zhu +11
Warehouse operations are governed by Standard Operating Procedures (SOPs) that encode complex, multi-system decision logic, which must be executed reliably under strict time constr…
Faster Optimization on Sparse Graphs via Neural Reparametrization
Nima Dehmamy, Csaba Both, Jianzhi Long +1
In mathematical optimization, second-order Newton's methods generally converge faster than first-order methods, but they require the inverse of the Hessian, hence are computational…
Learning Disentangled Representations of Video with Missing Data
Armand Comas-Massagué, Chi Zhang, Zlatan Feric +2
Missing data poses significant challenges while learning representations of video sequences. We present Disentangled Imputed Video autoEncoder (DIVE), a deep generative model that…
Finding Patient Zero: Learning Contagion Source with Graph Neural Networks
Chintan Shah, Nima Dehmamy, Nicola Perra +4
Locating the source of an epidemic, or patient zero (P0), can provide critical insights into the infection's transmission course and allow efficient resource allocation. Existing m…
MFBind: a Multi-Fidelity Approach for Evaluating Drug Compounds in Practical Generative Modeling
Peter Eckmann, Dongxia Wu, Germano Heinzelmann +2
Current generative models for drug discovery primarily use molecular docking to evaluate the quality of generated compounds. However, such models are often not useful in practice b…
Learning from Multiway Data: Simple and Efficient Tensor Regression
Rose Yu, Yan Liu
Tensor regression has shown to be advantageous in learning tasks with multi-directional relatedness. Given massive multiway data, traditional methods are often too slow to operate…
SELFIES and the future of molecular string representations
Mario Krenn, Qianxiang Ai, Senja Barthel +28
Artificial intelligence (AI) and machine learning (ML) are expanding in popularity for broad applications to challenging tasks in chemistry and materials science. Examples include…
Generator Surgery for Compressed Sensing
Niklas Smedemark-Margulies, Jung Yeon Park, Max Daniels +3
Image recovery from compressive measurements requires a signal prior for the images being reconstructed. Recent work has explored the use of deep generative models with low latent…
Deep Imitation Learning for Bimanual Robotic Manipulation
Fan Xie, Alexander Chowdhury, M. Clara De Paolis Kaluza +3
We present a deep imitation learning framework for robotic bimanual manipulation in a continuous state-action space. A core challenge is to generalize the manipulation skills to ob…
Understanding the Difficulty of Solving Cauchy Problems with PINNs
Tao Wang, Bo Zhao, Sicun Gao +1
Physics-Informed Neural Networks (PINNs) have gained popularity in scientific computing in recent years. However, they often fail to achieve the same level of accuracy as classical…
Assessing Low Back Movement with Motion Tape Sensor Data Through Deep Learning
Jared Levy, Aarti Lalwani, Elijah Wyckoff +4
Back pain is a pervasive issue affecting a significant portion of the population, often worsened by certain movements of the lower back. Assessing these movements is important for…
CaTS-Bench: Can Language Models Describe Time Series?
Luca Zhou, Pratham Yashwante, Marshall Fisher +4
Time series captioning, the task of describing time series in natural language, requires numeric and temporal reasoning, trend interpretation, and contextual understanding. Existin…
Quantifying Uncertainty in Deep Spatiotemporal Forecasting
Dongxia Wu, Liyao Gao, Xinyue Xiong +4
Deep learning is gaining increasing popularity for spatiotemporal forecasting. However, prior works have mostly focused on point estimates without quantifying the uncertainty of th…
Towards Physics-informed Deep Learning for Turbulent Flow Prediction
Rui Wang, Karthik Kashinath, Mustafa Mustafa +2
While deep learning has shown tremendous success in a wide range of domains, it remains a grand challenge to incorporate physical principles in a systematic manner to the design, t…
Can LLM feedback enhance review quality? A randomized study of 20K reviews at ICLR 2025
Nitya Thakkar, Mert Yuksekgonul, Jake Silberg +6
Peer review at AI conferences is stressed by rapidly rising submission volumes, leading to deteriorating review quality and increased author dissatisfaction. To address these issue…
Multi-fidelity Hierarchical Neural Processes
Dongxia Wu, Matteo Chinazzi, Alessandro Vespignani +2
Science and engineering fields use computer simulation extensively. These simulations are often run at multiple levels of sophistication to balance accuracy and efficiency. Multi-f…
Conformal Prediction for Time-series Forecasting with Change Points
Sophia Sun, Rose Yu
Conformal prediction has been explored as a general and efficient way to provide uncertainty quantification for time series. However, current methods struggle to handle time series…
Taming the Long Tail of Deep Probabilistic Forecasting
Jedrzej Kozerawski, Mayank Sharan, Rose Yu
Deep probabilistic forecasting is gaining attention in numerous applications ranging from weather prognosis, through electricity consumption estimation, to autonomous vehicle traje…
Understanding why shooters shoot -- An AI-powered engine for basketball performance profiling
Alejandro Rodriguez Pascual, Ishan Mehta, Muhammad Khan +2
Understanding player shooting profiles is an essential part of basketball analysis: knowing where certain opposing players like to shoot from can help coaches neutralize offensive…
NAOMI: Non-Autoregressive Multiresolution Sequence Imputation
Yukai Liu, Rose Yu, Stephan Zheng +2
Missing value imputation is a fundamental problem in spatiotemporal modeling, from motion tracking to the dynamics of physical systems. Deep autoregressive models suffer from error…
A Survey of Weight Space Learning: Understanding, Representation, and Generation
Xiaolong Han, Zehong Wang, Bo Zhao +8
Neural network weights are typically viewed as the end product of training, while most deep learning research focuses on data, features, and architectures. However, recent advances…
Symmetry-Informed Governing Equation Discovery
Jianke Yang, Wang Rao, Nima Dehmamy +2
Despite the advancements in learning governing differential equations from observations of dynamical systems, data-driven methods are often unaware of fundamental physical laws, su…
Copula Conformal Prediction for Multi-step Time Series Forecasting
Sophia Sun, Rose Yu
Accurate uncertainty measurement is a key step to building robust and reliable machine learning systems. Conformal prediction is a distribution-free uncertainty quantification algo…
Guardian-regularized Safe Offline Reinforcement Learning for Smart Weaning of Mechanical Circulatory Devices
Aysin Tumay, Sophia Sun, Sonia Fereidooni +3
We study the sequential decision-making problem for automated weaning of mechanical circulatory support (MCS) devices in cardiogenic shock patients. MCS devices are percutaneous mi…
Neural Lander: Stable Drone Landing Control using Learned Dynamics
Guanya Shi, Xichen Shi, Michael O'Connell +5
Precise near-ground trajectory control is difficult for multi-rotor drones, due to the complex aerodynamic effects caused by interactions between multi-rotor airflow and the enviro…
Generative OOD-regularized Model-based Policy Optimization
Aysin Tumay, Jiahe Huang, Elise Jortberg +1
We study sequential decision-making with offline reinforcement learning (RL). Traditional offline RL policies may result in out-of-distribution (OOD) actions when training relies o…
Elucidated Rolling Diffusion Models for Probabilistic Forecasting of Complex Dynamics
Salva Rühling Cachay, Miika Aittala, Karsten Kreis +4
Diffusion models are a powerful tool for probabilistic forecasting, yet most applications in high-dimensional complex systems predict future states individually. This approach stru…
Improving Learning to Optimize Using Parameter Symmetries
Guy Zamir, Aryan Dokania, Bo Zhao +1
We analyze a learning-to-optimize (L2O) algorithm that exploits parameter space symmetry to enhance optimization efficiency. Prior work has shown that jointly learning symmetry tra…
Understanding the Representation Power of Graph Neural Networks in Learning Graph Topology
Nima Dehmamy, Albert-László Barabási, Rose Yu
To deepen our understanding of graph neural networks, we investigate the representation power of Graph Convolutional Networks (GCN) through the looking glass of graph moments, a ke…
Multi-resolution Tensor Learning for Large-Scale Spatial Data
Stephan Zheng, Rose Yu, Yisong Yue
High-dimensional tensor models are notoriously computationally expensive to train. We present a meta-learning algorithm, MMT, that can significantly speed up the process for spatia…
Long-term Forecasting using Higher Order Tensor RNNs
Rose Yu, Stephan Zheng, Anima Anandkumar +1
We present Higher-Order Tensor RNN (HOT-RNN), a novel family of neural sequence architectures for multivariate forecasting in environments with nonlinear dynamics. Long-term foreca…
Diffusion-BBO: Diffusion-Based Inverse Modeling for Online Black-Box Optimization
Dongxia Wu, Nikki Lijing Kuang, Ruijia Niu +2
Online black-box optimization (BBO) aims to optimize an objective function by iteratively querying a black-box oracle in a sample-efficient way. While prior studies focus on forwar…
U-Cast: A Surprisingly Simple and Efficient Frontier Probabilistic AI Weather Forecaster
Salva Rühling Cachay, Duncan Watson-Parris, Rose Yu
AI-based weather forecasting now rivals traditional physics-based ensembles, but state-of-the-art (SOTA) models rely on specialized architectures and massive computational budgets,…
Adapting While Learning: Grounding LLMs for Scientific Problems with Intelligent Tool Usage Adaptation
Bohan Lyu, Yadi Cao, Duncan Watson-Parris +3
Large Language Models (LLMs) demonstrate promising capabilities in solving scientific problems but often suffer from the issue of hallucination. While integrating LLMs with tools c…
Symmetry Teleportation for Accelerated Optimization
Bo Zhao, Nima Dehmamy, Robin Walters +1
Existing gradient-based optimization methods update parameters locally, in a direction that minimizes the loss function. We study a different approach, symmetry teleportation, that…
Data Augmentation vs. Equivariant Networks: A Theory of Generalization on Dynamics Forecasting
Rui Wang, Robin Walters, Rose Yu
Exploiting symmetry in dynamical systems is a powerful way to improve the generalization of deep learning. The model learns to be invariant to transformation and hence is more robu…
Automatic Symmetry Discovery with Lie Algebra Convolutional Network
Nima Dehmamy, Robin Walters, Yanchen Liu +2
Existing equivariant neural networks require prior knowledge of the symmetry group and discretization for continuous groups. We propose to work with Lie algebras (infinitesimal gen…
Emergence of Hierarchical Emotion Organization in Large Language Models
Maya Okawa, Bo Zhao, Eric J. Bigelow +4
As large language models (LLMs) increasingly power conversational agents, understanding how they model users' emotional states is critical for ethical deployment. Inspired by emoti…
Physics-Guided Deep Learning for Dynamical Systems: A Survey
Rui Wang, Rose Yu
Modeling complex physical dynamics is a fundamental task in science and engineering. Traditional physics-based models are sample efficient, and interpretable but often rely on rigi…
Demystifying Mergeability: Interpretable Properties to Predict Model Merging Success
Luca Zhou, Bo Zhao, Rose Yu +1
Model merging combines knowledge from separately fine-tuned models, yet the factors driving its success remain poorly understood. While recent work treats mergeability as an intrin…
Zephyrus: An Agentic Framework for Weather Science
Sumanth Varambally, Marshall Fisher, Jas Thakker +14
Foundation models for weather science are pre-trained on vast amounts of structured numerical data and outperform traditional weather forecasting systems. However, these models lac…
On the Theoretical Expressive Power and the Design Space of Higher-Order Graph Transformers
Cai Zhou, Rose Yu, Yusu Wang
Graph transformers have recently received significant attention in graph learning, partly due to their ability to capture more global interaction via self-attention. Nevertheless,…
On the Connection Between MPNN and Graph Transformer
Chen Cai, Truong Son Hy, Rose Yu +1
Graph Transformer (GT) recently has emerged as a new paradigm of graph learning algorithms, outperforming the previously popular Message Passing Neural Network (MPNN) on multiple b…
Automatic Integration for Spatiotemporal Neural Point Processes
Zihao Zhou, Rose Yu
Learning continuous-time point processes is essential to many discrete event forecasting tasks. However, integration poses a major challenge, particularly for spatiotemporal point…
Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting
Yaguang Li, Rose Yu, Cyrus Shahabi +1
Spatiotemporal forecasting has various applications in neuroscience, climate and transportation domain. Traffic forecasting is one canonical example of such learning task. The task…
Recursive Flow Matching
Jiahe Huang, Sihan Xu, Sharvaree Vadgama +1
Generative models have emerged as a powerful paradigm for solving physics systems and modeling complex spatiotemporal dynamics. However, achieving high physical accuracy without in…
ClimSim-Online: A Large Multi-scale Dataset and Framework for Hybrid ML-physics Climate Emulation
Sungduk Yu, Zeyuan Hu, Akshay Subramaniam +44
Modern climate projections lack adequate spatial and temporal resolution due to computational constraints, leading to inaccuracies in representing critical processes like thunderst…
Generative Adversarial Symmetry Discovery
Jianke Yang, Robin Walters, Nima Dehmamy +1
Despite the success of equivariant neural networks in scientific applications, they require knowing the symmetry group a priori. However, it may be difficult to know which symmetry…
LIMO: Latent Inceptionism for Targeted Molecule Generation
Peter Eckmann, Kunyang Sun, Bo Zhao +3
Generation of drug-like molecules with high binding affinity to target proteins remains a difficult and resource-intensive task in drug discovery. Existing approaches primarily emp…
DYffusion: A Dynamics-informed Diffusion Model for Spatiotemporal Forecasting
Salva Rühling Cachay, Bo Zhao, Hailey Joren +1
While diffusion models can successfully generate data and make predictions, they are predominantly designed for static images. We propose an approach for efficiently training diffu…
Discovering Symbolic Differential Equations with Symmetry Invariants
Jianke Yang, Manu Bhat, Bryan Hu +4
Discovering symbolic differential equations from data uncovers fundamental dynamical laws underlying complex systems. However, existing methods often struggle with the vast search…
Meta-Learning Dynamics Forecasting Using Task Inference
Rui Wang, Robin Walters, Rose Yu
Current deep learning models for dynamics forecasting struggle with generalization. They can only forecast in a specific domain and fail when applied to systems with different para…