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06/14/2023
ClimSim: An open large-scale dataset for training high-resolution physics emulators in hybrid multi-scale climate simulators
Modern climate projections lack adequate spatial and temporal resolution...
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05/07/2023
Disentangled Multi-Fidelity Deep Bayesian Active Learning
To balance quality and cost, various domain areas of science and enginee...
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04/17/2023
Long-term Forecasting with TiDE: Time-series Dense Encoder
Recent work has shown that simple linear models can outperform several T...
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03/17/2023
Understanding why shooters shoot – An AI-powered engine for basketball performance profiling
Understanding player shooting profiles is an essential part of basketbal...
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02/01/2023
Generative Adversarial Symmetry Discovery
Despite the success of equivariant neural networks in scientific applica...
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01/27/2023
On the Connection Between MPNN and Graph Transformer
Graph Transformer (GT) recently has emerged as a new paradigm of graph l...
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12/06/2022
Copula Conformal Prediction for Multi-step Time Series Forecasting
Accurate uncertainty measurement is a key step to building robust and re...
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06/17/2022
LIMO: Latent Inceptionism for Targeted Molecule Generation
Generation of drug-like molecules with high binding affinity to target p...
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06/10/2022
Multi-fidelity Hierarchical Neural Processes
Science and engineering fields use computer simulation extensively. Thes...
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05/26/2022
Faster Optimization on Sparse Graphs via Neural Reparametrization
In mathematical optimization, second-order Newton's methods generally co...
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05/21/2022
Symmetry Teleportation for Accelerated Optimization
Existing gradient-based optimization methods update the parameters local...
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05/04/2022
Probabilistic Symmetry for Improved Trajectory Forecasting
Trajectory prediction is a core AI problem with broad applications in ro...
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03/31/2022
SELFIES and the future of molecular string representations
Artificial intelligence (AI) and machine learning (ML) are expanding in ...
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02/27/2022
Taming the Long Tail of Deep Probabilistic Forecasting
Deep probabilistic forecasting is gaining attention in numerous applicat...
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12/25/2021
AI-Bind: Improving Binding Predictions for Novel Protein Targets and Ligands
Identifying novel drug-target interactions (DTI) is a critical and rate ...
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12/12/2021
Neural Point Process for Learning Spatiotemporal Event Dynamics
Learning the dynamics of spatiotemporal events is a fundamental problem....
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09/15/2021
Automatic Symmetry Discovery with Lie Algebra Convolutional Network
Existing equivariant neural networks for continuous groups require discr...
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06/05/2021
Accelerating Stochastic Simulation with Interactive Neural Processes
Stochastic simulations such as large-scale, spatiotemporal, age-structur...
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05/25/2021
Quantifying Uncertainty in Deep Spatiotemporal Forecasting
Deep learning is gaining increasing popularity for spatiotemporal foreca...
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04/12/2021
Traffic Forecasting using Vehicle-to-Vehicle Communication
We take the first step in using vehicle-to-vehicle (V2V) communication t...
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02/22/2021
Generator Surgery for Compressed Sensing
Image recovery from compressive measurements requires a signal prior for...
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02/20/2021
Meta-Learning Dynamics Forecasting Using Task Inference
Current deep learning models for dynamics forecasting struggle with gene...
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02/12/2021
DeepGLEAM: a hybrid mechanistic and deep learning model for COVID-19 forecasting
We introduce DeepGLEAM, a hybrid model for COVID-19 forecasting. DeepGLE...
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11/20/2020
Bridging Physics-based and Data-driven modeling for Learning Dynamical Systems
How can we learn a dynamical system to make forecasts, when some variabl...
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10/21/2020
Trajectory Prediction using Equivariant Continuous Convolution
Trajectory prediction is a critical part of many AI applications, for ex...
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10/11/2020
Deep Imitation Learning for Bimanual Robotic Manipulation
We present a deep imitation learning framework for robotic bimanual mani...
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06/23/2020
Learning Disentangled Representations of Video with Missing Data
Missing data poses significant challenges while learning representations...
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06/21/2020
Finding Patient Zero: Learning Contagion Source with Graph Neural Networks
Locating the source of an epidemic, or patient zero (P0), can provide cr...
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05/12/2020
Aortic Pressure Forecasting with Deep Sequence Learning
Mean aortic pressure is a major determinant of perfusion in all organ sy...
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02/13/2020
Multiresolution Tensor Learning for Efficient and Interpretable Spatial Analysis
Efficient and interpretable spatial analysis is crucial in many fields s...
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02/08/2020
Incorporating Symmetry into Deep Dynamics Models for Improved Generalization
Training machine learning models that can learn complex spatiotemporal d...
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11/20/2019
Towards Physics-informed Deep Learning for Turbulent Flow Prediction
While deep learning has shown tremendous success in a wide range of doma...
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07/11/2019
Understanding the Representation Power of Graph Neural Networks in Learning Graph Topology
To deepen our understanding of graph neural networks, we investigate the...
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01/30/2019
NAOMI: Non-Autoregressive Multiresolution Sequence Imputation
Missing value imputation is a fundamental problem in modeling spatiotemp...
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11/19/2018
Neural Lander: Stable Drone Landing Control using Learned Dynamics
Precise trajectory control near ground is difficult for multi-rotor dron...
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10/10/2018
Learning Tensor Latent Features
We study the problem of learning latent feature models (LFMs) for tensor...
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02/19/2018
Multi-resolution Tensor Learning for Large-Scale Spatial Data
High-dimensional tensor models are notoriously computationally expensive...
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10/31/2017
Tensor Regression Meets Gaussian Processes
Low-rank tensor regression, a new model class that learns high-order cor...
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07/06/2017
Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting
Spatiotemporal forecasting has various applications in neuroscience, cli...
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10/25/2016