b'Rose Yu'

research

∙ 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...

8 Sungduk Yu, et al. ∙

research

∙ 05/07/2023

Disentangled Multi-Fidelity Deep Bayesian Active Learning

To balance quality and cost, various domain areas of science and enginee...

0 Dongxia Wu, et al. ∙

research

∙ 04/17/2023

Long-term Forecasting with TiDE: Time-series Dense Encoder

Recent work has shown that simple linear models can outperform several T...

0 Abhimanyu Das, et al. ∙

research

∙ 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...

0 Alejandro Rodriguez Pascual, et al. ∙

research

∙ 02/01/2023

Generative Adversarial Symmetry Discovery

Despite the success of equivariant neural networks in scientific applica...

0 Jianke Yang, et al. ∙

research

∙ 01/27/2023

On the Connection Between MPNN and Graph Transformer

Graph Transformer (GT) recently has emerged as a new paradigm of graph l...

0 Chen Cai, et al. ∙

research

∙ 12/06/2022

Copula Conformal Prediction for Multi-step Time Series Forecasting

Accurate uncertainty measurement is a key step to building robust and re...

0 Sophia Sun, et al. ∙

research

∙ 06/17/2022

LIMO: Latent Inceptionism for Targeted Molecule Generation

Generation of drug-like molecules with high binding affinity to target p...

19 Peter Eckmann, et al. ∙

research

∙ 06/10/2022

Multi-fidelity Hierarchical Neural Processes

Science and engineering fields use computer simulation extensively. Thes...

0 Dongxia Wu, et al. ∙

research

∙ 05/26/2022

Faster Optimization on Sparse Graphs via Neural Reparametrization

In mathematical optimization, second-order Newton's methods generally co...

0 Nima Dehmamy, et al. ∙

research

∙ 05/21/2022

Symmetry Teleportation for Accelerated Optimization

Existing gradient-based optimization methods update the parameters local...

11 Bo Zhao, et al. ∙

research

∙ 05/04/2022

Probabilistic Symmetry for Improved Trajectory Forecasting

Trajectory prediction is a core AI problem with broad applications in ro...

0 Sophia Sun, et al. ∙

research

∙ 03/31/2022

SELFIES and the future of molecular string representations

Artificial intelligence (AI) and machine learning (ML) are expanding in ...

0 Mario Krenn, et al. ∙

research

∙ 02/27/2022

Taming the Long Tail of Deep Probabilistic Forecasting

Deep probabilistic forecasting is gaining attention in numerous applicat...

0 Jedrzej Kozerawski, et al. ∙

research

∙ 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 ...

0 Ayan Chatterjee, et al. ∙

research

∙ 12/12/2021

Neural Point Process for Learning Spatiotemporal Event Dynamics

Learning the dynamics of spatiotemporal events is a fundamental problem....

0 ZiHao Zhou, et al. ∙

research

∙ 09/15/2021

Automatic Symmetry Discovery with Lie Algebra Convolutional Network

Existing equivariant neural networks for continuous groups require discr...

0 Nima Dehmamy, et al. ∙

research

∙ 06/05/2021

Accelerating Stochastic Simulation with Interactive Neural Processes

Stochastic simulations such as large-scale, spatiotemporal, age-structur...

0 Dongxia Wu, et al. ∙

research

∙ 05/25/2021

Quantifying Uncertainty in Deep Spatiotemporal Forecasting

Deep learning is gaining increasing popularity for spatiotemporal foreca...

0 Dongxia Wu, et al. ∙

research

∙ 04/12/2021

Traffic Forecasting using Vehicle-to-Vehicle Communication

We take the first step in using vehicle-to-vehicle (V2V) communication t...

0 Steven Wong, et al. ∙

research

∙ 02/22/2021

Generator Surgery for Compressed Sensing

Image recovery from compressive measurements requires a signal prior for...

8 Niklas Smedemark-Margulies, et al. ∙

research

∙ 02/20/2021

Meta-Learning Dynamics Forecasting Using Task Inference

Current deep learning models for dynamics forecasting struggle with gene...

0 Rui Wang, et al. ∙

research

∙ 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...

0 Dongxia Wu, et al. ∙

research

∙ 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...

3 Rui Wang, et al. ∙

research

∙ 10/21/2020

Trajectory Prediction using Equivariant Continuous Convolution

Trajectory prediction is a critical part of many AI applications, for ex...

0 Robin Walters, et al. ∙

research

∙ 10/11/2020

Deep Imitation Learning for Bimanual Robotic Manipulation

We present a deep imitation learning framework for robotic bimanual mani...

0 Fan Xie, et al. ∙

research

∙ 06/23/2020

Learning Disentangled Representations of Video with Missing Data

Missing data poses significant challenges while learning representations...

16 Armand Comas Massague, et al. ∙

research

∙ 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...

0 Chintan Shah, et al. ∙

research

∙ 05/12/2020

Aortic Pressure Forecasting with Deep Sequence Learning

Mean aortic pressure is a major determinant of perfusion in all organ sy...

0 Rui Wang, et al. ∙

research

∙ 02/13/2020

Multiresolution Tensor Learning for Efficient and Interpretable Spatial Analysis

Efficient and interpretable spatial analysis is crucial in many fields s...

58 Jung Yeon Park, et al. ∙

research

∙ 02/08/2020

Incorporating Symmetry into Deep Dynamics Models for Improved Generalization

Training machine learning models that can learn complex spatiotemporal d...

0 Rui Wang, et al. ∙

research

∙ 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...

8 Rui Wang, et al. ∙

research

∙ 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...

0 Nima Dehmamy, et al. ∙

research

∙ 01/30/2019

NAOMI: Non-Autoregressive Multiresolution Sequence Imputation

Missing value imputation is a fundamental problem in modeling spatiotemp...

38 Yukai Liu, et al. ∙

research

∙ 11/19/2018

Neural Lander: Stable Drone Landing Control using Learned Dynamics

Precise trajectory control near ground is difficult for multi-rotor dron...

0 Guanya Shi, et al. ∙

research

∙ 10/10/2018

Learning Tensor Latent Features

We study the problem of learning latent feature models (LFMs) for tensor...

0 Sung-En Chang, et al. ∙

research

∙ 02/19/2018

Multi-resolution Tensor Learning for Large-Scale Spatial Data

High-dimensional tensor models are notoriously computationally expensive...

0 Stephan Zheng, et al. ∙

research

∙ 10/31/2017

Tensor Regression Meets Gaussian Processes

Low-rank tensor regression, a new model class that learns high-order cor...

0 Rose Yu, et al. ∙

research

∙ 07/06/2017

Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting

Spatiotemporal forecasting has various applications in neuroscience, cli...

0 Yaguang Li, et al. ∙

research

∙ 10/25/2016

Socratic Learning: Augmenting Generative Models to Incorporate Latent Subsets in Training Data

A challenge in training discriminative models like neural networks is ob...

0 Paroma Varma, et al. ∙

Rose Yu

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