Search Results for author: Rose Yu

Found 45 papers, 18 papers with code

Data Augmentation vs. Equivariant Networks: A Theory of Generalization on Dynamics Forecasting

no code implementations19 Jun 2022 Rui Wang, Robin Walters, Rose Yu

In this work, we derive the generalization bounds for data augmentation and equivariant networks, characterizing their effect on learning in a unified framework.

Data Augmentation Generalization Bounds

LIMO: Latent Inceptionism for Targeted Molecule Generation

1 code implementation17 Jun 2022 Peter Eckmann, Kunyang Sun, Bo Zhao, Mudong Feng, Michael K. Gilson, Rose Yu

We corroborate these docking-based results with more accurate molecular dynamics-based calculations of absolute binding free energy and show that one of our generated drug-like compounds has a predicted $K_D$ (a measure of binding affinity) of $6 \cdot 10^{-14}$ M against the human estrogen receptor, well beyond the affinities of typical early-stage drug candidates and most FDA-approved drugs to their respective targets.

Drug Discovery Gaussian Processes

Multi-fidelity Hierarchical Neural Processes

no code implementations10 Jun 2022 Dongxia Wu, Matteo Chinazzi, Alessandro Vespignani, Yi-An Ma, Rose Yu

MF-HNP is flexible enough to handle non-nested high dimensional data at different fidelity levels with varying input and output dimensions.

Epidemiology Gaussian Processes

Faster Optimization on Sparse Graphs via Neural Reparametrization

no code implementations26 May 2022 Nima Dehmamy, Csaba Both, Jianzhi Long, Rose Yu

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 computationally expensive.

Symmetry Teleportation for Accelerated Optimization

no code implementations21 May 2022 Bo Zhao, Nima Dehmamy, Robin Walters, Rose Yu

Existing gradient-based optimization methods update the parameters locally, in a direction that minimizes the loss function.

Second-order methods

Probabilistic Symmetry for Improved Trajectory Forecasting

no code implementations4 May 2022 Sophia Sun, Robin Walters, Jinxi Li, Rose Yu

Trajectory prediction is a core AI problem with broad applications in robotics and autonomous driving.

Autonomous Driving Decision Making +2

Taming the Long Tail of Deep Probabilistic Forecasting

no code implementations27 Feb 2022 Jedrzej Kozerawski, Mayank Sharan, Rose Yu

We present two moment-based tailedness measurement concepts to improve performance on the difficult tail examples: Pareto Loss and Kurtosis Loss.

Time Series Trajectory Prediction

Approximately Equivariant Networks for Imperfectly Symmetric Dynamics

1 code implementation28 Jan 2022 Rui Wang, Robin Walters, Rose Yu

Incorporating symmetry as an inductive bias into neural network architecture has led to improvements in generalization, data efficiency, and physical consistency in dynamics modeling.

Inductive Bias

Neural Point Process for Learning Spatiotemporal Event Dynamics

1 code implementation12 Dec 2021 ZiHao Zhou, Xingyi Yang, Ryan Rossi, Handong Zhao, Rose Yu

The key construction of our approach is the nonparametric space-time intensity function, governed by a latent process.

Point Processes Variational Inference

Accelerating Optimization using Neural Reparametrization

no code implementations29 Sep 2021 Nima Dehmamy, Csaba Both, Jianzhi Long, Rose Yu

We tackle the problem of accelerating certain optimization problems related to steady states in ODE and energy minimization problems common in physics.

Automatic Symmetry Discovery with Lie Algebra Convolutional Network

1 code implementation NeurIPS 2021 Nima Dehmamy, Robin Walters, Yanchen Liu, Dashun Wang, Rose Yu

Existing equivariant neural networks require prior knowledge of the symmetry group and discretization for continuous groups.

Physics-Guided Deep Learning for Dynamical Systems: A Survey

no code implementations2 Jul 2021 Rui Wang, Rose Yu

Modeling complex physical dynamics is a fundamental task in science and engineering.

Accelerating Stochastic Simulation with Spatiotemporal Neural Processes

no code implementations5 Jun 2021 Dongxia Wu, Matteo Chinazzi, Alessandro Vespignani, Yi-An Ma, Rose Yu

We propose Spatiotemporal Neural Processes (STNP), a neural latent variable model to mimic the spatiotemporal dynamics of stochastic simulators.

Active Learning

Traffic Forecasting using Vehicle-to-Vehicle Communication

1 code implementation12 Apr 2021 Steven Wong, Lejun Jiang, Robin Walters, Tamás G. Molnár, Gábor Orosz, Rose Yu

In order to best utilize real-world V2V communication data, we integrate first principle models with deep learning.

Generator Surgery for Compressed Sensing

no code implementations22 Feb 2021 Niklas Smedemark-Margulies, Jung Yeon Park, Max Daniels, Rose Yu, Jan-Willem van de Meent, Paul Hand

We introduce a method for achieving low representation error using generators as signal priors.

Meta-Learning Dynamics Forecasting Using Task Inference

no code implementations20 Feb 2021 Rui Wang, Robin Walters, Rose Yu

DyAd has two parts: an encoder which infers the time-invariant hidden features of the task with weak supervision, and a forecaster which learns the shared dynamics of the entire domain.


Lie Algebra Convolutional Neural Networks with Automatic Symmetry Extraction

no code implementations1 Jan 2021 Nima Dehmamy, Yanchen Liu, Robin Walters, Rose Yu

We propose to learn the symmetries during the training of the group equivariant architectures.

Neural Point Process for Forecasting Spatiotemporal Events

no code implementations1 Jan 2021 ZiHao Zhou, Xingyi Yang, Xinyi He, Ryan Rossi, Handong Zhao, Rose Yu

To the best of our knowledge, this is the first neural point process model that can jointly predict both the space and time of events.

Density Estimation Point Processes

Bridging Physics-based and Data-driven modeling for Learning Dynamical Systems

3 code implementations20 Nov 2020 Rui Wang, Danielle Maddix, Christos Faloutsos, Yuyang Wang, Rose Yu

While much research on distribution shift has focused on changes in the data domain, our work calls attention to rethink generalization for learning dynamical systems.

Trajectory Prediction using Equivariant Continuous Convolution

no code implementations ICLR 2021 Robin Walters, Jinxi Li, Rose Yu

Trajectory prediction is a critical part of many AI applications, for example, the safe operation of autonomous vehicles.

Autonomous Vehicles Trajectory Prediction

Deep Imitation Learning for Bimanual Robotic Manipulation

1 code implementation NeurIPS 2020 Fan Xie, Alexander Chowdhury, M. Clara De Paolis Kaluza, Linfeng Zhao, Lawson L. S. Wong, Rose Yu

Compared to baselines, our model generalizes better and achieves higher success rates on several simulated bimanual robotic manipulation tasks.

Imitation Learning

Dynamic Relational Inference in Multi-Agent Trajectories

no code implementations16 Jul 2020 Ruichao Xiao, Manish Kumar Singh, Rose Yu

Neural relational inference (NRI) is a deep generative model that can reason about relations in complex dynamics without supervision.

Learning Disentangled Representations of Video with Missing Data

1 code implementation23 Jun 2020 Armand Comas-Massagué, Chi Zhang, Zlatan Feric, Octavia Camps, Rose Yu

Missing data poses significant challenges while learning representations of video sequences.

Finding Patient Zero: Learning Contagion Source with Graph Neural Networks

no code implementations21 Jun 2020 Chintan Shah, Nima Dehmamy, Nicola Perra, Matteo Chinazzi, Albert-László Barabási, Alessandro Vespignani, Rose Yu

% We observe that GNNs can identify P0 close to the theoretical bound on accuracy, without explicit input of dynamics or its parameters.

Aortic Pressure Forecasting with Deep Sequence Learning

no code implementations12 May 2020 Eliza Huang, Rui Wang, Uma Chandrasekaran, Rose Yu

The aim of this study was to forecast the mean aortic pressure five minutes in advance, using the 25 Hz time series data of previous five minutes as input.

Time Series

Multiresolution Tensor Learning for Efficient and Interpretable Spatial Analysis

1 code implementation ICML 2020 Jung Yeon Park, Kenneth Theo Carr, Stephan Zheng, Yisong Yue, Rose Yu

Efficient and interpretable spatial analysis is crucial in many fields such as geology, sports, and climate science.

Incorporating Symmetry into Deep Dynamics Models for Improved Generalization

no code implementations ICLR 2021 Rui Wang, Robin Walters, Rose Yu

Recent work has shown deep learning can accelerate the prediction of physical dynamics relative to numerical solvers.

Towards Physics-informed Deep Learning for Turbulent Flow Prediction

1 code implementation20 Nov 2019 Rui Wang, Karthik Kashinath, Mustafa Mustafa, Adrian Albert, Rose Yu

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, training, and inference of such models.

Neural Lander: Stable Drone Landing Control using Learned Dynamics

no code implementations19 Nov 2018 Guanya Shi, Xichen Shi, Michael O'Connell, Rose Yu, Kamyar Azizzadenesheli, Animashree Anandkumar, Yisong Yue, Soon-Jo Chung

To the best of our knowledge, this is the first DNN-based nonlinear feedback controller with stability guarantees that can utilize arbitrarily large neural nets.

Multi-resolution Tensor Learning for Large-Scale Spatial Data

no code implementations19 Feb 2018 Stephan Zheng, Rose Yu, Yisong Yue

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


Long-term Forecasting using Tensor-Train RNNs

no code implementations ICLR 2018 Rose Yu, Stephan Zheng, Anima Anandkumar, Yisong Yue

We present Tensor-Train RNN (TT-RNN), a novel family of neural sequence architectures for multivariate forecasting in environments with nonlinear dynamics.

Long-term Forecasting using Higher Order Tensor RNNs

1 code implementation ICLR 2018 Rose Yu, Stephan Zheng, Anima Anandkumar, Yisong Yue

We present Higher-Order Tensor RNN (HOT-RNN), a novel family of neural sequence architectures for multivariate forecasting in environments with nonlinear dynamics.

Time Series

Tensor Regression Meets Gaussian Processes

no code implementations31 Oct 2017 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.

Bayesian Inference Gaussian Processes

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

no code implementations25 Oct 2016 Paroma Varma, Bryan He, Dan Iter, Peng Xu, Rose Yu, Christopher De Sa, Christopher Ré

Prior work has explored learning accuracies for these sources even without ground truth labels, but they assume that a single accuracy parameter is sufficient to model the behavior of these sources over the entire training set.

Relation Extraction

Learning from Multiway Data: Simple and Efficient Tensor Regression

no code implementations8 Jul 2016 Rose Yu, Yan Liu

In this paper, we introduce subsampled tensor projected gradient to solve the problem.

Multi-Task Learning

A Survey on Social Media Anomaly Detection

no code implementations6 Jan 2016 Rose Yu, Huida Qiu, Zhen Wen, Ching-Yung Lin, Yan Liu

In this paper, we present a survey on existing approaches to address this problem.

Anomaly Detection

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