research
∙
06/27/2023
HyenaDNA: Long-Range Genomic Sequence Modeling at Single Nucleotide Resolution
Genomic (DNA) sequences encode an enormous amount of information for gen...
share
research
∙
03/29/2023
Ideal Abstractions for Decision-Focused Learning
We present a methodology for formulating simplifying abstractions in mac...
research
∙
03/16/2023
Effectively Modeling Time Series with Simple Discrete State Spaces
Time series modeling is a well-established problem, which often requires...
research
∙
02/21/2023
Hyena Hierarchy: Towards Larger Convolutional Language Models
Recent advances in deep learning have relied heavily on the use of large...
research
∙
12/24/2022
Deep Latent State Space Models for Time-Series Generation
Methods based on ordinary differential equations (ODEs) are widely used ...
research
∙
11/26/2022
Transform Once: Efficient Operator Learning in Frequency Domain
Spectral analysis provides one of the most effective paradigms for infor...
research
∙
04/15/2022
Self-Similarity Priors: Neural Collages as Differentiable Fractal Representations
Many patterns in nature exhibit self-similarity: they can be compactly d...
research
∙
03/13/2022
Neural Solvers for Fast and Accurate Numerical Optimal Control
Synthesizing optimal controllers for dynamical systems often involves so...
research
∙
10/06/2021
Which Shortcut Cues Will DNNs Choose? A Study from the Parameter-Space Perspective
Deep neural networks (DNNs) often rely on easy-to-learn discriminatory f...
research
∙
06/22/2021
Continuous-Depth Neural Models for Dynamic Graph Prediction
We introduce the framework of continuous-depth graph neural networks (GN...
research
∙
06/08/2021
Neural Hybrid Automata: Learning Dynamics with Multiple Modes and Stochastic Transitions
Effective control and prediction of dynamical systems often require appr...
research
∙
06/07/2021
Differentiable Multiple Shooting Layers
We detail a novel class of implicit neural models. Leveraging time-paral...
research
∙
06/07/2021
Learning Stochastic Optimal Policies via Gradient Descent
We systematically develop a learning-based treatment of stochastic optim...
research
∙
01/14/2021
Optimal Energy Shaping via Neural Approximators
We introduce optimal energy shaping as an enhancement of classical passi...
research
∙
10/16/2020
Neural Ordinary Differential Equations for Intervention Modeling
By interpreting the forward dynamics of the latent representation of neu...
research
∙
09/20/2020
TorchDyn: A Neural Differential Equations Library
Continuous-depth learning has recently emerged as a novel perspective on...
research
∙
07/19/2020
Hypersolvers: Toward Fast Continuous-Depth Models
The infinite-depth paradigm pioneered by Neural ODEs has launched a rena...
research
∙
03/18/2020
Stable Neural Flows
We introduce a provably stable variant of neural ordinary differential e...
research
∙
02/19/2020
Dissecting Neural ODEs
Continuous deep learning architectures have recently re-emerged as varia...
research
∙
11/18/2019
Graph Neural Ordinary Differential Equations
We extend the framework of graph neural networks (GNN) to continuous tim...
research
∙
09/24/2019
WATTNet: Learning to Trade FX via Hierarchical Spatio-Temporal Representation of Highly Multivariate Time Series
Finance is a particularly challenging application area for deep learning...
research
∙
09/06/2019
