Search Results for author:
Found 20 papers, 8 papers with code
Ideal Abstractions for Decision-Focused Learning
We present a methodology for formulating simplifying abstractions in machine learning systems by identifying and harnessing the utility structure of decisions.
Hyena Hierarchy: Towards Larger Convolutional Language Models
Recent advances in deep learning have relied heavily on the use of large Transformers due to their ability to learn at scale.
Ranked #26 on
Language Modelling
on WikiText-103
Deep Latent State Space Models for Time-Series Generation
Methods based on ordinary differential equations (ODEs) are widely used to build generative models of time-series.
Transform Once: Efficient Operator Learning in Frequency Domain
Instead, this work introduces a blueprint for frequency domain learning through a single transform: transform once (T1).
Self-Similarity Priors: Neural Collages as Differentiable Fractal Representations
We investigate how to leverage the representations produced by Neural Collages in various tasks, including data compression and generation.
Neural Solvers for Fast and Accurate Numerical Optimal Control
Synthesizing optimal controllers for dynamical systems often involves solving optimization problems with hard real-time constraints.
Continuous-Depth Neural Models for Dynamic Graph Prediction
We introduce the framework of continuous-depth graph neural networks (GNNs).
Neural Hybrid Automata: Learning Dynamics with Multiple Modes and Stochastic Transitions
Effective control and prediction of dynamical systems often require appropriate handling of continuous-time and discrete, event-triggered processes.
Learning Stochastic Optimal Policies via Gradient Descent
We systematically develop a learning-based treatment of stochastic optimal control (SOC), relying on direct optimization of parametric control policies.
Optimal Energy Shaping via Neural Approximators
We introduce optimal energy shaping as an enhancement of classical passivity-based control methods.
Neural Ordinary Differential Equations for Intervention Modeling
By interpreting the forward dynamics of the latent representation of neural networks as an ordinary differential equation, Neural Ordinary Differential Equation (Neural ODE) emerged as an effective framework for modeling a system dynamics in the continuous time domain.
TorchDyn: A Neural Differential Equations Library
Continuous-depth learning has recently emerged as a novel perspective on deep learning, improving performance in tasks related to dynamical systems and density estimation.
Hypersolvers: Toward Fast Continuous-Depth Models
The infinite-depth paradigm pioneered by Neural ODEs has launched a renaissance in the search for novel dynamical system-inspired deep learning primitives; however, their utilization in problems of non-trivial size has often proved impossible due to poor computational scalability.
Stable Neural Flows
We introduce a provably stable variant of neural ordinary differential equations (neural ODEs) whose trajectories evolve on an energy functional parametrised by a neural network.
Port-Hamiltonian Gradient Flows
In this paper we present a general framework for continuous--time gradient descent, often referred to as gradient flow.
Neural Ordinary Differential Equation Value Networks for Parametrized Action Spaces
Action spaces equipped with parameter sets are a common occurrence in reinforcement learning applications.
Hierarchical Reinforcement Learning
reinforcement-learning
+1
Dissecting Neural ODEs
Continuous deep learning architectures have recently re-emerged as Neural Ordinary Differential Equations (Neural ODEs).
Graph Neural Ordinary Differential Equations
We introduce the framework of continuous--depth graph neural networks (GNNs).
Port-Hamiltonian Approach to Neural Network Training
Neural networks are discrete entities: subdivided into discrete layers and parametrized by weights which are iteratively optimized via difference equations.
