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Found 8 papers, 6 papers with code
Investigating Continual Pretraining in Large Language Models: Insights and Implications
This paper studies the evolving domain of Continual Learning (CL) in large language models (LLMs), with a focus on developing strategies for efficient and sustainable training.
Infinite dSprites for Disentangled Continual Learning: Separating Memory Edits from Generalization
In a simple setting with direct supervision on the generative factors, we show how learning class-agnostic transformations offers a way to circumvent catastrophic forgetting and improve classification accuracy over time.
Modulated Neural ODEs
Neural ordinary differential equations (NODEs) have been proven useful for learning non-linear dynamics of arbitrary trajectories.
Learning Interacting Dynamical Systems with Latent Gaussian Process ODEs
We study time uncertainty-aware modeling of continuous-time dynamics of interacting objects.
Variational multiple shooting for Bayesian ODEs with Gaussian processes
Recent machine learning advances have proposed black-box estimation of unknown continuous-time system dynamics directly from data.
Continuous-Time Model-Based Reinforcement Learning
Model-based reinforcement learning (MBRL) approaches rely on discrete-time state transition models whereas physical systems and the vast majority of control tasks operate in continuous-time.
Model-based Reinforcement Learning
reinforcement-learning
+1
ODE$^2$VAE: Deep generative second order ODEs with Bayesian neural networks
We present Ordinary Differential Equation Variational Auto-Encoder (ODE$^2$VAE), a latent second order ODE model for high-dimensional sequential data.
Ranked #1 on
Video Prediction
on CMU Mocap-1
Asynchronous Stochastic Quasi-Newton MCMC for Non-Convex Optimization
The results support our theory and show that the proposed algorithm provides a significant speedup over the recently proposed synchronous distributed L-BFGS algorithm.
