Search Results for author:
Found 9 papers, 2 papers with code
Vision-Language Models as a Source of Rewards
Building generalist agents that can accomplish many goals in rich open-ended environments is one of the research frontiers for reinforcement learning.
In-context Reinforcement Learning with Algorithm Distillation
We propose Algorithm Distillation (AD), a method for distilling reinforcement learning (RL) algorithms into neural networks by modeling their training histories with a causal sequence model.
A*Net: A Scalable Path-based Reasoning Approach for Knowledge Graphs
Experiments on both transductive and inductive knowledge graph reasoning benchmarks show that A*Net achieves competitive performance with existing state-of-the-art path-based methods, while merely visiting 10% nodes and 10% edges at each iteration.
Ranked #10 on
Link Property Prediction
on ogbl-wikikg2
Higher Order Generalization Error for First Order Discretization of Langevin Diffusion
We propose a novel approach to analyze generalization error for discretizations of Langevin diffusion, such as the stochastic gradient Langevin dynamics (SGLD).
An Empirical Study of Large-Batch Stochastic Gradient Descent with Structured Covariance Noise
We demonstrate that the learning performance of our method is more accurately captured by the structure of the covariance matrix of the noise rather than by the variance of gradients.
A general system of differential equations to model first order adaptive algorithms
In this paper, we derive a non-autonomous system of differential equations, which is the continuous time limit of adaptive optimization methods.
Exploring Curvature Noise in Large-Batch Stochastic Optimization
Unfortunately, a major drawback is the so-called generalization gap: large-batch training typically leads to a degradation in generalization performance of the model as compared to small-batch training.
Scalable Recommender Systems through Recursive Evidence Chains
Recommender systems can be formulated as a matrix completion problem, predicting ratings from user and item parameter vectors.
Implicit Manifold Learning on Generative Adversarial Networks
This paper raises an implicit manifold learning perspective in Generative Adversarial Networks (GANs), by studying how the support of the learned distribution, modelled as a submanifold $\mathcal{M}_{\theta}$, perfectly match with $\mathcal{M}_{r}$, the support of the real data distribution.
