5 citations · 8 across the 8 of their papers we have counts for
4 papers · 1 filter
Parallel Stochastic Gradient-Based Planning for World Models
Michael Psenka, Michael Rabbat, Aditi Krishnapriyan +2
World models simulate environment dynamics from raw sensory inputs like video. However, using them for planning can be challenging due to the vast and unstructured search space. We…
Gaussian Embeddings: How JEPAs Secretly Learn Your Data Density
Randall Balestriero, Nicolas Ballas, Mike Rabbat +1
Joint Embedding Predictive Architectures (JEPAs) learn representations able to solve numerous downstream tasks out-of-the-box. JEPAs combine two objectives: (i) a latent-space pred…
Accelerating Neural Network Training: An Analysis of the AlgoPerf Competition
Priya Kasimbeg, Frank Schneider, Runa Eschenhagen +11
The goal of the AlgoPerf: Training Algorithms competition is to evaluate practical speed-ups in neural network training achieved solely by improving the underlying training algorit…
Towards General-Purpose Model-Free Reinforcement Learning
Scott Fujimoto, Pierluca D'Oro, Amy Zhang +2
Reinforcement learning (RL) promises a framework for near-universal problem-solving. In practice however, RL algorithms are often tailored to specific benchmarks, relying on carefu…