Browsing: Berkeley

Overview of adaptive parallel reasoning. What if a reasoning model could decide for itself when to decompose and parallelize independent…

GRASP is a new gradient-based planner for learned dynamics (a “world model”) that makes long-horizon planning practical by (1) lifting…

An encoder (optical system) maps objects to noiseless images, which noise corrupts into measurements. Our information estimator uses only these…

Understanding the behavior of complex machine learning systems, particularly Large Language Models (LLMs), is a critical challenge in modern artificial…

× Predicting Ego-centric Video from human Actions (PEVA). Given past video frames and an action specifying a desired change in…

What exactly does word2vec learn, and how? Answering this question amounts to understanding representation learning in a minimal yet interesting…

In this post, I’ll introduce a reinforcement learning (RL) algorithm based on an “alternative” paradigm: divide and conquer. Unlike traditional…

Every year, the Berkeley Artificial Intelligence Research (BAIR) Lab graduates some of the most talented and innovative minds in artificial…

As computer vision researchers, we believe that every pixel can tell a story. However, there seems to be a writer’s…

The ability of LLMs to execute commands through plain language (e.g. English) has enabled agentic systems that can complete a…