BAIR Blog

Gradient-based Planning for World Models at Longer Horizons

.grasp-results-table table { font-size: 0.875rem; line-height: 1.35; width: 100%; } .grasp-results-table th, .grasp-results-table td { padding: 0.35rem 0.5rem; } /* Consistent whitespace between major sections (this post is long and hr-heavy) */ article.post-content h2 { margin-top: 2.75rem; margin-bottom: 0.75rem; } article.post-content h2:first-of-type { margin-top: 2.25rem; } article.post-content h3 { margin-top: 1.65rem; margin-bottom: 0.5rem; } article.post-content hr { margin-top: 2.5rem;…

BAIR Blog

Lire

Identifying Interactions at Scale for LLMs

--> Understanding the behavior of complex machine learning systems, particularly Large Language Models (LLMs), is a critical challenge in modern artificial intelligence. Interpretability research aims to make the decision-making process more transparent to model builders and impacted humans, a step toward safer and more trustworthy AI. To gain a comprehensive understanding, we can analyze these systems through different lenses: feature attribution, which isolates the specific input features…

BAIR Blog

Lire

Information-Driven Design of Imaging Systems

An encoder (optical system) maps objects to noiseless images, which noise corrupts into measurements. Our information estimator uses only these noisy measurements and a noise model to quantify how well measurements distinguish objects. Many imaging systems produce measurements that humans never see or cannot interpret directly. Your smartphone processes raw sensor data through algorithms before producing the final photo. MRI scanners collect frequency-space measurements that require…

BAIR Blog

Lire