310 citations · 1.9k across the 36 of their papers we have counts for
35 papers
Sparse Prototype Code Underlies Classification and Prediction Across Modalities
Yehonatan Avidan, Daniel D. Lee, Haim Sompolinsky
Neural representations have become a central tool for studying the internal mechanisms of modern AI models, yet their complex high-dimensional structure makes them difficult to int…
Diffusion-Inspired Masked Fine-Tuning for Knowledge Injection in Autoregressive LLMs
Xu Pan, Ely Hahami, Jingxuan Fan +2
Large language models (LLMs) are often used in environments where facts evolve, yet factual knowledge updates via fine-tuning on unstructured text often suffer from 1) reliance on…
Memorization and Knowledge Injection in Gated LLMs
Xu Pan, Ely Hahami, Zechen Zhang +1
Large Language Models (LLMs) currently struggle to sequentially add new memories and integrate new knowledge. These limitations contrast with the human ability to continuously lear…
Unraveling the geometry of visual relational reasoning
Jiaqi Shang, Gabriel Kreiman, Haim Sompolinsky
Humans readily generalize abstract relations, such as recognizing "constant" in shape or color, whereas neural networks struggle, limiting their flexible reasoning. To investigate…
Simplified derivations for high-dimensional convex learning problems
David G. Clark, Haim Sompolinsky
Statistical-physics calculations in machine learning and theoretical neuroscience often involve lengthy derivations that obscure physical interpretation. Here, we give concise, non…
Diverse capability and scaling of diffusion and auto-regressive models when learning abstract rules
Binxu Wang, Jiaqi Shang, Haim Sompolinsky
Humans excel at discovering regular structures from limited samples and applying inferred rules to novel settings. We investigate whether modern generative models can similarly lea…