47 citations · 161 across the 15 of their papers we have counts for
7 papers · 1 filter
Dense Associative Memory Through the Lens of Random Features
Benjamin Hoover, Duen Horng Chau, Hendrik Strobelt +2
Dense Associative Memories are high storage capacity variants of the Hopfield networks that are capable of storing a large number of memory patterns in the weights of the network o…
Transformer Explainer: Learning LLM Transformers with Interactive Visual Explanation and Experimentation
Aeree Cho, Grace C. Kim, Alexander Karpekov +6
The Transformer architecture underpins modern large language models powering state-of-the-art text generation and AI applications. However, its complexity makes it difficult for no…
Memory in Plain Sight: Surveying the Uncanny Resemblances of Associative Memories and Diffusion Models
Benjamin Hoover, Hendrik Strobelt, Dmitry Krotov +3
The generative process of Diffusion Models (DMs) has recently set state-of-the-art on many AI generation benchmarks. Though the generative process is traditionally understood as an…
Energy Transformer
Benjamin Hoover, Yuchen Liang, Bao Pham +5
Our work combines aspects of three promising paradigms in machine learning, namely, attention mechanism, energy-based models, and associative memory. Attention is the power-house d…
Concept Evolution in Deep Learning Training: A Unified Interpretation Framework and Discoveries
Haekyu Park, Seongmin Lee, Benjamin Hoover +7
We present ConceptEvo, a unified interpretation framework for deep neural networks (DNNs) that reveals the inception and evolution of learned concepts during training. Our work add…
Shared Interest: Measuring Human-AI Alignment to Identify Recurring Patterns in Model Behavior
Angie Boggust, Benjamin Hoover, Arvind Satyanarayan +1
Saliency methods -- techniques to identify the importance of input features on a model's output -- are a common step in understanding neural network behavior. However, interpreting…
CogMol: Target-Specific and Selective Drug Design for COVID-19 Using Deep Generative Models
Vijil Chenthamarakshan, Payel Das, Samuel C. Hoffman +8
The novel nature of SARS-CoV-2 calls for the development of efficient de novo drug design approaches. In this study, we propose an end-to-end framework, named CogMol (Controlled Ge…