1 citations · 3 across the 10 of their papers we have counts for
5 papers · 1 filter
Mixture of Thoughts: Learning to Aggregate What Experts Think, Not Just What They Say
Jacob Fein-Ashley, Dhruv Parikh, Rajgopal Kannan +1
Open-source Large Language Models (LLMs) increasingly specialize by domain (e.g., math, code, general reasoning), motivating systems that leverage complementary strengths across mo…
Linear Diffusion Networks
Jacob Fein-Ashley
We present Linear Diffusion Networks (LDNs), a novel architecture that reinterprets sequential data processing as a unified diffusion process. Our model integrates adaptive diffusi…
Iterate to Accelerate: A Unified Framework for Iterative Reasoning and Feedback Convergence
Jacob Fein-Ashley
We introduce a unified framework for iterative reasoning that leverages non-Euclidean geometry via Bregman divergences, higher-order operator averaging, and adaptive feedback mecha…
SPECTRE: An FFT-Based Efficient Drop-In Replacement to Self-Attention for Long Contexts
Jacob Fein-Ashley, Neelesh Gupta, Rajgopal Kannan +1
Long-context transformers face significant efficiency challenges due to the quadratic cost of self-attention. However, many modern applications-from multi-turn dialogue to high-res…
ClusterViG: Efficient Globally Aware Vision GNNs via Image Partitioning
Dhruv Parikh, Jacob Fein-Ashley, Tian Ye +2
Convolutional Neural Networks (CNN) and Vision Transformers (ViT) have dominated the field of Computer Vision (CV). Graph Neural Networks (GNN) have performed remarkably well acros…