1 citations · 3 across the 10 of their papers we have counts for
11 papers
Latent Denoising Improves Visual Alignment in Large Multimodal Models
Dhruv Parikh, Jacob Fein-Ashley, Rajgopal Kannan +1
Large Multimodal Models (LMMs) such as LLaVA are typically trained with an autoregressive language modeling objective, providing only indirect supervision to visual tokens. This of…
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…
Diffusion Models with Anisotropic Gaussian Splatting for Image Inpainting
Jacob Fein-Ashley, Benjamin Fein-Ashley
Image inpainting is a fundamental task in computer vision, aiming to restore missing or corrupted regions in images realistically. While recent deep learning approaches have signif…
Contextual Feedback Loops: Amplifying Deep Reasoning with Iterative Top-Down Feedback
Jacob Fein-Ashley, Rajgopal Kannan, Viktor Prasanna
Conventional deep networks rely on one-way backpropagation that overlooks reconciling high-level predictions with lower-level representations. We propose \emph{Contextual Feedback…
Studying the Effects of Self-Attention on SAR Automatic Target Recognition
Jacob Fein-Ashley, Rajgopal Kannan, Viktor Prasanna
Attention mechanisms are critically important in the advancement of synthetic aperture radar (SAR) automatic target recognition (ATR) systems. Traditional SAR ATR models often stru…
A Single Graph Convolution Is All You Need: Efficient Grayscale Image Classification
Jacob Fein-Ashley, Sachini Wickramasinghe, Bingyi Zhang +2
Image classifiers for domain-specific tasks like Synthetic Aperture Radar Automatic Target Recognition (SAR ATR) and chest X-ray classification often rely on convolutional neural n…
Benchmarking Deep Learning Classifiers for SAR Automatic Target Recognition
Jacob Fein-Ashley, Tian Ye, Rajgopal Kannan +2
Synthetic Aperture Radar SAR Automatic Target Recognition ATR is a key technique of remote-sensing image recognition which can be supported by deep neural networks The existing wor…