2 citations · 2 across the 8 of their papers we have counts for
8 papers
Reinforcement Learning from Rich Feedback with Distributional DAgger
Rishabh Agrawal, Jacob Fein-Ashley, Paria Rashidinejad
Reasoning models have advanced rapidly, but the dominant reinforcement learning from verifiable rewards (RLVR) recipe remains surprisingly narrow: sample many responses and reward…
Solve the Loop: Attractor Models for Language and Reasoning
Jacob Fein-Ashley, Paria Rashidinejad
Looped Transformers offer a promising alternative to purely feed-forward computation by iteratively refining latent representations, improving language modeling and reasoning. Yet…
Bridging Hidden States in Vision-Language Models
Benjamin Fein-Ashley, Jacob Fein-Ashley
Vision-Language Models (VLMs) are a new family of models that align image content with natural language. Existing approaches typically fuse either (a) early: by mixing tokens/featu…
Flowing Through Layers: A Continuous Dynamical Systems Perspective on Transformers
Jacob Fein-Ashley
We show that the standard discrete update rule of transformer layers can be naturally interpreted as a forward Euler discretization of a continuous dynamical system. Our Transforme…
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…
HVT: A Comprehensive Vision Framework for Learning in Non-Euclidean Space
Jacob Fein-Ashley, Ethan Feng, Minh Pham
Data representation in non-Euclidean spaces has proven effective for capturing hierarchical and complex relationships in real-world datasets. Hyperbolic spaces, in particular, prov…
A Comparison of Traditional and Deep Learning Methods for Parameter Estimation of the Ornstein-Uhlenbeck Process
Jacob Fein-Ashley
We consider the Ornstein-Uhlenbeck (OU) process, a stochastic process widely used in finance, physics, and biology. Parameter estimation of the OU process is a challenging problem.…