8 citations · 10 across the 9 of their papers we have counts for
10 papers
From the Loss Landscape to Diverse Feature Learning in Neural Networks
David Aram Yunis
Over the course of the last decade, neural networks have grown from an academic curiosity to moving the markets of nations. Despite this explosion in both research and deployment,…
Teaching an Agent to Sketch One Part at a Time
Xiaodan Du, Ruize Xu, David Yunis +2
We develop a method for producing vector sketches one part at a time. To do this, we train a multi-modal language model-based agent using a novel multi-turn process-reward reinforc…
Context-Efficient Retrieval with Factual Decomposition
Yanhong Li, David Yunis, David McAllester +1
There has recently been considerable interest in incorporating information retrieval into large language models (LLMs). Retrieval from a dynamically expanding external corpus of te…
Reducing the Scope of Language Models
David Yunis, Siyu Huo, Chulaka Gunasekara +1
Large language models (LLMs) are deployed in a wide variety of user-facing applications. Typically, these deployments have some specific purpose, like answering questions grounded…
Approaching Deep Learning through the Spectral Dynamics of Weights
David Yunis, Kumar Kshitij Patel, Samuel Wheeler +5
We propose an empirical approach centered on the spectral dynamics of weights -- the behavior of singular values and vectors during optimization -- to unify and clarify several phe…
Deciphering 'What' and 'Where' Visual Pathways from Spectral Clustering of Layer-Distributed Neural Representations
Xiao Zhang, David Yunis, Michael Maire
We present an approach for analyzing grouping information contained within a neural network's activations, permitting extraction of spatial layout and semantic segmentation from th…
Subwords as Skills: Tokenization for Sparse-Reward Reinforcement Learning
David Yunis, Justin Jung, Falcon Dai +1
Exploration in sparse-reward reinforcement learning is difficult due to the requirement of long, coordinated sequences of actions in order to achieve any reward. Moreover, in conti…
Statler: State-Maintaining Language Models for Embodied Reasoning
Takuma Yoneda, Jiading Fang, Peng Li +7
There has been a significant research interest in employing large language models to empower intelligent robots with complex reasoning. Existing work focuses on harnessing their ab…
Jointly Learning to Construct and Control Agents using Deep Reinforcement Learning
Charles Schaff, David Yunis, Ayan Chakrabarti +1
The physical design of a robot and the policy that controls its motion are inherently coupled, and should be determined according to the task and environment. In an increasing numb…
Jointly Optimizing Placement and Inference for Beacon-based Localization
Charles Schaff, David Yunis, Ayan Chakrabarti +1
The ability of robots to estimate their location is crucial for a wide variety of autonomous operations. In settings where GPS is unavailable, measurements of transmissions from fi…