4 citations · 13 across the 32 of their papers we have counts for
5 papers · 1 filter
Perspectives on the State and Future of Deep Learning - 2023
Micah Goldblum, Anima Anandkumar, Richard Baraniuk +7
The goal of this series is to chronicle opinions and issues in the field of machine learning as they stand today and as they change over time. The plan is to host this survey perio…
Mitigating Over-smoothing in Transformers via Regularized Nonlocal Functionals
Tam Nguyen, Tan M. Nguyen, Richard G. Baraniuk
Transformers have achieved remarkable success in a wide range of natural language processing and computer vision applications. However, the representation capacity of a deep transf…
Training Dynamics of Deep Network Linear Regions
Ahmed Imtiaz Humayun, Randall Balestriero, Richard Baraniuk
The study of Deep Network (DN) training dynamics has largely focused on the evolution of the loss function, evaluated on or around train and test set data points. In fact, many DN…
Novice Learner and Expert Tutor: Evaluating Math Reasoning Abilities of Large Language Models with Misconceptions
Naiming Liu, Shashank Sonkar, Zichao Wang +2
We propose novel evaluations for mathematical reasoning capabilities of Large Language Models (LLMs) based on mathematical misconceptions. Our primary approach is to simulate LLMs…
Implicit Neural Representations and the Algebra of Complex Wavelets
T. Mitchell Roddenberry, Vishwanath Saragadam, Maarten V. de Hoop +1
Implicit neural representations (INRs) have arisen as useful methods for representing signals on Euclidean domains. By parameterizing an image as a multilayer perceptron (MLP) on E…