18 citations · 25 across the 7 of their papers we have counts for
9 papers
Fitting Vision Adapters at Frontier Scales
Jaehoon Lee, Harry Partridge, Mudith Jayasekara +3
Training a small projector between a frozen vision encoder and language model is an established approach to multimodal learning. As the parameter count of language models scales dr…
The Optimization Landscape of Learning Compacted Context Models
Thomas Villeneuve, Alex Sandomirsky, Charles O'Neill +2
Many works approach continual learning through the lens of infinite context windows. As an agent puts more observation into context (concretely the KV cache), compacting said conte…
Role of Uncertainty in Anticipatory Trajectory Prediction for a Ping-Pong Playing Robot
Nima Rahmanian, Michael Gupta, Renzo Soatto +4
Robotic interaction in fast-paced environments presents a substantial challenge, particularly in tasks requiring the prediction of dynamic, non-stationary objects for timely and ac…
Learning a Diffusion Model Policy from Rewards via Q-Score Matching
Michael Psenka, Alejandro Escontrela, Pieter Abbeel +1
Diffusion models have become a popular choice for representing actor policies in behavior cloning and offline reinforcement learning. This is due to their natural ability to optimi…
Representation Learning via Manifold Flattening and Reconstruction
Michael Psenka, Druv Pai, Vishal Raman +2
This work proposes an algorithm for explicitly constructing a pair of neural networks that linearize and reconstruct an embedded submanifold, from finite samples of this manifold.…
Pursuit of a Discriminative Representation for Multiple Subspaces via Sequential Games
Druv Pai, Michael Psenka, Chih-Yuan Chiu +3
We consider the problem of learning discriminative representations for data in a high-dimensional space with distribution supported on or around multiple low-dimensional linear sub…
Closed-Loop Data Transcription to an LDR via Minimaxing Rate Reduction
Xili Dai, Shengbang Tong, Mingyang Li +8
This work proposes a new computational framework for learning a structured generative model for real-world datasets. In particular, we propose to learn a closed-loop transcription…
Second-order optimization for tensors with fixed tensor-train rank
Michael Psenka, Nicolas Boumal
There are several different notions of "low rank" for tensors, associated to different formats. Among them, the Tensor Train (TT) format is particularly well suited for tensors of…
A Proof of The Triangular Ashbaugh-Benguria-Payne-Pólya-Weinberger Inequality
Ryan Arbon, Mohammed Mannan, Michael Psenka +1
In this paper, we show that for all triangles in the plane, the equilateral triangle maximizes the ratio of the first two Dirichlet-Laplacian eigenvalues. This is an extension of w…