5 citations · 8 across the 9 of their papers we have counts for
9 papers
MIRAGE: Robust multi-modal architectures translate fMRI-to-image models from vision to mental imagery
Reese Kneeland, Cesar Kadir Torrico Villanueva, Jordyn Ojeda +4
To be useful for downstream applications, vision decoding models that are trained to reconstruct seen images from human brain activity must be able to generalize to internally gene…
Algorithmic Simplification of Neural Networks with Mosaic-of-Motifs
Pedram Bakhtiarifard, Tong Chen, Jonathan Wenshøj +2
Large-scale deep learning models are well-suited for compression. Across a variety of tasks, methods like pruning, quantization, and knowledge distillation have been used to achiev…
CoDeQ: End-to-End Joint Model Compression with Dead-Zone Quantizer for High-Sparsity and Low-Precision Networks
Jonathan Wenshøj, Tong Chen, Bob Pepin +1
While joint pruning--quantization is theoretically superior to sequential application, current joint methods rely on auxiliary procedures outside the training loop for finding comp…
A Discrepancy-Based Perspective on Dataset Condensation
Tong Chen, Raghavendra Selvan
Given a dataset of finitely many elements , the goal of dataset condensation (DC) is to construct a synthetic dataset $\mathcal{S} = \{\ti…
Hotspot-Driven Peptide Design via Multi-Fragment Autoregressive Extension
Jiahan Li, Tong Chen, Shitong Luo +7
Peptides, short chains of amino acids, interact with target proteins, making them a unique class of protein-based therapeutics for treating human diseases. Recently, deep generativ…
RS-NeRF: Neural Radiance Fields from Rolling Shutter Images
Muyao Niu, Tong Chen, Yifan Zhan +3
Neural Radiance Fields (NeRFs) have become increasingly popular because of their impressive ability for novel view synthesis. However, their effectiveness is hindered by the Rollin…
MindEye2: Shared-Subject Models Enable fMRI-To-Image With 1 Hour of Data
Paul S. Scotti, Mihir Tripathy, Cesar Kadir Torrico Villanueva +8
Reconstructions of visual perception from brain activity have improved tremendously, but the practical utility of such methods has been limited. This is because such models are tra…
An Empirical Study of the Influence of Adversarial Fine-Tuning on Compressed Neural Networks
Hallgrimur Thorsteinsson, Valdemar J Henriksen, Daniel I R Cruz +2
As deep learning (DL) models are increasingly being integrated into our everyday lives, ensuring their safety by making them robust against adversarial attacks has become increasin…
Is Adversarial Training with Compressed Datasets Effective?
Tong Chen, Raghavendra Selvan
Dataset Condensation (DC) refers to the recent class of dataset compression methods that generate a smaller, synthetic, dataset from a larger dataset. This synthetic dataset aims t…