1 citations · 3 across the 6 of their papers we have counts for
6 papers
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…
Generative Lifting of Multiview to 3D from Unknown Pose: Wrapping NeRF inside Diffusion
Xin Yuan, Rana Hanocka, Michael Maire
We cast multiview reconstruction from unknown pose as a generative modeling problem. From a collection of unannotated 2D images of a scene, our approach simultaneously learns both…
Drop-Connect as a Fault-Tolerance Approach for RRAM-based Deep Neural Network Accelerators
Mingyuan Xiang, Xuhan Xie, Pedro Savarese +3
Resistive random-access memory (RRAM) is widely recognized as a promising emerging hardware platform for deep neural networks (DNNs). Yet, due to manufacturing limitations, current…
SONIQ: System-Optimized Noise-Injected Ultra-Low-Precision Quantization with Full-Precision Parity
Cyrus Zhou, Pedro Savarese, Zack Hassman +4
Ultra-low-precision inference can sharply reduce memory and latency but often degrades accuracy and relies on specialized hardware. We present SONIQ, a system-optimized, noise-inje…
HyperFields: Towards Zero-Shot Generation of NeRFs from Text
Sudarshan Babu, Richard Liu, Avery Zhou +3
We introduce HyperFields, a method for generating text-conditioned Neural Radiance Fields (NeRFs) with a single forward pass and (optionally) some fine-tuning. Key to our approach…
Factorized Diffusion Architectures for Unsupervised Image Generation and Segmentation
Xin Yuan, Michael Maire
We develop a neural network architecture which, trained in an unsupervised manner as a denoising diffusion model, simultaneously learns to both generate and segment images. Learnin…