13 citations · 22 across the 8 of their papers we have counts for
9 papers
When Models Know When They Do Not Know: Calibration, Cascading, and Cleaning
Chenjie Hao, Weyl Lu, Yuko Ishiwaka +3
When a model knows when it does not know, many possibilities emerge. The first question is how to enable a model to recognize that it does not know. A promising approach is to use…
PICO-RAM: A PVT-Insensitive Analog Compute-In-Memory SRAM Macro with In-Situ Multi-Bit Charge Computing and 6T Thin-Cell-Compatible Layout
Zhiyu Chen, Ziyuan Wen, Weier Wan +6
Analog compute-in-memory (CIM) in static random-access memory (SRAM) is promising for accelerating deep learning inference by circumventing the memory wall and exploiting ultra-eff…
Heavy-Tailed Regularization of Weight Matrices in Deep Neural Networks
Xuanzhe Xiao, Zeng Li, Chuanlong Xie +1
Unraveling the reasons behind the remarkable success and exceptional generalization capabilities of deep neural networks presents a formidable challenge. Recent insights from rando…
Unsupervised Learning of Structured Representations via Closed-Loop Transcription
Shengbang Tong, Xili Dai, Yubei Chen +5
This paper proposes an unsupervised method for learning a unified representation that serves both discriminative and generative purposes. While most existing unsupervised learning…
Bag of Image Patch Embedding Behind the Success of Self-Supervised Learning
Yubei Chen, Adrien Bardes, Zengyi Li +1
Self-supervised learning (SSL) has recently achieved tremendous empirical advancements in learning image representation. However, our understanding of the principle behind learning…
Neural Manifold Clustering and Embedding
Zengyi Li, Yubei Chen, Yann LeCun +1
Given a union of non-linear manifolds, non-linear subspace clustering or manifold clustering aims to cluster data points based on manifold structures and also learn to parameterize…
A Neural Network MCMC sampler that maximizes Proposal Entropy
Zengyi Li, Yubei Chen, Friedrich T. Sommer
Markov Chain Monte Carlo (MCMC) methods sample from unnormalized probability distributions and offer guarantees of exact sampling. However, in the continuous case, unfavorable geom…
Complex Amplitude-Phase Boltzmann Machines
Zengyi Li, Friedrich T. Sommer
We extend the framework of Boltzmann machines to a network of complex-valued neurons with variable amplitudes, referred to as Complex Amplitude-Phase Boltzmann machine (CAP-BM). Th…
Learning Energy-Based Models in High-Dimensional Spaces with Multi-scale Denoising Score Matching
Zengyi Li, Yubei Chen, Friedrich T. Sommer
Energy-Based Models (EBMs) assign unnormalized log-probability to data samples. This functionality has a variety of applications, such as sample synthesis, data denoising, sample r…