6 citations · 18 across the 11 of their papers we have counts for
11 papers
Enhancing Stochastic Gradient Descent: A Unified Framework and Novel Acceleration Methods for Faster Convergence
Yichuan Deng, Zhao Song, Chiwun Yang
Based on SGD, previous works have proposed many algorithms that have improved convergence speed and generalization in stochastic optimization, such as SGDm, AdaGrad, Adam, etc. How…
Unmasking Transformers: A Theoretical Approach to Data Recovery via Attention Weights
Yichuan Deng, Zhao Song, Shenghao Xie +1
In the realm of deep learning, transformers have emerged as a dominant architecture, particularly in natural language processing tasks. However, with their widespread adoption, con…
Clustered Linear Contextual Bandits with Knapsacks
Yichuan Deng, Michalis Mamakos, Zhao Song
In this work, we study clustered contextual bandits where rewards and resource consumption are the outcomes of cluster-specific linear models. The arms are divided in clusters, wit…
Convergence of Two-Layer Regression with Nonlinear Units
Yichuan Deng, Zhao Song, Shenghao Xie
Large language models (LLMs), such as ChatGPT and GPT4, have shown outstanding performance in many human life task. Attention computation plays an important role in training LLMs.…
Zero-th Order Algorithm for Softmax Attention Optimization
Yichuan Deng, Zhihang Li, Sridhar Mahadevan +1
Large language models (LLMs) have brought about significant transformations in human society. Among the crucial computations in LLMs, the softmax unit holds great importance. Its h…
Faster Robust Tensor Power Method for Arbitrary Order
Yichuan Deng, Zhao Song, Junze Yin
Tensor decomposition is a fundamental method used in various areas to deal with high-dimensional data. \emph{Tensor power method} (TPM) is one of the widely-used techniques in the…