633 citations · 964 across the 10 of their papers we have counts for
7 papers · 1 filter
A Middle Path for On-Premises LLM Deployment: Preserving Privacy Without Sacrificing Model Confidentiality
Hanbo Huang, Yihan Li, Bowen Jiang +5
Privacy-sensitive users require deploying large language models (LLMs) within their own infrastructure (on-premises) to safeguard private data and enable customization. However, vu…
Achieving Small Test Error in Mildly Overparameterized Neural Networks
Shiyu Liang, Ruoyu Sun, R. Srikant
Recent theoretical works on over-parameterized neural nets have focused on two aspects: optimization and generalization. Many existing works that study optimization and generalizat…
The Global Landscape of Neural Networks: An Overview
Ruoyu Sun, Dawei Li, Shiyu Liang +2
One of the major concerns for neural network training is that the non-convexity of the associated loss functions may cause bad landscape. The recent success of neural networks sugg…
Revisiting Landscape Analysis in Deep Neural Networks: Eliminating Decreasing Paths to Infinity
Shiyu Liang, Ruoyu Sun, R. Srikant
Traditional landscape analysis of deep neural networks aims to show that no sub-optimal local minima exist in some appropriate sense. From this, one may be tempted to conclude that…
Understanding the Loss Surface of Neural Networks for Binary Classification
Shiyu Liang, Ruoyu Sun, Yixuan Li +1
It is widely conjectured that the reason that training algorithms for neural networks are successful because all local minima lead to similar performance, for example, see (LeCun e…
Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks
Shiyu Liang, Yixuan Li, R. Srikant
We consider the problem of detecting out-of-distribution images in neural networks. We propose ODIN, a simple and effective method that does not require any change to a pre-trained…
Why Deep Neural Networks for Function Approximation?
Shiyu Liang, R. Srikant
Recently there has been much interest in understanding why deep neural networks are preferred to shallow networks. We show that, for a large class of piecewise smooth functions, th…