Surrogate Lagrangian Relaxation: A Path To Retrain-free Deep Neural Network Pruning

no code implementations • 8 Apr 2023 • Shanglin Zhou, Mikhail A. Bragin, Lynn Pepin, Deniz Gurevin, Fei Miao, Caiwen Ding

We evaluate our method on image classification tasks using CIFAR-10 and ImageNet with state-of-the-art MLP-Mixer, Swin Transformer, and VGG-16, ResNet-18, ResNet-50 and ResNet-110, MobileNetV2.

Image Classification Lane Detection +4

Shared Information-Based Safe And Efficient Behavior Planning For Connected Autonomous Vehicles

no code implementations • 8 Feb 2023 • Songyang Han, Shanglin Zhou, Lynn Pepin, Jiangwei Wang, Caiwen Ding, Fei Miao

The recent advancements in wireless technology enable connected autonomous vehicles (CAVs) to gather data via vehicle-to-vehicle (V2V) communication, such as processed LIDAR and camera data from other vehicles.

Autonomous Vehicles Multi-agent Reinforcement Learning

Enabling Retrain-free Deep Neural Network Pruning using Surrogate Lagrangian Relaxation

no code implementations • 18 Dec 2020 • Deniz Gurevin, Shanglin Zhou, Lynn Pepin, Bingbing Li, Mikhail Bragin, Caiwen Ding, Fei Miao

We further accelerate the convergence of the SLR by using quadratic penalties.

Image Classification Lane Detection +4

A Multi-Agent Reinforcement Learning Approach For Safe and Efficient Behavior Planning Of Connected Autonomous Vehicles

no code implementations • 9 Mar 2020 • Songyang Han, Shanglin Zhou, Jiangwei Wang, Lynn Pepin, Caiwen Ding, Jie Fu, Fei Miao

The truncated Q-function utilizes the shared information from neighboring CAVs such that the joint state and action spaces of the Q-function do not grow in our algorithm for a large-scale CAV system.

Autonomous Vehicles Multi-agent Reinforcement Learning +1