Summary of Bpqp: a Differentiable Convex Optimization Framework For Efficient End-to-end Learning, by Jianming Pan et al.
BPQP: A Differentiable Convex Optimization Framework for Efficient End-to-End Learningby Jianming Pan, Zeqi Ye, Xiao…
BPQP: A Differentiable Convex Optimization Framework for Efficient End-to-End Learningby Jianming Pan, Zeqi Ye, Xiao…
Zero-Forget Preservation of Semantic Communication Alignment in Distributed AI Networksby Jingzhi Hu, Geoffrey Ye LiFirst…
NeuroLifting: Neural Inference on Markov Random Fields at Scaleby Yaomin Wang, Chaolong Ying, Xiaodong Luo,…
Pilot Contamination Aware Transformer for Downlink Power Control in Cell-Free Massive MIMO Networksby Atchutaram K.…
Swarm Intelligence-Driven Client Selection for Federated Learning in Cybersecurity applicationsby Koffka Khan, Wayne GoodridgeFirst submitted…
Proactive Gradient Conflict Mitigation in Multi-Task Learning: A Sparse Training Perspectiveby Zhi Zhang, Jiayi Shen,…
Diffusion Self-Distillation for Zero-Shot Customized Image Generationby Shengqu Cai, Eric Chan, Yunzhi Zhang, Leonidas Guibas,…
Preserving Deep Representations In One-Shot Pruning: A Hessian-Free Second-Order Optimization Frameworkby Ryan Lucas, Rahul MazumderFirst…
Federated Learning with Uncertainty and Personalization via Efficient Second-order Optimizationby Shivam Pal, Aishwarya Gupta, Saqib…
An End-to-End Smart Predict-then-Optimize Framework for Vehicle Relocation Problems in Large-Scale Vehicle Crowd Sensingby Xinyu…