1 code implementation • 13 Mar 2024 • Hengyuan Ma, Wenlian Lu, Jianfeng Feng
Combinatorial optimization problems are widespread but inherently challenging due to their discrete nature. The primary limitation of existing methods is that they can only access a small fraction of the solution space at each iteration, resulting in limited efficiency for searching the global optimal.
1 code implementation • 30 May 2023 • Hengyuan Ma, Yang Qi, Li Zhang, Wenlian Lu, Jianfeng Feng
Building robust, interpretable, and secure AI system requires quantifying and representing uncertainty under a probabilistic perspective to mimic human cognitive abilities.
1 code implementation • 13 Feb 2023 • Hengyuan Ma, Li Zhang, Xiatian Zhu, Jianfeng Feng
Compared with the latest generative models (\eg, CLD-SGM, DDIM, and Analytic-DDIM), PDS can achieve the best sampling quality on CIFAR-10 at a FID score of 1. 99.
1 code implementation • 5 Jul 2022 • Hengyuan Ma, Li Zhang, Xiatian Zhu, Jianfeng Feng
However, a fundamental limitation is that their inference is very slow due to a need for many (e. g., 2000) iterations of sequential computations.
no code implementations • 8 Jun 2022 • Hengyuan Ma, Li Zhang, Xiatian Zhu, Jingfeng Zhang, Jianfeng Feng
To ensure stability of convergence in sampling and generation quality, however, this sequential sampling process has to take a small step size and many sampling iterations (e. g., 2000).
no code implementations • 29 Sep 2021 • Hengyuan Ma, Qi Yang, Bowen Sun, Long Shun, Junkui Li, Jianfeng Feng
Graph neural networks (GNN) demonstrate excellent performance on many graph-based tasks; however, they also impose a heavy computational burden when trained on a large-scale graph.