11 citations · 25 across the 8 of their papers we have counts for
8 papers
DScheLLM: Enabling Dynamic Scheduling through a Fine-Tuned Dual-System Large language Model
Lixiang Zhang, Chenggong Zhao, Qing Gao +3
Production scheduling is highly susceptible to dynamic disruptions, such as variations in processing times, machine availability, and unexpected task insertions. Conventional appro…
Rule-Based Conflict-Free Decision Framework in Swarm Confrontation
Zhaoqi Dong, Zhinan Wang, Quanqi Zheng +3
Traditional rule-based decision-making methods with interpretable advantage, such as finite state machine, suffer from the jitter or deadlock(JoD) problems in extremely dynamic sce…
AttriCLIP: A Non-Incremental Learner for Incremental Knowledge Learning
Runqi Wang, Xiaoyue Duan, Guoliang Kang +5
Continual learning aims to enable a model to incrementally learn knowledge from sequentially arrived data. Previous works adopt the conventional classification architecture, which…
Resilient Binary Neural Network
Sheng Xu, Yanjing Li, Teli Ma +5
Binary neural networks (BNNs) have received ever-increasing popularity for their great capability of reducing storage burden as well as quickening inference time. However, there is…
IDa-Det: An Information Discrepancy-aware Distillation for 1-bit Detectors
Sheng Xu, Yanjing Li, Bohan Zeng +5
Knowledge distillation (KD) has been proven to be useful for training compact object detection models. However, we observe that KD is often effective when the teacher model and stu…
Recurrent Bilinear Optimization for Binary Neural Networks
Sheng Xu, Yanjing Li, Tiancheng Wang +6
Binary Neural Networks (BNNs) show great promise for real-world embedded devices. As one of the critical steps to achieve a powerful BNN, the scale factor calculation plays an esse…
TerViT: An Efficient Ternary Vision Transformer
Sheng Xu, Yanjing Li, Teli Ma +4
Vision transformers (ViTs) have demonstrated great potential in various visual tasks, but suffer from expensive computational and memory cost problems when deployed on resource-con…
An Improved Reinforcement Learning Algorithm for Learning to Branch
Qingyu Qu, Xijun Li, Yunfan Zhou +6
Most combinatorial optimization problems can be formulated as mixed integer linear programming (MILP), in which branch-and-bound (B\&B) is a general and widely used method. Recentl…