14 citations · 39 across the 11 of their papers we have counts for
11 papers
YouZhi: Towards High-Concurrency Financial LLMs via Adaptive GQA-to-MLA Transition
PSBC LLM Team, Huawei LLM Team, Ruihan Long +56
Large language models (LLMs) drive significant financial innovations, yet their high-concurrency deployment is severely bottlenecked by KV cache memory overhead, which inflates inf…
TVT: Training-Free Vision Transformer Search on Tiny Datasets
Zimian Wei, Hengyue Pan, Lujun Li +4
Training-free Vision Transformer (ViT) architecture search is presented to search for a better ViT with zero-cost proxies. While ViTs achieve significant distillation gains from CN…
EMQ: Evolving Training-free Proxies for Automated Mixed Precision Quantization
Peijie Dong, Lujun Li, Zimian Wei +3
Mixed-Precision Quantization~(MQ) can achieve a competitive accuracy-complexity trade-off for models. Conventional training-based search methods require time-consuming candidate tr…
Self-Evolution Learning for Mixup: Enhance Data Augmentation on Few-Shot Text Classification Tasks
Haoqi Zheng, Qihuang Zhong, Liang Ding +4
Text classification tasks often encounter few shot scenarios with limited labeled data, and addressing data scarcity is crucial. Data augmentation with mixup has shown to be effect…
Progressive Meta-Pooling Learning for Lightweight Image Classification Model
Peijie Dong, Xin Niu, Zhiliang Tian +5
Practical networks for edge devices adopt shallow depth and small convolutional kernels to save memory and computational cost, which leads to a restricted receptive field. Conventi…
RD-NAS: Enhancing One-shot Supernet Ranking Ability via Ranking Distillation from Zero-cost Proxies
Peijie Dong, Xin Niu, Lujun Li +5
Neural architecture search (NAS) has made tremendous progress in the automatic design of effective neural network structures but suffers from a heavy computational burden. One-shot…
OVO: One-shot Vision Transformer Search with Online distillation
Zimian Wei, Hengyue Pan, Xin Niu +1
Pure transformers have shown great potential for vision tasks recently. However, their accuracy in small or medium datasets is not satisfactory. Although some existing methods intr…
DMFormer: Closing the Gap Between CNN and Vision Transformers
Zimian Wei, Hengyue Pan, Lujun Li +4
Vision transformers have shown excellent performance in computer vision tasks. As the computation cost of their self-attention mechanism is expensive, recent works tried to replace…
Prior-Guided One-shot Neural Architecture Search
Peijie Dong, Xin Niu, Lujun Li +5
Neural architecture search methods seek optimal candidates with efficient weight-sharing supernet training. However, recent studies indicate poor ranking consistency about the perf…
Multi-trial Neural Architecture Search with Lottery Tickets
Zimian Wei, Hengyue Pan, Lujun Li +4
Neural architecture search (NAS) has brought significant progress in recent image recognition tasks. Most existing NAS methods apply restricted search spaces, which limits the uppe…
On the Resistance of Nearest Neighbor to Random Noisy Labels
Wei Gao, Bin-Bin Yang, Zhi-Hua Zhou
Nearest neighbor has always been one of the most appealing non-parametric approaches in machine learning, pattern recognition, computer vision, etc. Previous empirical studies part…