output
20062024
most citedStatistical physics of human cooperation

1.6k citations

Showing 2023Show all

30 papers · 1 filter

cs.LG202330 cited

ClST: A Convolutional Transformer Framework for Automatic Modulation Recognition by Knowledge Distillation

Dongbin Hou, Lixin Li, Wensheng Lin +2

With the rapid development of deep learning (DL) in recent years, automatic modulation recognition (AMR) with DL has achieved high accuracy. However, insufficient training signal d…

cs.LG202331 cited

A Novel Normalized-Cut Solver with Nearest Neighbor Hierarchical Initialization

Feiping Nie, Jitao Lu, Danyang Wu +2

Normalized-Cut (N-Cut) is a famous model of spectral clustering. The traditional N-Cut solvers are two-stage: 1) calculating the continuous spectral embedding of normalized Laplaci…

cs.SD202315 cited

Decoupling and Interacting Multi-Task Learning Network for Joint Speech and Accent Recognition

Qijie Shao, Pengcheng Guo, Jinghao Yan +2

Accents, as variations from standard pronunciation, pose significant challenges for speech recognition systems. Although joint automatic speech recognition (ASR) and accent recogni…

cs.CR202327 cited

B^2SFL: A Bi-level Blockchained Architecture for Secure Federated Learning-based Traffic Prediction

Hao Guo, Collin Meese, Wanxin Li +2

Federated Learning (FL) is a privacy-preserving machine learning (ML) technology that enables collaborative training and learning of a global ML model based on aggregating distribu…

quant-ph20238 cited

Berry Curvature and Bulk-Boundary Correspondence from Transport Measurement for Photonic Chern Bands

Chao Chen, Run-Ze Liu, Jizhou Wu +12

Berry curvature is a fundamental element to characterize topological quantum physics, while a full measurement of Berry curvature in momentum space was not reported for topological…

cs.CV20232 cited

A Geometrical Approach to Evaluate the Adversarial Robustness of Deep Neural Networks

Yang Wang, Bo Dong, Ke Xu +4

Deep Neural Networks (DNNs) are widely used for computer vision tasks. However, it has been shown that deep models are vulnerable to adversarial attacks, i.e., their performances d…