activity
20182023
most citedMixup-Based Acoustic Scene Classification Using Multi-Channel Convolutional Neural Network

7 citations · 21 across the 10 of their papers we have counts for

collaborators

10 papers

cs.LG2023★ 1 cited

Uncertainty-Penalized Reinforcement Learning from Human Feedback with Diverse Reward LoRA Ensembles

Yuanzhao Zhai, Han Zhang, Yu Lei +5

Reinforcement learning from human feedback (RLHF) emerges as a promising paradigm for aligning large language models (LLMs). However, a notable challenge in RLHF is overoptimizatio…

cs.MA2022

Diversifying Message Aggregation in Multi-Agent Communication via Normalized Tensor Nuclear Norm Regularization

Yuanzhao Zhai, Kele Xu, Bo Ding +3

Aggregating messages is a key component for the communication of multi-agent reinforcement learning (Comm-MARL). Recently, it has witnessed the prevalence of graph attention networ…

cs.LG2022★ 1 cited

Self-Supervised Exploration via Temporal Inconsistency in Reinforcement Learning

Zijian Gao, Kele Xu, Yuanzhao Zhai +4

Under sparse extrinsic reward settings, reinforcement learning has remained challenging, despite surging interests in this field. Previous attempts suggest that intrinsic reward ca…

cs.AI2022

Dynamic Memory-based Curiosity: A Bootstrap Approach for Exploration

Zijian Gao, YiYing Li, Kele Xu +5

The sparsity of extrinsic rewards poses a serious challenge for reinforcement learning (RL). Currently, many efforts have been made on curiosity which can provide a representative…

cs.LG2022★ 1 cited

Nuclear Norm Maximization Based Curiosity-Driven Learning

Chao Chen, Zijian Gao, Kele Xu +5

To handle the sparsity of the extrinsic rewards in reinforcement learning, researchers have proposed intrinsic reward which enables the agent to learn the skills that might come in…

cs.IR2021★ 3 cited

FINT: Field-aware INTeraction Neural Network For CTR Prediction

Zhishan Zhao, Sen Yang, Guohui Liu +2

As a critical component for online advertising and marking, click-through rate (CTR) prediction has draw lots of attentions from both industry and academia field. Recently, the dee…

eess.IV2019★ 4 cited

Denoising convolutional autoencoder based B-mode ultrasound tongue image feature extraction

Bo Li, Kele Xu, Dawei Feng +3

B-mode ultrasound tongue imaging is widely used in the speech production field. However, efficient interpretation is in a great need for the tongue image sequences. Inspired by the…

cs.LG2018★ 3 cited

Collaborative Deep Learning Across Multiple Data Centers

Kele Xu, Haibo Mi, Dawei Feng +4

Valuable training data is often owned by independent organizations and located in multiple data centers. Most deep learning approaches require to centralize the multi-datacenter da…

cs.CV2018★ 1 cited

Sample Dropout for Audio Scene Classification Using Multi-Scale Dense Connected Convolutional Neural Network

Dawei Feng, Kele Xu, Haibo Mi +2

Acoustic scene classification is an intricate problem for a machine. As an emerging field of research, deep Convolutional Neural Networks (CNN) achieve convincing results. In this…

cs.CV2018★ 7 cited

Mixup-Based Acoustic Scene Classification Using Multi-Channel Convolutional Neural Network

Kele Xu, Dawei Feng, Haibo Mi +5

Audio scene classification, the problem of predicting class labels of audio scenes, has drawn lots of attention during the last several years. However, it remains challenging and f…