304 citations · 583 across the 11 of their papers we have counts for
11 papers
Federated Learning in the Presence of Adversarial Client Unavailability
Lili Su, Ming Xiang, Jiaming Xu +1
Federated learning is a decentralized machine learning framework that enables collaborative model training without revealing raw data. Due to the diverse hardware and software limi…
Global Convergence of Federated Learning for Mixed Regression
Lili Su, Jiaming Xu, Pengkun Yang
This paper studies the problem of model training under Federated Learning when clients exhibit cluster structure. We contextualize this problem in mixed regression, where each clie…
Speaker and Direction Inferred Dual-channel Speech Separation
Chenxing Li, Jiaming Xu, Nima Mesgarani +1
Most speech separation methods, trying to separate all channel sources simultaneously, are still far from having enough general- ization capabilities for real scenarios where the n…
Audio-visual Speech Separation with Adversarially Disentangled Visual Representation
Peng Zhang, Jiaming Xu, Jing shi +2
Speech separation aims to separate individual voice from an audio mixture of multiple simultaneous talkers. Although audio-only approaches achieve satisfactory performance, they bu…
Sequence to Multi-Sequence Learning via Conditional Chain Mapping for Mixture Signals
Jing Shi, Xuankai Chang, Pengcheng Guo +5
Neural sequence-to-sequence models are well established for applications which can be cast as mapping a single input sequence into a single output sequence. In this work, we focus…
Speaker-Conditional Chain Model for Speech Separation and Extraction
Jing Shi, Jiaming Xu, Yusuke Fujita +2
Speech separation has been extensively explored to tackle the cocktail party problem. However, these studies are still far from having enough generalization capabilities for real s…
DMRM: A Dual-channel Multi-hop Reasoning Model for Visual Dialog
Feilong Chen, Fandong Meng, Jiaming Xu +3
Visual Dialog is a vision-language task that requires an AI agent to engage in a conversation with humans grounded in an image. It remains a challenging task since it requires the…
Concept Learning through Deep Reinforcement Learning with Memory-Augmented Neural Networks
Jing Shi, Jiaming Xu, Yiqun Yao +1
Deep neural networks have shown superior performance in many regimes to remember familiar patterns with large amounts of data. However, the standard supervised deep learning paradi…
Cascaded Mutual Modulation for Visual Reasoning
Yiqun Yao, Jiaming Xu, Feng Wang +1
Visual reasoning is a special visual question answering problem that is multi-step and compositional by nature, and also requires intensive text-vision interactions. We propose CMM…
Self-Taught Convolutional Neural Networks for Short Text Clustering
Jiaming Xu, Bo Xu, Peng Wang +3
Short text clustering is a challenging problem due to its sparseness of text representation. Here we propose a flexible Self-Taught Convolutional neural network framework for Short…
Text Classification Improved by Integrating Bidirectional LSTM with Two-dimensional Max Pooling
Peng Zhou, Zhenyu Qi, Suncong Zheng +3
Recurrent Neural Network (RNN) is one of the most popular architectures used in Natural Language Processsing (NLP) tasks because its recurrent structure is very suitable to process…