23 citations · 88 across the 19 of their papers we have counts for
19 papers
Speech representation learning: Learning bidirectional encoders with single-view, multi-view, and multi-task methods
Qingming Tang
This thesis focuses on representation learning for sequence data over time or space, aiming to improve downstream sequence prediction tasks by using the learned representations. Su…
On-Device Constrained Self-Supervised Speech Representation Learning for Keyword Spotting via Knowledge Distillation
Gene-Ping Yang, Yue Gu, Qingming Tang +2
Large self-supervised models are effective feature extractors, but their application is challenging under on-device budget constraints and biased dataset collection, especially in…
Threshold-Consistent Margin Loss for Open-World Deep Metric Learning
Qin Zhang, Linghan Xu, Qingming Tang +4
Existing losses used in deep metric learning (DML) for image retrieval often lead to highly non-uniform intra-class and inter-class representation structures across test classes an…
Learning for Transductive Threshold Calibration in Open-World Recognition
Qin Zhang, Dongsheng An, Tianjun Xiao +6
In deep metric learning for visual recognition, the calibration of distance thresholds is crucial for achieving desired model performance in the true positive rates (TPR) or true n…
Weight-sharing Supernet for Searching Specialized Acoustic Event Classification Networks Across Device Constraints
Guan-Ting Lin, Qingming Tang, Chieh-Chi Kao +2
Acoustic Event Classification (AEC) has been widely used in devices such as smart speakers and mobile phones for home safety or accessibility support. As AEC models run on more and…
Federated Self-Supervised Learning for Acoustic Event Classification
Meng Feng, Chieh-Chi Kao, Qingming Tang +4
Standard acoustic event classification (AEC) solutions require large-scale collection of data from client devices for model optimization. Federated learning (FL) is a compelling fr…
Multi-Task Self-Supervised Pre-Training for Music Classification
Ho-Hsiang Wu, Chieh-Chi Kao, Qingming Tang +4
Deep learning is very data hungry, and supervised learning especially requires massive labeled data to work well. Machine listening research often suffers from limited labeled data…
Unsupervised Pre-training of Bidirectional Speech Encoders via Masked Reconstruction
Weiran Wang, Qingming Tang, Karen Livescu
We propose an approach for pre-training speech representations via a masked reconstruction loss. Our pre-trained encoder networks are bidirectional and can therefore be used direct…
Towards Disentangled Representations for Human Retargeting by Multi-view Learning
Chao Yang, Xiaofeng Liu, Qingming Tang +1
We study the problem of learning disentangled representations for data across multiple domains and its applications in human retargeting. Our goal is to map an input image to an id…
Dependency-aware Attention Control for Unconstrained Face Recognition with Image Sets
Xiaofeng Liu, B. V. K Vijaya Kumar, Chao Yang +2
This paper targets the problem of image set-based face verification and identification. Unlike traditional single media (an image or video) setting, we encounter a set of heterogen…
Variational Sequential Labelers for Semi-Supervised Learning
Mingda Chen, Qingming Tang, Karen Livescu +1
We introduce a family of multitask variational methods for semi-supervised sequence labeling. Our model family consists of a latent-variable generative model and a discriminative l…
Controllable Paraphrase Generation with a Syntactic Exemplar
Mingda Chen, Qingming Tang, Sam Wiseman +1
Prior work on controllable text generation usually assumes that the controlled attribute can take on one of a small set of values known a priori. In this work, we propose a novel t…
A Multi-Task Approach for Disentangling Syntax and Semantics in Sentence Representations
Mingda Chen, Qingming Tang, Sam Wiseman +1
We propose a generative model for a sentence that uses two latent variables, with one intended to represent the syntax of the sentence and the other to represent its semantics. We…
Image Inpainting using Block-wise Procedural Training with Annealed Adversarial Counterpart
Chao Yang, Yuhang Song, Xiaofeng Liu +2
Recent advances in deep generative models have shown promising potential in image inpanting, which refers to the task of predicting missing pixel values of an incomplete image usin…
Acoustic feature learning using cross-domain articulatory measurements
Qingming Tang, Weiran Wang, Karen Livescu
Previous work has shown that it is possible to improve speech recognition by learning acoustic features from paired acoustic-articulatory data, for example by using canonical corre…
Acoustic Feature Learning via Deep Variational Canonical Correlation Analysis
Qingming Tang, Weiran Wang, Karen Livescu
We study the problem of acoustic feature learning in the setting where we have access to another (non-acoustic) modality for feature learning but not at test time. We use deep vari…
Network Inference by Learned Node-Specific Degree Prior
Qingming Tang, Lifu Tu, Weiran Wang +1
We propose a novel method for network inference from partially observed edges using a node-specific degree prior. The degree prior is derived from observed edges in the network to…
Learning Scale-Free Networks by Dynamic Node-Specific Degree Prior
Qingming Tang, Siqi Sun, Jinbo Xu
Learning the network structure underlying data is an important problem in machine learning. This paper introduces a novel prior to study the inference of scale-free networks, which…
Exact Hybrid Covariance Thresholding for Joint Graphical Lasso
Qingming Tang, Chao Yang, Jian Peng +1
This paper considers the problem of estimating multiple related Gaussian graphical models from a -dimensional dataset consisting of different classes. Our work is based upon the…