Publications (24)
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…
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…
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…
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…
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…
Bermuda: Bidirectional de novo assembly of transcripts with new insights for handling uneven coverage
Qingming Tang, Sheng Wang, Jian Peng +2
Motivation: RNA-seq has made feasible the analysis of a whole set of expressed mRNAs. Mapping-based assembly of RNA-seq reads sometimes is infeasible due to lack of high-quality re…
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…
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…
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…
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…
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…
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…
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…
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…
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…
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…
Fast Text-to-Audio Generation with One-Step Sampling via Energy-Scoring and Auxiliary Contextual Representation Distillation
Kuan-Po Huang, Bo-Ru Lu, Byeonggeun Kim +8
Autoregressive (AR) models with diffusion heads have recently achieved strong text-to-audio performance, yet their iterative decoding and multi-step sampling process introduce high…
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…
Improving Text-to-Audio Instruction Following via Fine-Grained Feedback from Audio-Aware Large Language Models
Chun-Yi Kuan, Siwon Kim, Byeonggeun Kim +8
The paper proposes using audio-aware large language models to give fine‑grained feedback on text‑to‑audio generation, improving how well the generated audio follows multi‑event and…
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…
Generative Audio Language Modeling with Continuous-valued Tokens and Masked Next-Token Prediction
Shu-wen Yang, Byeonggeun Kim, Kuan-Po Huang +8
Autoregressive next-token prediction with the Transformer decoder has become a de facto standard in large language models (LLMs), achieving remarkable success in Natural Language P…
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…
IMPACT: Iterative Mask-based Parallel Decoding for Text-to-Audio Generation with Diffusion Modeling
Kuan-Po Huang, Shu-wen Yang, Huy Phan +8
Text-to-audio generation synthesizes realistic sounds or music given a natural language prompt. Diffusion-based frameworks, including the Tango and the AudioLDM series, represent t…
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…