Search Results for author:
Found 18 papers, 2 papers with code
Speech representation learning: Learning bidirectional encoders with single-view, multi-view, and multi-task methods
Though I focus on speech data, the methods described in this thesis can also be applied to other domains.
Threshold-Consistent Margin Loss for Open-World Deep Metric Learning
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 and data distributions.
On-Device Constrained Self-Supervised Speech Representation Learning for Keyword Spotting via Knowledge Distillation
Our approach used a teacher-student framework to transfer knowledge from a larger, more complex model to a smaller, light-weight model using dual-view cross-correlation distillation and the teacher's codebook as learning objectives.
Learning for Transductive Threshold Calibration in Open-World Recognition
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 negative rates (TNR).
Federated Self-Supervised Learning for Acoustic Event Classification
Standard acoustic event classification (AEC) solutions require large-scale collection of data from client devices for model optimization.
Multi-Task Self-Supervised Pre-Training for Music Classification
Deep learning is very data hungry, and supervised learning especially requires massive labeled data to work well.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+7
Towards Disentangled Representations for Human Retargeting by Multi-view Learning
We study the problem of learning disentangled representations for data across multiple domains and its applications in human retargeting.
Dependency-aware Attention Control for Unconstrained Face Recognition with Image Sets
This paper targets the problem of image set-based face verification and identification.
Variational Sequential Labelers for Semi-Supervised Learning
Our model family consists of a latent-variable generative model and a discriminative labeler.
Ranked #72 on
Named Entity Recognition (NER)
on CoNLL 2003 (English)
Controllable Paraphrase Generation with a Syntactic Exemplar
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.
Variational recurrent models for representation learning
Existing variational recurrent models typically use stochastic recurrent connections to model the dependence among neighboring latent variables, while generation assumes independence of generated data per time step given the latent sequence.
A Multi-Task Approach for Disentangling Syntax and Semantics in Sentence Representations
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.
Image Inpainting using Block-wise Procedural Training with Annealed Adversarial Counterpart
We present a new approach to address the difficulty of training a very deep generative model to synthesize high-quality photo-realistic inpainting.
Acoustic feature learning using cross-domain articulatory measurements
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 correlation analysis (CCA) or its deep extensions.
Acoustic Feature Learning via Deep Variational Canonical Correlation Analysis
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.
Network Inference by Learned Node-Specific Degree Prior
We propose a novel method for network inference from partially observed edges using a node-specific degree prior.
Learning Scale-Free Networks by Dynamic Node-Specific Degree Prior
Learning the network structure underlying data is an important problem in machine learning.
Exact Hybrid Covariance Thresholding for Joint Graphical Lasso
This paper proposes a novel hybrid covariance thresholding algorithm that can effectively identify zero entries in the precision matrices and split a large joint graphical lasso problem into small subproblems.
