24 citations · 114 across the 41 of their papers we have counts for
6 papers · 1 filter
An empirical study of domain-agnostic semi-supervised learning via energy-based models: joint-training and pre-training
Yunfu Song, Huahuan Zheng, Zhijian Ou
A class of recent semi-supervised learning (SSL) methods heavily rely on domain-specific data augmentations. In contrast, generative SSL methods involve unsupervised learning based…
CAT: CRF-based ASR Toolkit
Keyu An, Hongyu Xiang, Zhijian Ou
In this paper, we present a new open source toolkit for automatic speech recognition (ASR), named CAT (CRF-based ASR Toolkit). A key feature of CAT is discriminative training in th…
Neural CRF transducers for sequence labeling
Kai Hu, Zhijian Ou, Min Hu +1
Conditional random fields (CRFs) have been shown to be one of the most successful approaches to sequence labeling. Various linear-chain neural CRFs (NCRFs) are developed to impleme…
A Review of Learning with Deep Generative Models from Perspective of Graphical Modeling
Zhijian Ou
This document aims to provide a review on learning with deep generative models (DGMs), which is an highly-active area in machine learning and more generally, artificial intelligenc…
Joint Stochastic Approximation learning of Helmholtz Machines
Haotian Xu, Zhijian Ou
Though with progress, model learning and performing posterior inference still remains a common challenge for using deep generative models, especially for handling discrete hidden v…
Block-Wise MAP Inference for Determinantal Point Processes with Application to Change-Point Detection
Jinye Zhang, Zhijian Ou
Existing MAP inference algorithms for determinantal point processes (DPPs) need to calculate determinants or conduct eigenvalue decomposition generally at the scale of the full ker…