research
∙
06/03/2021
JIZHI: A Fast and Cost-Effective Model-As-A-Service System for Web-Scale Online Inference at Baidu
In modern internet industries, deep learning based recommender systems h...
share
research
∙
10/12/2020
Improving Text Generation with Student-Forcing Optimal Transport
Neural language models are often trained with maximum likelihood estimat...
research
∙
05/04/2020
Improving Adversarial Text Generation by Modeling the Distant Future
Auto-regressive text generation models usually focus on local fluency, a...
research
∙
01/20/2020
Nested-Wasserstein Self-Imitation Learning for Sequence Generation
Reinforcement learning (RL) has been widely studied for improving sequen...
research
∙
11/20/2019
Graph-Driven Generative Models for Heterogeneous Multi-Task Learning
We propose a novel graph-driven generative model, that unifies multiple ...
research
∙
10/21/2019
Collaborative Filtering with A Synthetic Feedback Loop
We propose a novel learning framework for recommendation systems, assist...
research
∙
10/20/2019
An Optimal Transport Framework for Zero-Shot Learning
We present an optimal transport (OT) framework for generalized zero-shot...
research
∙
09/30/2019
Improving Textual Network Learning with Variational Homophilic Embeddings
The performance of many network learning applications crucially hinges o...
research
∙
09/14/2019
Ouroboros: On Accelerating Training of Transformer-Based Language Models
Language models are essential for natural language processing (NLP) task...
research
∙
06/05/2019
Improving Textual Network Embedding with Global Attention via Optimal Transport
Constituting highly informative network embeddings is an important tool ...
research
∙
05/15/2019
On Norm-Agnostic Robustness of Adversarial Training
Adversarial examples are carefully perturbed in-puts for fooling machine...
research
∙
03/17/2019
Topic-Guided Variational Autoencoders for Text Generation
We propose a topic-guided variational autoencoder (TGVAE) model for text...
research
∙
11/02/2018
Sequence Generation with Guider Network
Sequence generation with reinforcement learning (RL) has received signif...
research
∙
09/12/2018
Distilled Wasserstein Learning for Word Embedding and Topic Modeling
We propose a novel Wasserstein method with a distillation mechanism, yie...
research
∙
09/10/2018
Second-Order Adversarial Attack and Certifiable Robustness
We propose a powerful second-order attack method that outperforms existi...
research
∙
05/29/2018
A Unified Particle-Optimization Framework for Scalable Bayesian Sampling
There has been recent interest in developing scalable Bayesian sampling ...
research
∙
05/24/2018
Baseline Needs More Love: On Simple Word-Embedding-Based Models and Associated Pooling Mechanisms
Many deep learning architectures have been proposed to model the composi...
research
∙
05/14/2018
NASH: Toward End-to-End Neural Architecture for Generative Semantic Hashing
Semantic hashing has become a powerful paradigm for fast similarity sear...
research
∙
05/10/2018
Joint Embedding of Words and Labels for Text Classification
Word embeddings are effective intermediate representations for capturing...
research
∙
02/25/2018
Wide Compression: Tensor Ring Nets
Deep neural networks have demonstrated state-of-the-art performance in a...
research
∙
12/28/2017
Topic Compositional Neural Language Model
We propose a Topic Compositional Neural Language Model (TCNLM), a novel ...
research
∙
12/01/2017
InverseNet: Solving Inverse Problems with Splitting Networks
We propose a new method that uses deep learning techniques to solve the ...
research
∙
11/15/2017
Zero-Shot Learning via Class-Conditioned Deep Generative Models
We present a deep generative model for learning to predict classes not s...
research
∙
09/04/2017
A Convergence Analysis for A Class of Practical Variance-Reduction Stochastic Gradient MCMC
Stochastic gradient Markov Chain Monte Carlo (SG-MCMC) has been develope...
research
∙
09/04/2017