b'Ruiyi Zhang'

research

∙ 09/02/2023

Bias and Fairness in Large Language Models: A Survey

Rapid advancements of large language models (LLMs) have enabled the proc...

0 Isabel O. Gallegos, et al. ∙

research

∙ 08/22/2023

Knowledge Graph Prompting for Multi-Document Question Answering

The 'pre-train, prompt, predict' paradigm of large language models (LLMs...

0 Yu Wang, et al. ∙

research

∙ 07/23/2023

Learning Navigational Visual Representations with Semantic Map Supervision

Being able to perceive the semantics and the spatial structure of the en...

0 Yicong Hong, et al. ∙

research

∙ 06/29/2023

LLaVAR: Enhanced Visual Instruction Tuning for Text-Rich Image Understanding

Instruction tuning unlocks the superior capability of Large Language Mod...

0 Yanzhe Zhang, et al. ∙

research

∙ 05/31/2023

Label-Retrieval-Augmented Diffusion Models for Learning from Noisy Labels

Learning from noisy labels is an important and long-standing problem in ...

0 Jian Chen, et al. ∙

research

∙ 05/23/2023

Enhancing Detail Preservation for Customized Text-to-Image Generation: A Regularization-Free Approach

Recent text-to-image generation models have demonstrated impressive capa...

0 Yufan Zhou, et al. ∙

research

∙ 05/20/2023

Few-Shot Dialogue Summarization via Skeleton-Assisted Prompt Transfer

In real-world scenarios, labeled samples for dialogue summarization are ...

0 Kaige Xie, et al. ∙

research

∙ 05/09/2023

Towards Building the Federated GPT: Federated Instruction Tuning

While “instruction-tuned" generative large language models (LLMs) have d...

0 Jianyi Zhang, et al. ∙

research

∙ 10/19/2022

Robustness of Demonstration-based Learning Under Limited Data Scenario

Demonstration-based learning has shown great potential in stimulating pr...

0 Hongxin Zhang, et al. ∙

research

∙ 07/18/2022

STT: Soft Template Tuning for Few-Shot Adaptation

Prompt tuning has been an extremely effective tool to adapt a pre-traine...

5 Ping Yu, et al. ∙

research

∙ 05/26/2022

Federated Non-negative Matrix Factorization for Short Texts Topic Modeling with Mutual Information

Non-negative matrix factorization (NMF) based topic modeling is widely u...

5 Shijing Si, et al. ∙

research

∙ 11/27/2021

LAFITE: Towards Language-Free Training for Text-to-Image Generation

One of the major challenges in training text-to-image generation models ...

9 Yufan Zhou, et al. ∙

research

∙ 09/09/2021

Graphine: A Dataset for Graph-aware Terminology Definition Generation

Precisely defining the terminology is the first step in scientific commu...

11 Zequn Liu, et al. ∙

research

∙ 03/17/2021

Improving Zero-shot Voice Style Transfer via Disentangled Representation Learning

Voice style transfer, also called voice conversion, seeks to modify one ...

0 Siyang Yuan, et al. ∙

research

∙ 01/02/2021

SDA: Improving Text Generation with Self Data Augmentation

Data augmentation has been widely used to improve deep neural networks i...

0 Ping Yu, et al. ∙

research

∙ 10/12/2020

Improving Text Generation with Student-Forcing Optimal Transport

Neural language models are often trained with maximum likelihood estimat...

2 Guoyin Wang, et al. ∙

research

∙ 09/20/2020

Repulsive Attention: Rethinking Multi-head Attention as Bayesian Inference

The neural attention mechanism plays an important role in many natural l...

8 Bang An, et al. ∙

research

∙ 09/15/2020

Unsupervised Abstractive Dialogue Summarization for Tete-a-Tetes

High-quality dialogue-summary paired data is expensive to produce and do...

0 Xinyuan Zhang, et al. ∙

research

∙ 07/09/2020

Influence Diagram Bandits: Variational Thompson Sampling for Structured Bandit Problems

We propose a novel framework for structured bandits, which we call an in...

0 Tong Yu, et al. ∙

research

∙ 05/24/2020

When does MAML Work the Best? An Empirical Study on Model-Agnostic Meta-Learning in NLP Applications

Model-Agnostic Meta-Learning (MAML), a model-agnostic meta-learning meth...

0 Zequn Liu, et al. ∙

research

∙ 05/04/2020

Reward Constrained Interactive Recommendation with Natural Language Feedback

Text-based interactive recommendation provides richer user feedback and ...

0 Ruiyi Zhang, et al. ∙

research

∙ 05/04/2020

Improving Adversarial Text Generation by Modeling the Distant Future

Auto-regressive text generation models usually focus on local fluency, a...

0 Ruiyi Zhang, et al. ∙

research

∙ 03/14/2020

Security Analysis of EOSIO Smart Contracts

The EOSIO blockchain, one of the representative Delegated Proof-of-Stake...

0 Ningyu He, et al. ∙

research

∙ 02/21/2020

GenDICE: Generalized Offline Estimation of Stationary Values

An important problem that arises in reinforcement learning and Monte Car...

11 Ruiyi Zhang, et al. ∙

research

∙ 01/20/2020

Nested-Wasserstein Self-Imitation Learning for Sequence Generation

Reinforcement learning (RL) has been widely studied for improving sequen...

0 Ruiyi Zhang, et al. ∙

research

∙ 12/21/2019

Learning Diverse Stochastic Human-Action Generators by Learning Smooth Latent Transitions

Human-motion generation is a long-standing challenging task due to the r...

9 Zhenyi Wang, et al. ∙

research

∙ 10/21/2019

Collaborative Filtering with A Synthetic Feedback Loop

We propose a novel learning framework for recommendation systems, assist...

0 Wenlin Wang, et al. ∙

research

∙ 09/30/2019

Improving Textual Network Learning with Variational Homophilic Embeddings

The performance of many network learning applications crucially hinges o...

0 Wenlin Wang, et al. ∙

research

∙ 06/07/2019

Figure Captioning with Reasoning and Sequence-Level Training

Figures, such as bar charts, pie charts, and line plots, are widely used...

0 Charles Chen, et al. ∙

research

∙ 03/17/2019

Topic-Guided Variational Autoencoders for Text Generation

We propose a topic-guided variational autoencoder (TGVAE) model for text...

0 Wenlin Wang, et al. ∙

research

∙ 02/19/2019

Scalable Thompson Sampling via Optimal Transport

Thompson sampling (TS) is a class of algorithms for sequential decision-...

2 Ruiyi Zhang, et al. ∙

research

∙ 01/18/2019

Improving Sequence-to-Sequence Learning via Optimal Transport

Sequence-to-sequence models are commonly trained via maximum likelihood ...

0 Liqun Chen, et al. ∙

research

∙ 11/02/2018

Sequence Generation with Guider Network

Sequence generation with reinforcement learning (RL) has received signif...

0 Ruiyi Zhang, et al. ∙

research

∙ 09/05/2018

Stochastic Particle-Optimization Sampling and the Non-Asymptotic Convergence Theory

Particle-optimization sampling (POS) is a recently developed technique t...

0 Jianyi Zhang, et al. ∙

research

∙ 08/09/2018

Policy Optimization as Wasserstein Gradient Flows

Policy optimization is a core component of reinforcement learning (RL), ...

0 Ruiyi Zhang, et al. ∙

research

∙ 07/04/2018

Accelerated First-order Methods on the Wasserstein Space for Bayesian Inference

We consider doing Bayesian inference by minimizing the KL divergence on ...

0 Chang Liu, et al. ∙

research

∙ 05/29/2018

A Unified Particle-Optimization Framework for Scalable Bayesian Sampling

There has been recent interest in developing scalable Bayesian sampling ...

0 Changyou Chen, et al. ∙

research

∙ 12/30/2017

Learning Structural Weight Uncertainty for Sequential Decision-Making

Learning probability distributions on the weights of neural networks (NN...

1 Ruiyi Zhang, et al. ∙

research

∙ 11/29/2017

Particle Optimization in Stochastic Gradient MCMC

Stochastic gradient Markov chain Monte Carlo (SG-MCMC) has been increasi...

0 Changyou Chen, et al. ∙

Ruiyi Zhang

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