b'Chang Zhou'

research

∙ 06/04/2022

Instance-wise Prompt Tuning for Pretrained Language Models

Prompt Learning has recently gained great popularity in bridging the gap...

Yuezihan Jiang, et al. ∙

research

∙ 05/24/2022

M6-Fashion: High-Fidelity Multi-modal Image Generation and Editing

The fashion industry has diverse applications in multi-modal image gener...

Zhikang Li, et al. ∙

research

∙ 05/17/2022

M6-Rec: Generative Pretrained Language Models are Open-Ended Recommender Systems

Industrial recommender systems have been growing increasingly complex, m...

Zeyu Cui, et al. ∙

research

∙ 03/29/2022

In-N-Out Generative Learning for Dense Unsupervised Video Segmentation

In this paper, we focus on the unsupervised Video Object Segmentation (V...

Xiao Pan, et al. ∙

research

∙ 03/23/2022

Modality Competition: What Makes Joint Training of Multi-modal Network Fail in Deep Learning? (Provably)

Despite the remarkable success of deep multi-modal learning in practice,...

Yu Huang, et al. ∙

research

∙ 02/07/2022

Unifying Architectures, Tasks, and Modalities Through a Simple Sequence-to-Sequence Learning Framework

In this work, we pursue a unified paradigm for multimodal pretraining to...

Peng Wang, et al. ∙

research

∙ 12/30/2021

Are we really making much progress? Revisiting, benchmarking, and refining heterogeneous graph neural networks

Heterogeneous graph neural networks (HGNNs) have been blossoming in rece...

Qingsong Lv, et al. ∙

research

∙ 10/08/2021

M6-10T: A Sharing-Delinking Paradigm for Efficient Multi-Trillion Parameter Pretraining

Recent expeditious developments in deep learning algorithms, distributed...

Junyang Lin, et al. ∙

research

∙ 08/19/2021

Causal Attention for Unbiased Visual Recognition

Attention module does not always help deep models learn causal features ...

Tan Wang, et al. ∙

research

∙ 06/08/2021

CSRNet: Cascaded Selective Resolution Network for Real-time Semantic Segmentation

Real-time semantic segmentation has received considerable attention due ...

Jingjing Xiong, et al. ∙

research

∙ 06/02/2021

Learning to Rehearse in Long Sequence Memorization

Existing reasoning tasks often have an important assumption that the inp...

Zhu Zhang, et al. ∙

research

∙ 05/31/2021

Controllable Gradient Item Retrieval

In this paper, we identify and study an important problem of gradient it...

Haonan Wang, et al. ∙

research

∙ 05/31/2021

Exploring Sparse Expert Models and Beyond

Mixture-of-Experts (MoE) models can achieve promising results with outra...

An Yang, et al. ∙

research

∙ 05/31/2021

Connecting Language and Vision for Natural Language-Based Vehicle Retrieval

Vehicle search is one basic task for the efficient traffic management in...

Shuai Bai, et al. ∙

research

∙ 05/31/2021

Sketch and Refine: Towards Faithful and Informative Table-to-Text Generation

Table-to-text generation refers to generating a descriptive text from a ...

Peng Wang, et al. ∙

research

∙ 05/29/2021

UFC-BERT: Unifying Multi-Modal Controls for Conditional Image Synthesis

Conditional image synthesis aims to create an image according to some mu...

Zhu Zhang, et al. ∙

research

∙ 05/26/2021

CogView: Mastering Text-to-Image Generation via Transformers

Text-to-Image generation in the general domain has long been an open pro...

Ming Ding, et al. ∙

research

∙ 03/01/2021

CogDL: An Extensive Toolkit for Deep Learning on Graphs

Graph representation learning aims to learn low-dimensional node embeddi...

Yukuo Cen, et al. ∙

research

∙ 03/01/2021

M6: A Chinese Multimodal Pretrainer

In this work, we construct the largest dataset for multimodal pretrainin...

Junyang Lin, et al. ∙

research

∙ 05/20/2020

Contrastive Learning for Debiased Candidate Generation in Large-Scale Recommender Systems

Deep candidate generation (DCG) that narrows down the collection of rele...

Chang Zhou, et al. ∙

research

∙ 05/20/2020

Contrastive Learning for Debiased Candidate Generation at Scale

Deep candidate generation has become an increasingly popular choice depl...

Chang Zhou, et al. ∙

research

∙ 05/20/2020

Understanding Negative Sampling in Graph Representation Learning

Graph representation learning has been extensively studied in recent yea...

Zhen Yang, et al. ∙

research

∙ 05/19/2020

Controllable Multi-Interest Framework for Recommendation

Recently, neural networks have been widely used in e-commerce recommende...

Yukuo Cen, et al. ∙

research

∙ 12/02/2019

ExperienceThinking: Hyperparameter Optimization with Budget Constraints

The problem of hyperparameter optimization exists widely in the real lif...

Chunnan Wang, et al. ∙

research

∙ 10/31/2019

Learning Disentangled Representations for Recommendation

User behavior data in recommender systems are driven by the complex inte...

Jianxin Ma, et al. ∙

research

∙ 07/04/2019

Dimensional Reweighting Graph Convolutional Networks

Graph Convolution Networks (GCNs) are becoming more and more popular for...

Xu Zou, et al. ∙

research

∙ 06/13/2019

Cognitive Knowledge Graph Reasoning for One-shot Relational Learning

Inferring new facts from existing knowledge graphs (KG) with explainable...

Zhengxiao Du, et al. ∙

research

∙ 05/14/2019

Cognitive Graph for Multi-Hop Reading Comprehension at Scale

We propose a new CogQA framework for multi-hop question answering in web...

Ming Ding, et al. ∙

research

∙ 04/03/2019

Personalized Bundle List Recommendation

Product bundling, offering a combination of items to customers, is one o...

Jinze Bai, et al. ∙

research

∙ 02/23/2019

AliGraph: A Comprehensive Graph Neural Network Platform

An increasing number of machine learning tasks require dealing with larg...

Rong Zhu, et al. ∙

research

∙ 09/11/2018

Deep Interest Evolution Network for Click-Through Rate Prediction

Click-through rate (CTR) prediction, whose goal is to estimate the proba...

Guorui Zhou, et al. ∙

research

∙ 11/17/2017

ATRank: An Attention-Based User Behavior Modeling Framework for Recommendation

A user can be represented as what he/she does along the history. A commo...

Chang Zhou, et al. ∙

Chang Zhou

Featured Co-authors