45 citations · 270 across the 21 of their papers we have counts for
21 papers
BAMBOO: A Comprehensive Benchmark for Evaluating Long Text Modeling Capacities of Large Language Models
Zican Dong, Tianyi Tang, Junyi Li +2
Large language models (LLMs) have achieved dramatic proficiency over NLP tasks with normal length. Recently, multiple studies have committed to extending the context length and enh…
Edge-based Local Push for Personalized PageRank
Hanzhi Wang, Zhewei Wei, Junhao Gan +3
Personalized PageRank (PPR) is a popular node proximity metric in graph mining and network research. Given a graph G=(V,E) and a source node , a single-source PPR (SSPPR)…
A Joint Model for Dropped Pronoun Recovery and Conversational Discourse Parsing in Chinese Conversational Speech
Jingxuan Yang, Kerui Xu, Jun Xu +5
In this paper, we present a neural model for joint dropped pronoun recovery (DPR) and conversational discourse parsing (CDP) in Chinese conversational speech. We show that DPR and…
Personalizing Search Results Using Hierarchical RNN with Query-aware Attention
Songwei Ge, Zhicheng Dou, Zhengbao Jiang +2
Search results personalization has become an effective way to improve the quality of search engines. Previous studies extracted information such as past clicks, user topical intere…
Unsupervised Adversarial Attacks on Deep Feature-based Retrieval with GAN
Guoping Zhao, Mingyu Zhang, Jiajun Liu +1
Studies show that Deep Neural Network (DNN)-based image classification models are vulnerable to maliciously constructed adversarial examples. However, little effort has been made t…
Generating Long and Informative Reviews with Aspect-Aware Coarse-to-Fine Decoding
Junyi Li, Wayne Xin Zhao, Ji-Rong Wen +1
Generating long and informative review text is a challenging natural language generation task. Previous work focuses on word-level generation, neglecting the importance of topical…
PRSim: Sublinear Time SimRank Computation on Large Power-Law Graphs
Zhewei Wei, Xiaodong He, Xiaokui Xiao +4
{\it SimRank} is a classic measure of the similarities of nodes in a graph. Given a node in graph , a {\em single-source SimRank query} returns the SimRank similarit…
A Long-Short Demands-Aware Model for Next-Item Recommendation
Ting Bai, Pan Du, Wayne Xin Zhao +2
Recommending the right products is the central problem in recommender systems, but the right products should also be recommended at the right time to meet the demands of users, so…
Face-Focused Cross-Stream Network for Deception Detection in Videos
Mingyu Ding, An Zhao, Zhiwu Lu +2
Automated deception detection (ADD) from real-life videos is a challenging task. It specifically needs to address two problems: (1) Both face and body contain useful cues regarding…
Zero-Shot Learning with Sparse Attribute Propagation
Nanyi Fei, Jiechao Guan, Zhiwu Lu +2
Zero-shot learning (ZSL) aims to recognize a set of unseen classes without any training images. The standard approach to ZSL requires a set of training images annotated with seen c…
Recursive Visual Attention in Visual Dialog
Yulei Niu, Hanwang Zhang, Manli Zhang +3
Visual dialog is a challenging vision-language task, which requires the agent to answer multi-round questions about an image. It typically needs to address two major problems: (1)…
Zero and Few Shot Learning with Semantic Feature Synthesis and Competitive Learning
Zhiwu Lu, Jiechao Guan, Aoxue Li +3
Zero-shot learning (ZSL) is made possible by learning a projection function between a feature space and a semantic space (e.g.,~an attribute space). Key to ZSL is thus to learn a p…
Transferrable Feature and Projection Learning with Class Hierarchy for Zero-Shot Learning
Aoxue Li, Zhiwu Lu, Jiechao Guan +3
Zero-shot learning (ZSL) aims to transfer knowledge from seen classes to unseen ones so that the latter can be recognised without any training samples. This is made possible by lea…
Domain-Invariant Projection Learning for Zero-Shot Recognition
An Zhao, Mingyu Ding, Jiechao Guan +3
Zero-shot learning (ZSL) aims to recognize unseen object classes without any training samples, which can be regarded as a form of transfer learning from seen classes to unseen ones…
KB4Rec: A Dataset for Linking Knowledge Bases with Recommender Systems
Wayne Xin Zhao, Gaole He, Hongjian Dou +3
To develop a knowledge-aware recommender system, a key data problem is how we can obtain rich and structured knowledge information for recommender system (RS) items. Existing datas…
RUM: network Representation learning throUgh Multi-level structural information preservation
Yanlei Yu, Zhiwu Lu, Jiajun Liu +3
We have witnessed the discovery of many techniques for network representation learning in recent years, ranging from encoding the context in random walks to embedding the lower ord…
Multi-Modal Multi-Scale Deep Learning for Large-Scale Image Annotation
Yulei Niu, Zhiwu Lu, Ji-Rong Wen +2
Image annotation aims to annotate a given image with a variable number of class labels corresponding to diverse visual concepts. In this paper, we address two main issues in large-…
Zero-Shot Fine-Grained Classification by Deep Feature Learning with Semantics
Aoxue Li, Zhiwu Lu, Liwei Wang +3
Fine-grained image classification, which aims to distinguish images with subtle distinctions, is a challenging task due to two main issues: lack of sufficient training data for eve…
A General SIMD-based Approach to Accelerating Compression Algorithms
Wayne Xin Zhao, Xudong Zhang, Daniel Lemire +4
Compression algorithms are important for data oriented tasks, especially in the era of Big Data. Modern processors equipped with powerful SIMD instruction sets, provide us an oppor…
Image classification by visual bag-of-words refinement and reduction
Zhiwu Lu, Liwei Wang, Ji-Rong Wen
This paper presents a new framework for visual bag-of-words (BOW) refinement and reduction to overcome the drawbacks associated with the visual BOW model which has been widely used…
Mining Frequent Neighborhood Patterns in Large Labeled Graphs
Jialong Han, Ji-Rong Wen
Over the years, frequent subgraphs have been an important sort of targeted patterns in the pattern mining literatures, where most works deal with databases holding a number of grap…