Search Results for author:
Found 95 papers, 27 papers with code
Self-Supervised Multi-Channel Hypergraph Convolutional Network for Social Recommendation
In this paper, we fill this gap and propose a multi-channel hypergraph convolutional network to enhance social recommendation by leveraging high-order user relations.
Socially-Aware Self-Supervised Tri-Training for Recommendation
Under this scheme, only a bijective mapping is built between nodes in two different views, which means that the self-supervision signals from other nodes are being neglected.
TrustLLM: Trustworthiness in Large Language Models
This paper introduces TrustLLM, a comprehensive study of trustworthiness in LLMs, including principles for different dimensions of trustworthiness, established benchmark, evaluation, and analysis of trustworthiness for mainstream LLMs, and discussion of open challenges and future directions.
Large Language Model based Multi-Agents: A Survey of Progress and Challenges
To provide the community with an overview of this dynamic field, we present this survey to offer an in-depth discussion on the essential aspects of multi-agent systems based on LLMs, as well as the challenges.
Self-Supervised Hypergraph Convolutional Networks for Session-based Recommendation
Moreover, to enhance hypergraph modeling, we devise another graph convolutional network which is based on the line graph of the hypergraph and then integrate self-supervised learning into the training of the networks by maximizing mutual information between the session representations learned via the two networks, serving as an auxiliary task to improve the recommendation task.
Graph-based Molecular Representation Learning
Recently, MRL has achieved considerable progress, especially in methods based on deep molecular graph learning.
What can Large Language Models do in chemistry? A comprehensive benchmark on eight tasks
In this paper, rather than pursuing state-of-the-art performance, we aim to evaluate capabilities of LLMs in a wide range of tasks across the chemistry domain.
A PCA-Based Change Detection Framework for Multidimensional Data Streams: Change Detection in Multidimensional Data Streams
In this paper, we propose a framework for detecting changes in multidimensional data streams based on principal component analysis, which is used for projecting data into a lower dimensional space, thus facilitating density estimation and change-score calculations.
MWP-BERT: Numeracy-Augmented Pre-training for Math Word Problem Solving
Math word problem (MWP) solving faces a dilemma in number representation learning.
Ranked #5 on
Math Word Problem Solving
on MathQA
Does Head Label Help for Long-Tailed Multi-Label Text Classification
To address the challenge of insufficient training data on tail label classification, we propose a Head-to-Tail Network (HTTN) to transfer the meta-knowledge from the data-rich head labels to data-poor tail labels.
NOSMOG: Learning Noise-robust and Structure-aware MLPs on Graphs
Existing methods attempt to address this scalability issue by training multi-layer perceptrons (MLPs) exclusively on node content features using labels derived from trained GNNs.
Collaborative Graph Walk for Semi-supervised Multi-Label Node Classification
In this work, we study semi-supervised multi-label node classification problem in attributed graphs.
Towards Improving Faithfulness in Abstractive Summarization
The evaluation of factual consistency also shows that our model generates more faithful summaries than baselines.
SenWave: Monitoring the Global Sentiments under the COVID-19 Pandemic
Since the first alert launched by the World Health Organization (5 January, 2020), COVID-19 has been spreading out to over 180 countries and territories.
Follow the Timeline! Generating Abstractive and Extractive Timeline Summary in Chronological Order
Nowadays, time-stamped web documents related to a general news query floods spread throughout the Internet, and timeline summarization targets concisely summarizing the evolution trajectory of events along the timeline.
Capturing Relations between Scientific Papers: An Abstractive Model for Related Work Section Generation
Hence, in this paper, we propose a Relation-aware Related work Generator (RRG), which generates an abstractive related work from the given multiple scientific papers in the same research area.
Target-aware Abstractive Related Work Generation with Contrastive Learning
The related work section is an important component of a scientific paper, which highlights the contribution of the target paper in the context of the reference papers.
ArMATH: a Dataset for Solving Arabic Math Word Problems
In addition, a transfer learning model is built to let the high-resource Chinese MWP solver promote the performance of the low-resource Arabic MWP solver.
Recurrent Attention Walk for Semi-supervised Classification
We propose to explore the neighborhood in a reinforcement learning setting and find a walk path well-tuned for classifying the unlabelled target nodes.
LogicRec: Recommendation with Users' Logical Requirements
In this work, we formulate the problem of recommendation with users' logical requirements (LogicRec) and construct benchmark datasets for LogicRec.
Analogical Math Word Problems Solving with Enhanced Problem-Solution Association
In this paper, we propose to build a novel MWP solver by leveraging analogical MWPs, which advance the solver's generalization ability across different kinds of MWPs.
Improving the Robustness of Summarization Systems with Dual Augmentation
The other factor is in the latent space, where the attacked inputs bring more variations to the hidden states.
GESF: A Universal Discriminative Mapping Mechanism for Graph Representation Learning
Graph embedding is a central problem in social network analysis and many other applications, aiming to learn the vector representation for each node.
Graph Embedding with Rich Information through Heterogeneous Network
In this paper, we proposed a novel and general framework of representation learning for graph with rich text information through constructing a bipartite heterogeneous network.
Coarse Grained Exponential Variational Autoencoders
Variational autoencoders (VAE) often use Gaussian or category distribution to model the inference process.
Risk Convergence of Centered Kernel Ridge Regression with Large Dimensional Data
This paper carries out a large dimensional analysis of a variation of kernel ridge regression that we call \emph{centered kernel ridge regression} (CKRR), also known in the literature as kernel ridge regression with offset.
Robust Federated Training via Collaborative Machine Teaching using Trusted Instances
In our work, we propose a collaborative and privacy-preserving machine teaching paradigm with multiple distributed teachers, to improve robustness of the federated training process against local data corruption.
Multi-View Multiple Clustering
It then uses matrix factorization on the individual matrices, along with the shared matrix, to generate diverse clusterings of high-quality.
ActiveHNE: Active Heterogeneous Network Embedding
To maximize the profit of utilizing the rare and valuable supervised information in HNEs, we develop a novel Active Heterogeneous Network Embedding (ActiveHNE) framework, which includes two components: Discriminative Heterogeneous Network Embedding (DHNE) and Active Query in Heterogeneous Networks (AQHN).
Weakly-paired Cross-Modal Hashing
FlexCMH first introduces a clustering-based matching strategy to explore the local structure of each cluster, and thus to find the potential correspondence between clusters (and samples therein) across modalities.
Cross-modal Zero-shot Hashing
It then defines an objective function to achieve deep feature learning compatible with the composite similarity preserving, category attribute space learning, and hashing coding function learning.
PINE: Universal Deep Embedding for Graph Nodes via Partial Permutation Invariant Set Functions
The key problem in graph node embedding lies in how to define the dependence to neighbors.
Active Multi-Label Crowd Consensus
AMCC accounts for the commonality and individuality of workers, and assumes that workers can be organized into different groups.
Prototypical Networks for Multi-Label Learning
We propose to formulate multi-label learning as a estimation of class distribution in a non-linear embedding space, where for each label, its positive data embeddings and negative data embeddings distribute compactly to form a positive component and negative component respectively, while the positive component and negative component are pushed away from each other.
Multi-View Multiple Clusterings using Deep Matrix Factorization
Multi-view clustering aims at integrating complementary information from multiple heterogeneous views to improve clustering results.
Attention-Aware Answers of the Crowd
However, they all assume that workers' label quality is stable over time (always at the same level whenever they conduct the tasks).
Efficient long-distance relation extraction with DG-SpanBERT
In natural language processing, relation extraction seeks to rationally understand unstructured text.
Ranked #16 on
Relation Extraction
on TACRED
Addressing Class-Imbalance Problem in Personalized Ranking
Pairwise ranking models have been widely used to address recommendation problems.
Graph Factorization Machines for Cross-Domain Recommendation
In this paper, we propose a Graph Factorization Machine (GFM) which utilizes the popular Factorization Machine to aggregate multi-order interactions from neighborhood for recommendation.
PAGE: A Simple and Optimal Probabilistic Gradient Estimator for Nonconvex Optimization
Then, we show that PAGE obtains the optimal convergence results $O(n+\frac{\sqrt{n}}{\epsilon^2})$ (finite-sum) and $O(b+\frac{\sqrt{b}}{\epsilon^2})$ (online) matching our lower bounds for both nonconvex finite-sum and online problems.
SAIL: Self-Augmented Graph Contrastive Learning
This paper studies learning node representations with graph neural networks (GNNs) for unsupervised scenario.
Temporal Positive-unlabeled Learning for Biomedical Hypothesis Generation via Risk Estimation
Then, the key is to capture the temporal evolution of node pair (term pair) relations from just the positive and unlabeled data.
Deep Incomplete Multi-View Multiple Clusterings
Multi-view clustering aims at exploiting information from multiple heterogeneous views to promote clustering.
Multi-typed Objects Multi-view Multi-instance Multi-label Learning
Multi-typed objects Multi-view Multi-instance Multi-label Learning (M4L) deals with interconnected multi-typed objects (or bags) that are made of diverse instances, represented with heterogeneous feature views and annotated with a set of non-exclusive but semantically related labels.
Deterministic and probabilistic deep learning models for inverse design of broadband acoustic cloak
Concealing an object from incoming waves (light and/or sound) remained science fiction for a long time due to the absence of wave-shielding materials in nature.
Applied Physics Computational Physics
ASAD: A Twitter-based Benchmark Arabic Sentiment Analysis Dataset
This paper provides a detailed description of a new Twitter-based benchmark dataset for Arabic Sentiment Analysis (ASAD), which is launched in a competition3, sponsored by KAUST for awarding 10000 USD, 5000 USD and 2000 USD to the first, second and third place winners, respectively.
CreditCoin: A Privacy-Preserving Blockchain-Based Incentive Announcement Network for Communications of Smart Vehicles
The vehicular announcement network is one of the most promising utilities in the communications of smart vehicles and in the smart transportation systems.
Characterizing the Evasion Attackability of Multi-label Classifiers
Evasion attack in multi-label learning systems is an interesting, widely witnessed, yet rarely explored research topic.
Combat Data Shift in Few-shot Learning with Knowledge Graph
However, in real-world applications, few-shot learning paradigm often suffers from data shift, i. e., samples in different tasks, even in the same task, could be drawn from various data distributions.
Graph Embedding for Recommendation against Attribute Inference Attacks
Specifically, in GERAI, we bind the information perturbation mechanism in differential privacy with the recommendation capability of graph convolutional networks.
Hierarchical Hyperedge Embedding-based Representation Learning for Group Recommendation
However, the representation learning for a group is most complex beyond the fusion of group member representation, as the personal preferences and group preferences may be in different spaces.
Fast-adapting and Privacy-preserving Federated Recommender System
In the mobile Internet era, the recommender system has become an irreplaceable tool to help users discover useful items, and thus alleviating the information overload problem.
Quaternion Factorization Machines: A Lightweight Solution to Intricate Feature Interaction Modelling
As a well-established approach, factorization machine (FM) is capable of automatically learning high-order interactions among features to make predictions without the need for manual feature engineering.
Uniting Heterogeneity, Inductiveness, and Efficiency for Graph Representation Learning
In WIDEN, we propose a novel inductive, meta path-free message passing scheme that packs up heterogeneous node features with their associated edges from both low- and high-order neighbor nodes.
FENet: A Frequency Extraction Network for Obstructive Sleep Apnea Detection
Obstructive Sleep Apnea (OSA) is a highly prevalent but inconspicuous disease that seriously jeopardizes the health of human beings.
Scientific Dataset Discovery via Topic-level Recommendation
Data intensive research requires the support of appropriate datasets.
Attack Transferability Characterization for Adversarially Robust Multi-label Classification
We unveil how the transferability level of the attack determines the attackability of the classifier via establishing an information-theoretic analysis of the adversarial risk.
FARF: A Fair and Adaptive Random Forests Classifier
This algorithm, called FARF (Fair and Adaptive Random Forests), is based on using online component classifiers and updating them according to the current distribution, that also accounts for fairness and a single hyperparameters that alters fairness-accuracy balance.
AppQ: Warm-starting App Recommendation Based on View Graphs
In this work, we propose AppQ, a novel app quality grading and recommendation system that extracts inborn features of apps based on app source code.
Overview of the Arabic Sentiment Analysis 2021 Competition at KAUST
From our recently released ASAD dataset, we provide the competitors with 55K tweets for training, and 20K tweets for validation, based on which the performance of participating teams are ranked on a leaderboard, https://www. kaggle. com/c/arabic-sentiment-analysis-2021-kaust.
Explainable Automatic Hypothesis Generation via High-order Graph Walks
In this paper, we study the automatic hypothesis generation (HG) problem, focusing on explainability.
A Simple and Debiased Sampling Method for Personalized Ranking
Pairwise ranking models have been widely used to address various problems, such as recommendation.
Towards Understanding the Robustness Against Evasion Attack on Categorical Data
Characterizing and assessing the adversarial vulnerability of classification models with categorical input has been a practically important, while rarely explored research problem.
Data-Efficient Language Shaped Few-shot Image Classification
Many existing works have demonstrated that language is a helpful guider for image understanding by neural networks.
Meta Cross-Modal Hashing on Long-Tailed Data
Due to the lack of training samples in the tail classes, MetaCMH first learns direct features from data in different modalities, and then introduces an associative memory module to learn the memory features of samples of the tail classes.
Cross-modal Zero-shot Hashing by Label Attributes Embedding
Cross-modal hashing (CMH) is one of the most promising methods in cross-modal approximate nearest neighbor search.
Crowdsourcing with Meta-Workers: A New Way to Save the Budget
Due to the unreliability of Internet workers, it's difficult to complete a crowdsourcing project satisfactorily, especially when the tasks are multiple and the budget is limited.
Open-Set Crowdsourcing using Multiple-Source Transfer Learning
First, OSCrowd integrates crowd theme related datasets into a large source domain to facilitate partial transfer learning to approximate the label space inference of these tasks.
P^2IR: Universal Deep Node Representation via Partial Permutation Invariant Set Functions
Our method can 1) learn an arbitrary form of the representation function from the neighborhood, without losing any potential dependence structures, 2) automatically decide the significance of neighbors at different distances, and 3) be applicable to both homogeneous and heterogeneous graph embedding, which may contain multiple types of nodes.
Data-Driven State Estimation for Light-Emitting Diode Underwater Optical Communication
Light-Emitting Diodes (LEDs) based underwater optical wireless communications (UOWCs), a technology with low latency and high data rates, have attracted significant importance for underwater robots.
Few-Shot Learning on Graphs
In light of this, few-shot learning on graphs (FSLG), which combines the strengths of graph representation learning and few-shot learning together, has been proposed to tackle the performance degradation in face of limited annotated data challenge.
Positive-Unlabeled Learning with Adversarial Data Augmentation for Knowledge Graph Completion
Most real-world knowledge graphs (KG) are far from complete and comprehensive.
TAR: Neural Logical Reasoning across TBox and ABox
Neural logical reasoning (NLR) is a fundamental task to explore such knowledge bases, which aims at answering multi-hop queries with logical operations based on distributed representations of queries and answers.
Unsupervised Mitigating Gender Bias by Character Components: A Case Study of Chinese Word Embedding
Word embeddings learned from massive text collections have demonstrated significant levels of discriminative biases. However, debias on the Chinese language, one of the most spoken languages, has been less explored. Meanwhile, existing literature relies on manually created supplementary data, which is time- and energy-consuming. In this work, we propose the first Chinese Gender-neutral word Embedding model (CGE) based on Word2vec, which learns gender-neutral word embeddings without any labeled data. Concretely, CGE utilizes and emphasizes the rich feminine and masculine information contained in radicals, i. e., a kind of component in Chinese characters, during the training procedure. This consequently alleviates discriminative gender biases. Experimental results on public benchmark datasets show that our unsupervised method outperforms the state-of-the-art supervised debiased word embedding models without sacrificing the functionality of the embedding model.
Few-shot News Recommendation via Cross-lingual Transfer
Second, the user preference over these topics is transferable across different platforms.
FALCON: Faithful Neural Semantic Entailment over ALC Ontologies
Many ontologies, i. e., Description Logic (DL) knowledge bases, have been developed to provide rich knowledge about various domains, and a lot of them are based on ALC, i. e., a prototypical and expressive DL, or its extensions.
ArtELingo: A Million Emotion Annotations of WikiArt with Emphasis on Diversity over Language and Culture
This paper introduces ArtELingo, a new benchmark and dataset, designed to encourage work on diversity across languages and cultures.
Pairwise Instance Relation Augmentation for Long-tailed Multi-label Text Classification
In response, we propose a Pairwise Instance Relation Augmentation Network (PIRAN) to augment tailed-label documents for balancing tail labels and head labels.
Multi Label Text Classification
Multi-Label Text Classification
+2
Generalizing Math Word Problem Solvers via Solution Diversification
In this paper, we design a new training framework for an MWP solver by introducing a solution buffer and a solution discriminator.
Scientific Paper Extractive Summarization Enhanced by Citation Graphs
We first propose a Multi-granularity Unsupervised Summarization model (MUS) as a simple and low-cost solution to the task.
Towards Efficient and Domain-Agnostic Evasion Attack with High-dimensional Categorical Inputs
Our work targets at searching feasible adversarial perturbation to attack a classifier with high-dimensional categorical inputs in a domain-agnostic setting.
AdvCat: Domain-Agnostic Robustness Assessment for Cybersecurity-Critical Applications with Categorical Inputs
Machine Learning-as-a-Service systems (MLaaS) have been largely developed for cybersecurity-critical applications, such as detecting network intrusions and fake news campaigns.
Knowledge Distillation on Graphs: A Survey
Therefore, to improve the applicability of GNNs and fully encode the complicated topological information, knowledge distillation on graphs (KDG) has been introduced to build a smaller yet effective model and exploit more knowledge from data, leading to model compression and performance improvement.
Learning towards Selective Data Augmentation for Dialogue Generation
As it is cumbersome and expensive to acquire a huge amount of data for training neural dialog models, data augmentation is proposed to effectively utilize existing training samples.
A Topic-aware Summarization Framework with Different Modal Side Information
To address these two challenges, we first propose a unified topic encoder, which jointly discovers latent topics from the document and various kinds of side information.
Let GPT be a Math Tutor: Teaching Math Word Problem Solvers with Customized Exercise Generation
In this paper, we present a novel approach for distilling math word problem solving capabilities from large language models (LLMs) into smaller, more efficient student models.
MinT: Boosting Generalization in Mathematical Reasoning via Multi-View Fine-Tuning
Our approach uniquely considers the various annotation formats as different "views" and leverages them in training the model.
Modeling non-uniform uncertainty in Reaction Prediction via Boosting and Dropout
With the widespread adoption of generative models, the Variational Autoencoder(VAE) framework has typically been employed to tackle challenges in reaction prediction, where the reactants are encoded as a condition for the decoder, which then generates the product.
Manipulating Predictions over Discrete Inputs in Machine Teaching
Machine teaching often involves the creation of an optimal (typically minimal) dataset to help a model (referred to as the `student') achieve specific goals given by a teacher.
SceMQA: A Scientific College Entrance Level Multimodal Question Answering Benchmark
The paper introduces SceMQA, a novel benchmark for scientific multimodal question answering at the college entrance level.
Are we making much progress? Revisiting chemical reaction yield prediction from an imbalanced regression perspective
The yield of a chemical reaction quantifies the percentage of the target product formed in relation to the reactants consumed during the chemical reaction.
UGMAE: A Unified Framework for Graph Masked Autoencoders
In light of this, we propose UGMAE, a unified framework for graph masked autoencoders to address these issues from the perspectives of adaptivity, integrity, complementarity, and consistency.
Defending Jailbreak Prompts via In-Context Adversarial Game
Large Language Models (LLMs) demonstrate remarkable capabilities across diverse applications.
Rethinking Scientific Summarization Evaluation: Grounding Explainable Metrics on Facet-aware Benchmark
Our findings confirm that FM offers a more logical approach to evaluating scientific summaries.
Zero-Shot Relational Learning for Multimodal Knowledge Graphs
Relational learning is an essential task in the domain of knowledge representation, particularly in knowledge graph completion (KGC). While relational learning in traditional single-modal settings has been extensively studied, exploring it within a multimodal KGC context presents distinct challenges and opportunities.
