research
∙
09/19/2023
RUEL: Retrieval-Augmented User Representation with Edge Browser Logs for Sequential Recommendation
Online recommender systems (RS) aim to match user needs with the vast am...
share
research
∙
08/06/2023
Serverless Federated AUPRC Optimization for Multi-Party Collaborative Imbalanced Data Mining
Multi-party collaborative training, such as distributed learning and fed...
research
∙
04/17/2023
Typos-aware Bottlenecked Pre-Training for Robust Dense Retrieval
Current dense retrievers (DRs) are limited in their ability to effective...
research
∙
03/27/2023
Lexicon-Enhanced Self-Supervised Training for Multilingual Dense Retrieval
Recent multilingual pre-trained models have shown better performance in ...
research
∙
03/14/2023
Disentangled Graph Social Recommendation
Social recommender systems have drawn a lot of attention in many online ...
research
∙
02/18/2023
A Comprehensive Survey on Pretrained Foundation Models: A History from BERT to ChatGPT
The Pretrained Foundation Models (PFMs) are regarded as the foundation f...
research
∙
02/04/2023
Knowledge-enhanced Neural Machine Reasoning: A Review
Knowledge-enhanced neural machine reasoning has garnered significant att...
research
∙
02/03/2023
LazyGNN: Large-Scale Graph Neural Networks via Lazy Propagation
Recent works have demonstrated the benefits of capturing long-distance d...
research
∙
10/05/2022
Revisiting Graph Contrastive Learning from the Perspective of Graph Spectrum
Graph Contrastive Learning (GCL), learning the node representations by a...
research
∙
08/24/2022
DP2-Pub: Differentially Private High-Dimensional Data Publication with Invariant Post Randomization
A large amount of high-dimensional and heterogeneous data appear in prac...
research
∙
08/16/2022
Knowledge-Injected Federated Learning
Federated learning is an emerging technique for training models from dec...
research
∙
08/01/2022
On Shapley Value in Data Assemblage Under Independent Utility
In many applications, an organization may want to acquire data from many...
research
∙
07/12/2022
Revealing Unfair Models by Mining Interpretable Evidence
The popularity of machine learning has increased the risk of unfair mode...
research
∙
06/21/2022
Bridging the Gap Between Indexing and Retrieval for Differentiable Search Index with Query Generation
The Differentiable Search Index (DSI) is a new, emerging paradigm for in...
research
∙
06/11/2022
Communication-Efficient Robust Federated Learning with Noisy Labels
Federated learning (FL) is a promising privacy-preserving machine learni...
research
∙
06/06/2022
Multi-Behavior Sequential Recommendation with Temporal Graph Transformer
Modeling time-evolving preferences of users with their sequential item i...
research
∙
05/16/2022
Trustworthy Graph Neural Networks: Aspects, Methods and Trends
Graph neural networks (GNNs) have emerged as a series of competent graph...
research
∙
05/07/2022
Multi-level Contrastive Learning for Cross-lingual Spoken Language Understanding
Although spoken language understanding (SLU) has achieved great success ...
research
∙
04/18/2022
Spatial-Temporal Hypergraph Self-Supervised Learning for Crime Prediction
Crime has become a major concern in many cities, which calls for the ris...
research
∙
03/10/2022
Membership Privacy Protection for Image Translation Models via Adversarial Knowledge Distillation
Image-to-image translation models are shown to be vulnerable to the Memb...
research
∙
03/05/2022
Cosine Model Watermarking Against Ensemble Distillation
Many model watermarking methods have been developed to prevent valuable ...
research
∙
01/07/2022
Fair and efficient contribution valuation for vertical federated learning
Federated learning is a popular technology for training machine learning...
research
∙
12/22/2021
Multiple Choice Questions based Multi-Interest Policy Learning for Conversational Recommendation
Conversational recommendation system (CRS) is able to obtain fine-graine...
research
∙
12/15/2021
Mining Minority-class Examples With Uncertainty Estimates
In the real world, the frequency of occurrence of objects is naturally s...
research
∙
10/08/2021
Knowledge-Enhanced Hierarchical Graph Transformer Network for Multi-Behavior Recommendation
Accurate user and item embedding learning is crucial for modern recommen...
research
∙
09/26/2021
AsySQN: Faster Vertical Federated Learning Algorithms with Better Computation Resource Utilization
Vertical federated learning (VFL) is an effective paradigm of training t...
research
∙
09/19/2021
Improving Fairness for Data Valuation in Federated Learning
Federated learning is an emerging decentralized machine learning scheme ...
research
∙
09/17/2021
Achieving Model Fairness in Vertical Federated Learning
Vertical federated learning (VFL), which enables multiple enterprises po...
research
∙
09/13/2021
FedFair: Training Fair Models In Cross-Silo Federated Learning
Building fair machine learning models becomes more and more important. A...
research
∙
09/03/2021
Learning from Multiple Noisy Augmented Data Sets for Better Cross-Lingual Spoken Language Understanding
Lack of training data presents a grand challenge to scaling out spoken l...
research
∙
08/30/2021
Auto-Split: A General Framework of Collaborative Edge-Cloud AI
In many industry scale applications, large and resource consuming machin...
research
∙
08/13/2021
Finding Representative Interpretations on Convolutional Neural Networks
Interpreting the decision logic behind effective deep convolutional neur...
research
∙
07/08/2021
Robust Counterfactual Explanations on Graph Neural Networks
Massive deployment of Graph Neural Networks (GNNs) in high-stake applica...
research
∙
06/26/2021
Decentralized Composite Optimization in Stochastic Networks: A Dual Averaging Approach with Linear Convergence
Decentralized optimization, particularly the class of decentralized comp...
research
∙
06/10/2021
Graph Neural Networks for Natural Language Processing: A Survey
Deep learning has become the dominant approach in coping with various ta...
research
∙
06/01/2021
Reinforced Iterative Knowledge Distillation for Cross-Lingual Named Entity Recognition
Named entity recognition (NER) is a fundamental component in many applic...
research
∙
03/08/2021
Model Complexity of Deep Learning: A Survey
Model complexity is a fundamental problem in deep learning. In this pape...
research
∙
12/11/2020
Reinforced Multi-Teacher Selection for Knowledge Distillation
In natural language processing (NLP) tasks, slow inference speed and hug...
research
∙
11/11/2020
CalibreNet: Calibration Networks for Multilingual Sequence Labeling
Lack of training data in low-resource languages presents huge challenges...
research
∙
11/01/2020
Comprehensible Counterfactual Interpretation on Kolmogorov-Smirnov Test
The Kolmogorov-Smirnov (KS) test is popularly used in many applications,...
research
∙
10/27/2020
Cross-lingual Machine Reading Comprehension with Language Branch Knowledge Distillation
Cross-lingual Machine Reading Comprehension (CLMRC) remains a challengin...
research
∙
10/14/2020
A Graph Representation of Semi-structured Data for Web Question Answering
The abundant semi-structured data on the Web, such as HTML-based tables ...
research
∙
10/06/2020
Differential Privacy and Its Applications in Social Network Analysis: A Survey
Differential privacy is effective in sharing information and preserving ...
research
∙
09/09/2020
A Survey on Data Pricing: from Economics to Data Science
Data are invaluable. How can we assess the value of data objectively, sy...
research
∙
08/18/2020
Accelerated Zeroth-Order Momentum Methods from Mini to Minimax Optimization
In the paper, we propose a new accelerated zeroth-order momentum (Acc-ZO...
research
∙
07/13/2020
Momentum-Based Policy Gradient Methods
In the paper, we propose a class of efficient momentum-based policy grad...
research
∙
07/07/2020
Personalized Federated Learning: An Attentive Collaboration Approach
For the challenging computational environment of IOT/edge computing, per...
research
∙
07/05/2020
AM-GCN: Adaptive Multi-channel Graph Convolutional Networks
Graph Convolutional Networks (GCNs) have gained great popularity in tack...
research
∙
06/16/2020
Measuring Model Complexity of Neural Networks with Curve Activation Functions
It is fundamental to measure model complexity of deep neural networks. T...
research
∙
06/13/2020