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09/12/2023
Re-Reading Improves Reasoning in Language Models
Reasoning presents a significant and challenging issue for Large Languag...
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07/10/2023
One-Shot Pruning for Fast-adapting Pre-trained Models on Devices
Large-scale pre-trained models have been remarkably successful in resolv...
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07/04/2023
Causal Reinforcement Learning: A Survey
Reinforcement learning is an essential paradigm for solving sequential d...
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06/06/2023
Personalization Disentanglement for Federated Learning
Personalized federated learning (PFL) jointly trains a variety of local ...
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06/01/2023
Improving the Robustness of Summarization Systems with Dual Augmentation
A robust summarization system should be able to capture the gist of the ...
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05/29/2023
Continual Task Allocation in Meta-Policy Network via Sparse Prompting
How to train a generalizable meta-policy by continually learning a seque...
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05/23/2023
Spatial-temporal Prompt Learning for Federated Weather Forecasting
Federated weather forecasting is a promising collaborative learning fram...
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05/12/2023
Knowledge Refinement via Interaction Between Search Engines and Large Language Models
Information retrieval (IR) plays a crucial role in locating relevant res...
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04/27/2023
Large Language Models are Strong Zero-Shot Retriever
In this work, we propose a simple method that applies a large language m...
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04/09/2023
Does Continual Learning Equally Forget All Parameters?
Distribution shift (e.g., task or domain shift) in continual learning (C...
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02/04/2023
Neural Time Series Analysis with Fourier Transform: A Survey
Recently, Fourier transform has been widely introduced into deep neural ...
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01/27/2023
Voting from Nearest Tasks: Meta-Vote Pruning of Pre-trained Models for Downstream Tasks
As a few large-scale pre-trained models become the major choices of vari...
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01/26/2023
Style-Aware Contrastive Learning for Multi-Style Image Captioning
Existing multi-style image captioning methods show promising results in ...
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01/26/2023
Improving Cross-modal Alignment for Text-Guided Image Inpainting
Text-guided image inpainting (TGII) aims to restore missing regions base...
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01/26/2023
Multimodal Event Transformer for Image-guided Story Ending Generation
Image-guided story ending generation (IgSEG) is to generate a story endi...
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01/22/2023
Prompt Federated Learning for Weather Forecasting: Toward Foundation Models on Meteorological Data
To tackle the global climate challenge, it urgently needs to develop a c...
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01/22/2023
Federated Recommendation with Additive Personalization
With rising concerns about privacy, developing recommendation systems in...
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12/20/2022
Fine-Grained Distillation for Long Document Retrieval
Long document retrieval aims to fetch query-relevant documents from a la...
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11/23/2022
Federated Learning on Non-IID Graphs via Structural Knowledge Sharing
Graph neural networks (GNNs) have shown their superiority in modeling gr...
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10/27/2022
Unsupervised Knowledge Graph Construction and Event-centric Knowledge Infusion for Scientific NLI
With the advance of natural language inference (NLI), a rising demand fo...
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09/21/2022
Federated Learning from Pre-Trained Models: A Contrastive Learning Approach
Federated Learning (FL) is a machine learning paradigm that allows decen...
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06/16/2022
Towards Robust Ranker for Text Retrieval
A ranker plays an indispensable role in the de facto 'retrieval rera...
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05/20/2022
FedNoiL: A Simple Two-Level Sampling Method for Federated Learning with Noisy Labels
Federated learning (FL) aims at training a global model on the server si...
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03/04/2022
ClarET: Pre-training a Correlation-Aware Context-To-Event Transformer for Event-Centric Generation and Classification
Generating new events given context with correlated ones plays a crucial...
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02/13/2022
On the Convergence of Clustered Federated Learning
In a federated learning system, the clients, e.g. mobile devices and org...
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10/13/2021
EventBERT: A Pre-Trained Model for Event Correlation Reasoning
Event correlation reasoning infers whether a natural language paragraph ...
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09/07/2021
Sequential Diagnosis Prediction with Transformer and Ontological Representation
Sequential diagnosis prediction on the Electronic Health Record (EHR) ha...
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09/06/2021
Eliminating Sentiment Bias for Aspect-Level Sentiment Classification with Unsupervised Opinion Extraction
Aspect-level sentiment classification (ALSC) aims at identifying the sen...
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08/24/2021
Federated Learning for Privacy-Preserving Open Innovation Future on Digital Health
Privacy protection is an ethical issue with broad concern in Artificial ...
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08/24/2021
Federated Learning for Open Banking
Open banking enables individual customers to own their banking data, whi...
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07/20/2021
MIMO: Mutual Integration of Patient Journey and Medical Ontology for Healthcare Representation Learning
Healthcare representation learning on the Electronic Health Record (EHR)...
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07/10/2021
Beyond Low-pass Filtering: Graph Convolutional Networks with Automatic Filtering
Graph convolutional networks are becoming indispensable for deep learnin...
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05/01/2021
FedProto: Federated Prototype Learning over Heterogeneous Devices
The heterogeneity across devices usually hinders the optimization conver...
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03/03/2021
Task Aligned Generative Meta-learning for Zero-shot Learning
Zero-shot learning (ZSL) refers to the problem of learning to classify i...
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02/25/2021
Emerging Trends in Federated Learning: From Model Fusion to Federated X Learning
Federated learning is a new learning paradigm that decouples data collec...
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02/03/2021
Isometric Propagation Network for Generalized Zero-shot Learning
Zero-shot learning (ZSL) aims to classify images of an unseen class only...
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12/02/2020
SemiNLL: A Framework of Noisy-Label Learning by Semi-Supervised Learning
Deep learning with noisy labels is a challenging task. Recent prominent ...
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11/06/2020
Confusable Learning for Large-class Few-Shot Classification
Few-shot image classification is challenging due to the lack of ample sa...
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11/02/2020
Cooperative Heterogeneous Deep Reinforcement Learning
Numerous deep reinforcement learning agents have been proposed, and each...
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10/10/2020
RatE: Relation-Adaptive Translating Embedding for Knowledge Graph Completion
Many graph embedding approaches have been proposed for knowledge graph c...
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09/24/2020
Attribute Propagation Network for Graph Zero-shot Learning
The goal of zero-shot learning (ZSL) is to train a model to classify sam...
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09/24/2020
BiteNet: Bidirectional Temporal Encoder Network to Predict Medical Outcomes
Electronic health records (EHRs) are longitudinal records of a patient's...
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06/28/2020
Many-Class Few-Shot Learning on Multi-Granularity Class Hierarchy
We study many-class few-shot (MCFS) problem in both supervised learning ...
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06/21/2020
A Universal Representation Transformer Layer for Few-Shot Image Classification
Few-shot classification aims to recognize unseen classes when presented ...
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06/03/2020
Interpretable Time-series Classification on Few-shot Samples
Recent few-shot learning works focus on training a model with prior meta...
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05/24/2020
Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks
Modeling multivariate time series has long been a subject that has attra...
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05/03/2020
Multi-Center Federated Learning
Federated learning has received great attention for its capability to tr...
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04/30/2020
Semantic Triple Encoder for Fast Open-Set Link Prediction
We improve both the open-set generalization and efficiency of link predi...
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04/29/2020
Exploiting Structured Knowledge in Text via Graph-Guided Representation Learning
In this work, we aim at equipping pre-trained language models with struc...
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02/24/2020