
-
Task Aligned Generative Meta-learning for Zero-shot Learning
Zero-shot learning (ZSL) refers to the problem of learning to classify i...
read it
-
Emerging Trends in Federated Learning: From Model Fusion to Federated X Learning
Federated learning is a new learning paradigm that decouples data collec...
read it
-
Isometric Propagation Network for Generalized Zero-shot Learning
Zero-shot learning (ZSL) aims to classify images of an unseen class only...
read it
-
SemiNLL: A Framework of Noisy-Label Learning by Semi-Supervised Learning
Deep learning with noisy labels is a challenging task. Recent prominent ...
read it
-
Confusable Learning for Large-class Few-Shot Classification
Few-shot image classification is challenging due to the lack of ample sa...
read it
-
Cooperative Heterogeneous Deep Reinforcement Learning
Numerous deep reinforcement learning agents have been proposed, and each...
read it
-
RatE: Relation-Adaptive Translating Embedding for Knowledge Graph Completion
Many graph embedding approaches have been proposed for knowledge graph c...
read it
-
Attribute Propagation Network for Graph Zero-shot Learning
The goal of zero-shot learning (ZSL) is to train a model to classify sam...
read it
-
BiteNet: Bidirectional Temporal Encoder Network to Predict Medical Outcomes
Electronic health records (EHRs) are longitudinal records of a patient's...
read it
-
Many-Class Few-Shot Learning on Multi-Granularity Class Hierarchy
We study many-class few-shot (MCFS) problem in both supervised learning ...
read it
-
A Universal Representation Transformer Layer for Few-Shot Image Classification
Few-shot classification aims to recognize unseen classes when presented ...
read it
-
Interpretable Time-series Classification on Few-shot Samples
Recent few-shot learning works focus on training a model with prior meta...
read it
-
Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks
Modeling multivariate time series has long been a subject that has attra...
read it
-
Multi-Center Federated Learning
Federated learning has received great attention for its capability to tr...
read it
-
Semantic Triple Encoder for Fast Open-Set Link Prediction
We improve both the open-set generalization and efficiency of link predi...
read it
-
Exploiting Structured Knowledge in Text via Graph-Guided Representation Learning
In this work, we aim at equipping pre-trained language models with struc...
read it
-
Rethinking 1D-CNN for Time Series Classification: A Stronger Baseline
For time series classification task using 1D-CNN, the selection of kerne...
read it
-
Self-Attention Enhanced Selective Gate with Entity-Aware Embedding for Distantly Supervised Relation Extraction
Distantly supervised relation extraction intrinsically suffers from nois...
read it
-
Suicidal Ideation Detection: A Review of Machine Learning Methods and Applications
Suicide is a critical issue in the modern society. Early detection and p...
read it
-
Multi-Task Learning for Conversational Question Answering over a Large-Scale Knowledge Base
We consider the problem of conversational question answering over a larg...
read it
-
Temporal Self-Attention Network for Medical Concept Embedding
In longitudinal electronic health records (EHRs), the event records of a...
read it
-
Learning to Propagate for Graph Meta-Learning
Meta-learning extracts the common knowledge acquired from learning diffe...
read it
-
Effective Search of Logical Forms for Weakly Supervised Knowledge-Based Question Answering
Many algorithms for Knowledge-Based Question Answering (KBQA) depend on ...
read it
-
Attributed Graph Clustering: A Deep Attentional Embedding Approach
Graph clustering is a fundamental task which discovers communities or gr...
read it
-
Graph WaveNet for Deep Spatial-Temporal Graph Modeling
Spatial-temporal graph modeling is an important task to analyze the spat...
read it
-
Decentralized Learning with Average Difference Aggregation for Proactive Online Social Care
The Internet and the Web are being increasingly used in proactive social...
read it
-
Prototype Propagation Networks (PPN) for Weakly-supervised Few-shot Learning on Category Graph
A variety of machine learning applications expect to achieve rapid learn...
read it
-
DAGCN: Dual Attention Graph Convolutional Networks
Graph convolutional networks (GCNs) have recently become one of the most...
read it
-
Learning Graph Embedding with Adversarial Training Methods
Graph embedding aims to transfer a graph into vectors to facilitate subs...
read it
-
A Comprehensive Survey on Graph Neural Networks
Deep learning has revolutionized many machine learning tasks in recent y...
read it
-
Learning Private Neural Language Modeling with Attentive Aggregation
Mobile keyboard suggestion is typically regarded as a word-level languag...
read it
-
Distributionally Robust Semi-Supervised Learning for People-Centric Sensing
Semi-supervised learning is crucial for alleviating labelling burdens in...
read it
-
NeuRec: On Nonlinear Transformation for Personalized Ranking
Modelling user-item interaction patterns is an important task for person...
read it
-
Fast Directional Self-Attention Mechanism
In this paper, we propose a self-attention mechanism, dubbed "fast direc...
read it
-
Multi-modality Sensor Data Classification with Selective Attention
Multimodal wearable sensor data classification plays an important role i...
read it
-
Bi-Directional Block Self-Attention for Fast and Memory-Efficient Sequence Modeling
Recurrent neural networks (RNN), convolutional neural networks (CNN) and...
read it
-
Adversarially Regularized Graph Autoencoder
Graph embedding is an effective method to represent graph data in a low ...
read it
-
Reinforced Self-Attention Network: a Hybrid of Hard and Soft Attention for Sequence Modeling
Many natural language processing tasks solely rely on sparse dependencie...
read it
-
Predicting Rich Drug-Drug Interactions via Biomedical Knowledge Graphs and Text Jointly Embedding
Minimizing adverse reactions caused by drug-drug interactions has always...
read it
-
Safe Medicine Recommendation via Medical Knowledge Graph Embedding
Most of the existing medicine recommendation systems that are mainly bas...
read it
-
DiSAN: Directional Self-Attention Network for RNN/CNN-Free Language Understanding
Recurrent neural nets (RNN) and convolutional neural nets (CNN) are wide...
read it
-
Dynamic Concept Composition for Zero-Example Event Detection
In this paper, we focus on automatically detecting events in unconstrain...
read it