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Multi-Task Learning

2,582 papers

Papers per year

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Papers

End-to-End Game-Focused Learning of Adversary Behavior in Security Games AAAI 2020
Tensorized LSTM with Adaptive Shared Memory for Learning Trends in Multivariate Time Series AAAI 2020
MIPaaL: Mixed Integer Program as a Layer AAAI 2020
Adapting Stable Matchings to Evolving Preferences AAAI 2020
Towards Socially Responsible AI: Cognitive Bias-Aware Multi-Objective Learning AAAI 2020
FastLAS: Scalable Inductive Logic Programming Incorporating Domain-Specific Optimisation Criteria AAAI 2020
COTSAE: CO-Training of Structure and Attribute Embeddings for Entity Alignment AAAI 2020
Multi-View Clustering in Latent Embedding Space AAAI 2020
Forgetting to Learn Logic Programs AAAI 2020
Gradient-Aware Model-Based Policy Search AAAI 2020
Potential Passenger Flow Prediction: A Novel Study for Urban Transportation Development AAAI 2020
IWE-Net: Instance Weight Network for Locating Negative Comments and its application to improve Traffic User Experience AAAI 2020
An Attention-Based Graph Neural Network for Heterogeneous Structural Learning AAAI 2020
Query-Driven Multi-Instance Learning AAAI 2020
Maximum Margin Multi-Dimensional Classification AAAI 2020
Coupled-View Deep Classifier Learning from Multiple Noisy Annotators AAAI 2020
Adaptive Activation Network and Functional Regularization for Efficient and Flexible Deep Multi-Task Learning AAAI 2020
Online Planner Selection with Graph Neural Networks and Adaptive Scheduling AAAI 2020
EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs AAAI 2020
Learning Efficient Representations for Fake Speech Detection AAAI 2020
Reinforcement Learning Based Meta-Path Discovery in Large-Scale Heterogeneous Information Networks AAAI 2020
Deep Embedded Complementary and Interactive Information for Multi-View Classification AAAI 2020
Two-Level Transformer and Auxiliary Coherence Modeling for Improved Text Segmentation AAAI 2020
Leveraging Multi-Token Entities in Document-Level Named Entity Recognition AAAI 2020
MMM: Multi-Stage Multi-Task Learning for Multi-Choice Reading Comprehension AAAI 2020