tDomain Generalization for Object Recognition with Multi-task Autoencoders
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Updated
Jan 30, 2020 - Python
tDomain Generalization for Object Recognition with Multi-task Autoencoders
Pruning, MTL, MOO benchmark repository
Investigation of how sampling strategies affect Selective Prediction performance in Multi Task Learning
Joint Structure Feature Exploration and Regularization for Multi-Task Graph Classification (TKDE 2016)
Learning tasks with orthogonal/disjoint supports
The repository for Abstract Dynamic Multi-tasking End-to-end Algorithm (ADMEA)
Unified of Segmentation and Object detection for Autonomous
We model the emotions evoked by videos in a different manner: instead of modeling the aggregated value we jointly model the emotions experienced by each viewer and the aggregated value using a multi-task learning approach. Concretely, we proposed two deep learning architectures: Single-Task (ST) architecture and Multi-Task (MT) architecture.
MTMAUNet: Multi-Task Multi-axis Attention UNet
Code for paper "MultiEmo: multi-task framework for emoji prediction"
Discrete-world as the name says
An implementation of the Multi-PCSF algorithm described in
Multi-task Rl with MCTS
Code for paper "Deep Reinforcement Learning based Multi-task Automated Channel Pruning for DNNs"
OSRL (Optimal Representation Learning in Multi-Task Bandits) comprises an algorithm that addresses the problem of sample complexity with fixed confidence in Multi-Task Bandit problems. Published at the Thirty-Seventh AAAI Conference on Artificial Intelligence (AAAI23)
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