内容 类型 开始时间 截止时间 备注

A brief review on multi-task learning Kim-Han Thung 1 Chong-Yaw Wee 2 9/17/2019 9:56:31 PM 9/23/2019 9:09:38 AM -

An_Overview_of_MultiTask_Learning_in_Deep_Neural_Networks Sebastian Ruder 9/23/2019 9:11:11 AM 9/24/2019 1:59:12 PM -

Deep_Asymmetric_MT_Feature_Learning(ICML18) Hae Beom Lee 1 2 Eunho Yang 3 2 Sung Ju Hwang 3 2 9/24/2019 2:52:43 PM 9/26/2019 12:34:20 PM -

Gradient_Normalization_for_Adaptive_Loss_Balancing_in_Deep_Multitask_Networks Zhao Chen 1 Vijay Badrinarayanan 1 Chen-Yu Lee 1 Andrew Rabinovich 1 9/26/2019 12:38:55 PM 9/29/2019 1:49:46 PM -

Pseudo-task_Augmentation_From_Deep_Multitask_Learning_to_Intratask_Sharing_and_Back Elliot Meyerson 1 2 Risto Miikkulainen 1 2 9/29/2019 1:54:57 PM 10/8/2019 3:36:44 PM -

Modeling_Task_Relationships_in_MTL_with_Multi-gate_Mixture-of-Experts Jiaqi Ma 1∗ , Zhe Zhao 2 , Xinyang Yi 2 , Jilin Chen 2 , Lichan Hong 2 , Ed H. Chi 2 10/8/2019 3:36:50 PM 10/10/2019 1:28:20 PM -

Multi-Task_Networks_With_Universe_Group_and_Task_Feature_Learning Shiva Pentyala ∗ 10/10/2019 1:29:11 PM 10/12/2019 10:06:40 AM -

Deep_Multi-Task_Learning_with_Adversarial-and-Cooperative_Nets Pei Yang 1,2 , Qi Tan 3 , Jieping Ye 4 , Hanghang Tong 2 and Jingrui He 2 10/12/2019 10:12:00 AM 10/14/2019 2:24:28 PM -

Multiple Relational Attention Network for Multi-task Learning Jiejie Zhao 1 , Bowen Du 1∗ , Leilei Sun 1 , Fuzhen Zhuang 2,3 , Weifeng Lv 1 , Hui Xiong 4 10/14/2019 2:26:18 PM 10/15/2019 8:59:33 PM -

Multi-Task Learning as Multi-Objective Optimization Ozan Sener Intel Labs Vladlen Koltun 10/15/2019 9:03:37 PM 10/17/2019 3:36:18 PM -

Deep Decentralized Multi-task Multi-Agent Reinforcement Learning under Partial Observability Shayegan Omidshafiei 1 Jason Pazis 1 Christopher Amato 2 Jonathan P. How 1 John Vian 3 10/17/2019 3:36:22 PM 10/17/2019 7:36:14 PM 约等于没看,不是研究领域相关

Dynamic Multi-Task Learning with Convolutional Neural Network Yuchun Fang 1 , Zhengyan Ma 1 , Zhaoxiang Zhang 2,3,4,5∗ , Xu-Yao Zhang 3 , Xiang Bai 6 10/17/2019 7:39:50 PM 10/18/2019 11:46:30 AM -

Cross-stitch Networks for Multi-task Learning Ishan Misra ∗ Abhinav Shrivastava ∗ Abhinav Gupta Martial Hebert 10/18/2019 11:48:36 AM 10/21/2019 3:27:53 PM -

Deep multi-task learning with low level tasks supervised at lower layers Anders Søgaard Yoav Goldberg 10/21/2019 3:31:05 PM 10/21/2019 10:12:30 PM -

Routing networks adaptive selection of non-linear functions for multi-task learning Clemens Rosenbaum 10/21/2019 10:19:01 PM 10/22/2019 3:36:53 PM -

deep_multi-task_representation_learning_a_tensor_factorisation_approach Yongxin Yang, Timothy M. Hospedales 10/22/2019 3:36:57 PM 10/23/2019 3:36:57 PM -

trace norm regularised deep multi-task learning(ICLR2017) Yongxin Yang, Timothy M. Hospedales 10/23/2019 3:36:57 PM 10/24/2019 11:32:12 AM -

intelligent synapses for multi-task and transfer learning Friedemann Zenke ∗ , Ben Poole ∗ , Surya Ganguli 10/24/2019 11:34:26 AM 10/24/2019 3:29:15 PM 与所研究领域偏了

重读三篇论文 - 11/5/2019 3:46:00 PM - -

Deep-MTL方法型论文 方法 9/17/2019 9:56:31 PM 10/24/2019 3:29:15 PM 18篇论文

深度学习基础-花书 知识 10/24/2019 3:29:15 PM 10/29/2019 7:35:10 PM 线代、概率论、数值计算

深度学习基础-花书 第二章 线代 10/24/2019 3:29:15 PM 10/29/2019 7:35:10 PM -

深度学习基础-花书 第三章 概率论 10/24/2019 3:29:15 PM 10/29/2019 7:35:10 PM -

深度学习基础-花书 第四章 数值计算 10/24/2019 3:29:15 PM 10/29/2019 7:35:10 PM -

Deep-MTL应用型论文 应用 10/29/2019 7:36:01 PM 10/30/2019 7:36:01 PM ICML的论文,侧重NLP和CV

play and never play ss 10/30/2019 7:36:01 PM 11/03/2019 12:00:00 PM Never until have one paper

Deep-MTL应用型论文 应用 11/4/2019 10:02:48 AM 11/5/2019 2:39:54 PM -

ICML - 10/29/2019 7:36:01 PM 11/5/2019 2:41:10 PM -

Two-Level Attention with Multi-task Learning for Facial Emotion Estimation Xiaohua Wang 11/5/2019 2:42:03 PM 11/5/2019 3:39:12 PM 与研究点无关

Deep-Multi-Task with Two-level attention 应用 11/5/2019 2:40:38 PM 11/5/2019 3:41:51 PM 确定与想法无关

重读Attention三篇论文 方法 11/5/2019 2:40:38 PM - 《Multiple_Relational_Attention_Network_for_Multi-task_Learning》《Modeling_Task_Relationships_in_MTL_with_Multi-gate_Mixture-of-Experts》《Attention is all you need》

Pytorch深度研究 代码 11/5/2019 2:40:38 PM 11/26/2019 1:12:48 PM 将几篇论文仔细研究了一遍;实现了第一个自己的Pytorch模型TLANModel;封装了一些网络训练的工具,包括训练、测试、打印、callback、checkpoint等提高代码可用性;后面只需要将模型实现即可

新一轮的论文阅读 方法 11/26/2019 1:14:51 PM - 进行新一轮的论文阅读

Information Cascades Modeling via Deep Multi-Task Learning 方法 11/26/2019 1:32:18 PM 11/26/2019 3:05:24 PM 信息级联预测相关,但是不是多任务学习为主体,其中的Attention是正常的attention

CVPR两篇论文看一哈 方法 11/26/2019 3:09:19 PM 11/29/2019 2:59:50 PM 23. CVPR19. Gao_NDDR-CNN_Layerwise_Feature_Fusing_in_Multi-Task_CNNs_by_Neural_Discriminative_CVPR_2019_paper 22. CVPR19. Liu_End-To-End_Multi-Task_Learning_With_Attention_CVPR_2019_paper

Latent Multi-Task Architecture Learning 方法 11/29/2019 3:00:58 PM - AAAI的论文

Meta Multi-Task Learning for Sequence Modeling 方法 11/29/2019 3:00:58 PM - AAAI的论文,算是应用

Learning Multiple Tasks with Multilinear Relationship Networks 方法 11/29/2019 3:00:58 PM - NIPS老论文

Learning Tree Structure in Multi-Task Learning 方法 11/29/2019 3:00:58 PM 12/1/2019 1:32:33 PM KDD15老论文