Deep multi-task learning with low level tasks supervised at lower layers

2016-08-01 · ACL 2016 8 · Anders S{\o}gaard, Yoav Goldberg

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CCG Supertagging Chunking Domain Adaptation Multi-Task Learning

Methods 이 논문이 사용한 방법론

Hierarchical MTL Multi-task learning (MTL) introduces an inductive bias, based on a-priori relations between tasks: the trainable model is compelled to model more general dependencies by using the…

Multilingual Lexicalized Constituency Parsing with Word-Level Auxiliary Tasks

2017-04-01 · EACL 2017 4 · Maximin Coavoux, Beno{\^\i}t Crabb{\'e}

We introduce a constituency parser based on a bi-LSTM encoder adapted from recent work (Cross and Huang, 2016b; Kiperwasser and Goldberg, 2016), which can incorporate a lower level character biLSTM (Ballesteros et al., 2…

Constituency Parsing Morphological Analysis Morphological Tagging POS

Hierarchical Reinforcement Learning By Discovering Intrinsic Options

2021-01-16 · ICLR 2021 1 · Jesse Zhang, Haonan Yu, Wei Xu

We propose a hierarchical reinforcement learning method, HIDIO, that can learn task-agnostic options in a self-supervised manner while jointly learning to utilize them to solve sparse-reward tasks. Unlike current hierarc…

Hierarchical Reinforcement Learning reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

Joint Unsupervised and Supervised Training for Automatic Speech Recognition via Bilevel Optimization

2024-01-13 · A F M Saif, Xiaodong Cui, Han Shen, Songtao Lu 외

In this paper, we present a novel bilevel optimization-based training approach to training acoustic models for automatic speech recognition (ASR) tasks that we term {bi-level joint unsupervised and supervised training (B…

Automatic Speech Recognition Automatic Speech Recognition (ASR)Bilevel Optimization speech-recognition+1

When Multi-Task Learning Meets Partial Supervision: A Computer Vision Review

2023-07-25 · Maxime Fontana, Michael Spratling, Miaojing Shi

Multi-Task Learning (MTL) aims to learn multiple tasks simultaneously while exploiting their mutual relationships. By using shared resources to simultaneously calculate multiple outputs, this learning paradigm has the po…

Benchmarking Multi-Task Learning

Self-supervised Depth Denoising Using Lower- and Higher-quality RGB-D sensors

2020-09-10 · Akhmedkhan Shabanov, Ilya Krotov, Nikolay Chinaev, Vsevolod Poletaev 외

Consumer-level depth cameras and depth sensors embedded in mobile devices enable numerous applications, such as AR games and face identification. However, the quality of the captured depth is sometimes insufficient for 3…

3D Object Reconstruction 3D Reconstruction Denoising Face Identification+1