<--- Back to Details
First PageDocument Content
Machine learning / Artificial intelligence / Computational neuroscience / Learning / Applied mathematics / Artificial neural networks / Cybernetics / Formal sciences / Deep learning / Convolutional neural network / Multi-task learning / Training /  test /  and validation sets
Date: 2018-05-23 20:17:13
Machine learning
Artificial intelligence
Computational neuroscience
Learning
Applied mathematics
Artificial neural networks
Cybernetics
Formal sciences
Deep learning
Convolutional neural network
Multi-task learning
Training
test
and validation sets

Universal Language Model Fine-tuning for Text Classification Jeremy Howard∗ fast.ai University of San Francisco

Add to Reading List

Source URL: arxiv.org

Download Document from Source Website

File Size: 956,46 KB

Share Document on Facebook

Similar Documents

Semi-supervised Multi-task Learning of Structured Prediction Models for Web Information Extraction Paramveer S. Dhillon S Sundararajan

Semi-supervised Multi-task Learning of Structured Prediction Models for Web Information Extraction Paramveer S. Dhillon S Sundararajan

DocID: 1unE8 - View Document

Multi-task Self-Supervised Visual Learning Carl Doersch† arXiv:1708.07860v1 [cs.CV] 25 Aug 2017  †

Multi-task Self-Supervised Visual Learning Carl Doersch† arXiv:1708.07860v1 [cs.CV] 25 Aug 2017 †

DocID: 1tDtE - View Document

Deep multi-task learning with low level tasks supervised at lower layers Anders Søgaard University of Copenhagen   Yoav Goldberg

Deep multi-task learning with low level tasks supervised at lower layers Anders Søgaard University of Copenhagen Yoav Goldberg

DocID: 1tg1g - View Document

DEEP NEURAL NETWORKS EMPLOYING MULTI-TASK LEARNING AND STACKED BOTTLENECK FEATURES FOR SPEECH SYNTHESIS Zhizheng Wu Cassia Valentini-Botinhao

DEEP NEURAL NETWORKS EMPLOYING MULTI-TASK LEARNING AND STACKED BOTTLENECK FEATURES FOR SPEECH SYNTHESIS Zhizheng Wu Cassia Valentini-Botinhao

DocID: 1rHBq - View Document

DEEP NEURAL NETWORKS EMPLOYING MULTI-TASK LEARNING AND STACKED BOTTLENECK FEATURES FOR SPEECH SYNTHESIS Zhizheng Wu Cassia Valentini-Botinhao

DEEP NEURAL NETWORKS EMPLOYING MULTI-TASK LEARNING AND STACKED BOTTLENECK FEATURES FOR SPEECH SYNTHESIS Zhizheng Wu Cassia Valentini-Botinhao

DocID: 1rj8C - View Document