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Learning Transferable Features with Deep Adaptation Networks


Learning Transferable Features with Deep Adaptation Networks


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# Title Author Topic Medium Score
1 Transfer Learning Materials Irene Li 1559
2 Performance guarantees for transferring representations Daniel McNamara 1323
3 Introduction to Convolutional Neural Networks Jon Shlens 1193
4 Transfer Learning — part 2 Ilya Prokin 9999
5 AutoML for large scale image classification and object detection Barret Zoph, Vijay Vasudevan, Jonathon Shlens, Quoc Le 713
6 Current & Future NLP Research A Few Random Remarks Jason Eisner 1108
7 A Comprehensive Hands-on Guide to Transfer Learning with Real-World Applications in Deep Learning Dipanjan (DJ) Sarkar 9999
8 Awesome - Most Cited Deep Learning Papers Terry Taewoong Um 1183
9 Secure Learning in Adversarial Deep Learning Networks Bo Li 746
10 On sentence representations, pt. 1: what can you fit into a single #$!%@*&% blog post? N/A 1188
11 Transfer Learning Andrej Karpathy 1183
12 An Overview of Multi-Task Learning in Deep Neural Networks Sebastian Ruder 1311
13 State-of-the-art Result for all Machine Learning Problems Reddit Sota 1181
14 Introducing state of the art text classification with universal language models Jeremy Howard, Sebastian Ruder 1162
15 Transfer Learning and Multi-Task Learning Sergey Levine 1559
16 One-Shot Imitation Learning Yan Duan, Marcin Andrychowicz, Bradly Stadie, Jonathan Ho, Jonas Sc... 9999
17 Transfer Learning - The Next Frontier for ML Sebastian Ruder 9999
18 Transfer Learning — part 1 Ilya Prokin 9999
19 Transfer Learning — part 1 Ilya Prokin 9999
20 Summaries and notes on Deep Learning research papers Denny Britz 1183
21 Top 10 IPython Notebook Tutorials for Data Science and Machine Learning Matthew Mayo 1559
22 Correlation Alignment for Domain Adaptation Baochen Sun, Jiashi Feng, Kate Saenko 1134
23 Transfer Learning - Machine Learnings Next Frontier Sebastian Ruder 1323
24 Transfer Learning - Machine Learning's Next Frontier Sebastian Ruder 9999
25 Deep Learning Reading List Caglar Gulcehre 1181
26 Deep Learning Reading List Caglar Gulcehre 1181
27 Deep Learning for Natural Language Processing Tianchuan Du, Vijay K. Shanker 1181
28 Transfer Learning for NLP Sebastian Ruder 9999
29 Transfer learning from pre-trained models Pedro Marcelino 1323
30 Deep Learning for Natural Language Processing and Machine Translation Kevin Duh 1143
31 Pretrained ConvNets for pytorch Remi Cadene 744
32 Speaker ID I Javier Hernando 1542
33 An Overview of Proxy-label Approaches for Semi-supervised Learning Sebastian Ruder 1203
34 Networks, Reinforcement, Relational, Recurrent, Learning Petar Velickovic 1800
35 An overview of proxy-label approaches for semi-supervised learning None
36 Deep multi-task learning with low level tasks supervised at lower layers Anders Søgaard, Yoav Goldberg 9999
37 Multi-domain Neural Network Language Generation for Spoken Dialogue Systems Tsung-Hsien Wen, Milica Gaši?, Nikola Mrkši?, Lina M. Rojas-Barahon... 9999
38 Introduction and Course Overview Sergey Levine 1009
39 Keras-GAN Erick Lindernoren 1180
40 NLP's ImageNet moment has arrived Sebastian Ruder 1338
41 Parametric Speech Synthesis Antonio Bonafonte 1543
42 Mathematics of Deep Learning Raja Giryes, René Vidal 1183
43 Deep learning architecture diagrams Zygmunt Z 1181
44 PyTorch-GAN Jun-Yan Zhu, Richard Zhang, Deepak Pathak, Trevor Darrell, Alexei A... 1189
45 Multilingual Sentiment and Subjectivity Analysis Rada Mihalcea, Carmen Banea, Janyce Wiebe 1122
46 All Code Implementations for NIPS 2016 papers peterkuharvarduk 1559
47 Optimization as a Model for Few-Shot Learning Hugo Larochelle 187
48 Machine Translation Reading List THUNLP-MT 1035
49 A Little Review of Domain Adaptation in 2017 Arthur Pesah 1323
50 Neural Semi-supervised Learning under Domain Shift Sebastian Ruder 1203