1 |
Codra: A Novel Discriminative Framework for Rhetorical Analysis |
Shafiq Joty, Giuseppe Carenini, Raymond T. Ng |
999 |
paper |
128.25 |
2 |
Similarity-driven Semantic Role Induction via Graph Partitioning |
Joel Lang, Mirella Lapata |
999 |
paper |
106.86 |
3 |
NLP’s generalization problem, and how researchers are tackling it |
Ana Marasovic |
711 |
resource |
98.22 |
4 |
Negated bio-events: analysis and identification |
Raheel Nawaz, Paul Thompson, Sophia Ananiadou |
999 |
paper |
96.93 |
5 |
Neural information retrieval: at the end of the early years |
Kezban Dilek Onal, Ye Zhang, Ismail Sengor Altingovde, Md Mustafizur Rahman, Pinar Karagoz, Alex Braylan, Brandon Dang, Heng-Lu Chang, Henna Kim, Quinten McNamara, Aaron Angert, Edward Banner, Vivek K |
232 |
survey |
96.22 |
6 |
An Overview of Multi-Task Learning in Deep Neural Networks |
Sebastian Ruder |
829 |
tutorial |
94.74 |
7 |
Highlights of EMNLP 2017: Exciting Datasets, Return of the Clusters, and More! |
Sebastian Ruder |
641 |
resource |
94.06 |
8 |
State-of-the-art neural coreference resolution for chatbots |
Thomas Wolf |
756 |
tutorial |
93.21 |
9 |
NLP's ImageNet moment has arrived |
Sebastian Ruder |
862 |
resource |
92.02 |
10 |
Word embeddings in 2017: Trends and future directions |
Sebastian Ruder |
721 |
resource |
86.56 |
11 |
Generative Models |
Andrej Karpathy, Pieter Abbeel, Greg Brockman, Peter Chen, Vicki Cheung, Rocky Duan, Ian Goodfellow, Durk Kingma, Jonathan Ho, Rein Houthooft, Tim Salimans, John Schulman, Ilya Sutskever, Wojciech Zar |
756 |
resource |
85.42 |
12 |
End-to-end speech |
Anthony Ndirango |
863 |
resource |
84.25 |
13 |
The 9 Deep Learning Papers You Need To Know About (Understanding CNNs Part 3) |
Adit Deshpande |
744 |
tutorial |
83.97 |
14 |
Some Highlights of MILA Deep Learning and Reinforcement Learning Summer Schools 2017 |
Mostafa Dehghani |
857 |
resource |
81.07 |
15 |
Deconstruction with Discrete Embeddings |
R2RT |
711 |
resource |
80.70 |
16 |
The data that transformed AI research—and possibly the world |
Dave Gershgorn |
107 |
resource |
80.55 |
17 |
ACL 2017 Report |
Yuta Kikuchi, Sosuke Kobayashi |
711 |
resource |
80.26 |
18 |
A Dozen Times Artificial Intelligence Startled the World |
Sumeet Agrawal |
811 |
resource |
80.12 |
19 |
Multi-Task Learning Objectives for Natural Language Processing |
Author Unknown |
133 |
resource |
79.65 |
20 |
Recent Advances in Document Summarization |
Jin-ge Yao, Xiaojun Wan, Jianguo Xiao |
421 |
survey |
78.39 |
21 |
Analyzing the Meaning of Sentences |
Steven Bird, Ewan Klein, Edward Loper |
721 |
course |
77.63 |
22 |
Automatic feature engineering using Generative Adversarial Networks |
Hamaad Shah |
711 |
resource |
76.66 |
23 |
Variational Inference using Implicit Models, Part I: Bayesian Logistic Regression |
Ferenc Huszár |
364 |
resource |
76.23 |
24 |
Learning about the world through video |
Moritz Mueller-Freitag |
811 |
resource |
76.16 |
25 |
Diversity and network coherence as indicators of interdisciplinarity: case studies in bionanoscience |
Ismael Rafols, Martin Meyer |
999 |
paper |
75.39 |
26 |
Gimli: open source and high-performance biomedical name recognition |
David Campos, Sergio Matos, Jose Oliveira |
999 |
paper |
75.32 |
27 |
A general framework for analysing diversity in science, technology and society |
Andy Stirling |
999 |
paper |
74.84 |
28 |
Learning when to skim and when to read |
Alexander Rosenberg Johansen, Bryan McCann, James Bradbury, Richard Socher |
713 |
tutorial |
74.30 |
29 |
Recommendation in Industry |
Xavier Amatriain |
999 |
tutorial |
74.18 |
30 |
Rohan #2: Artificial intelligence, ?Progress/?Time |
Rohan Kapur |
811 |
tutorial |
73.71 |
31 |
Fueling the Gold Rush: The Greatest Public Datasets for AI |
Luke de Oliveira |
107 |
resource |
73.67 |
32 |
Summaries and notes on Deep Learning research papers |
Denny Britz |
713 |
resource |
73.46 |
33 |
Deep Learning Achievements Over the Past Year |
Eduard Tyantov |
711 |
resource |
73.23 |
34 |
Lisbon Machine Learning Summer School Highlights |
Sebastian Ruder |
107 |
resource |
72.98 |
35 |
New wave of deep neural networks |
Alfredo Canziani, Abishek Chaurasia, Eugenio Culurciello |
713 |
tutorial |
72.79 |
36 |
Transfer Learning - Machine Learnings Next Frontier |
Sebastian Ruder |
978 |
tutorial |
72.60 |
37 |
Using Artificial Intelligence to Augment Human Intelligence |
Shan Carter, Michael Nielsen |
811 |
resource |
72.46 |
38 |
Must Know Tips/Tricks in Deep Neural Networks |
Xiu-Shen Wei |
713 |
tutorial |
72.08 |
39 |
A new kind of deep neural networks |
Eugenio Culurciello |
711 |
resource |
71.86 |
40 |
Tombones Computer Vision Blog |
Tomasz Malisiewicz |
958 |
resource |
71.01 |
41 |
[ICLR][NVIDIA] Progressive generative adversarial networks (GANs) explained with art forgery?—?Part I. |
Brendan Whitaker |
713 |
resource |
70.89 |
42 |
Four deep learning trends from ACL 2017: Part 1 |
Abigail See |
713 |
resource |
70.01 |
43 |
Text to Video Generation |
Antonia Antonova |
713 |
resource |
70.00 |
44 |
From GAN to WGAN |
Lilian Weng |
711 |
resource |
69.93 |
45 |
Recurrent Neural Networks |
Stephen Grossberg |
741 |
paper |
69.70 |
46 |
Deep Learning in NLP |
Vered Shwartz |
711 |
resource |
69.68 |
47 |
On word embeddings - Part 2: Approximating the Softmax |
Sebastian Ruder |
721 |
tutorial |
69.34 |
48 |
Parsing English in 500 Lines of Python |
Matthew Honnibal |
242 |
tutorial |
69.24 |
49 |
Revisiting Deep Learning as a Non-Equilibrium Process |
Carlos E. Perez |
711 |
resource |
69.13 |
50 |
Machine Learning for Humans |
Vishal Maini, Samer Sabri |
134 |
tutorial |
68.79 |
51 |
ResNet, AlexNet, VGG, Inception: Understanding various architectures of Convolutional Networks |
Koustubh |
744 |
resource |
68.39 |
52 |
FigureQA: an annotated figure dataset for visual reasoning |
Author Unknown |
862 |
resource |
68.10 |
53 |
Deep Learning for NLP: An Overview of Recent Trends |
Elvis |
711 |
resource |
67.87 |
54 |
Understanding and Implementing CycleGAN in TensorFlow |
Hardik Bansal, Archit Rathore |
731 |
tutorial |
67.83 |
55 |
GANs from Scratch 1: A deep introduction. With code in PyTorch and TensorFlow |
Diego Gomez Mosquera |
731 |
resource |
67.42 |
56 |
CoNLL-2015 Shared Task: Shallow Discourse Parsing |
Te Rutherford |
242 |
resource |
67.12 |
57 |
Towards data set augmentation with GANs |
Pedro Ferreira |
713 |
resource |
66.83 |
58 |
Ideas on interpreting machine learning |
Patrick Hall, Wen Phan, SriSatish Ambati |
134 |
tutorial |
66.79 |
59 |
Brief History of Machine Learning |
Eren Golge |
107 |
tutorial |
66.69 |
60 |
A social network's changing statistical properties and the quality of human innovation |
Brian Uzzi |
999 |
paper |
66.64 |
61 |
Convolutional neural networks, Part 1 |
Adrian Colyer |
744 |
resource |
66.50 |
62 |
Uncovering the Intuition behind Capsule Networks and Inverse Graphics: Part I |
Tanay Kothari |
641 |
resource |
65.91 |
63 |
Uncovering the Intuition behind Capsule Networks and Inverse Graphics |
Tanay Kothari |
711 |
resource |
65.89 |
64 |
How do we capture structure in relational data? |
Matthew Das Sarma |
711 |
resource |
65.87 |
65 |
Deep Learning from first principles in Python, R and Octave – Part 3 |
Tinniam V Ganesh |
711 |
resource |
65.85 |
66 |
Ensemble Learning to Improve Machine Learning Results |
Vadim Smolyakov |
999 |
resource |
65.70 |
67 |
Rohan & Lenny #3: Recurrent Neural Networks & LSTMs |
Rohan Kapur |
741 |
tutorial |
65.68 |
68 |
Natural Language Processing in Artificial Intelligence is almost human-level accurate. Worse yet, it gets smart! |
Rafal |
133 |
tutorial |
65.55 |
69 |
ICML+ACL’18: Structure Back in Play, Translation Wants More Context |
Andre Martins |
956 |
resource |
65.36 |
70 |
A survey of transfer learning |
Karl Weiss, Taghi M. Khoshgoftaar and DingDing Wang |
978 |
resource |
65.18 |
71 |
An Overview of Proxy-label Approaches for Semi-supervised Learning |
Sebastian Ruder |
581 |
resource |
65.13 |
72 |
An On-device Deep Neural Network for Face Detection |
Computer Vision Machine Learning Team |
862 |
resource |
65.06 |
73 |
Recurrent Neural Networks Tutorial, Part 1 - Introduction to RNNs |
Denny Britz |
741 |
tutorial |
65.05 |
74 |
Clustering cliques for graph-based summarization of the biomedical research literature |
Han Zhang, Marcelo Fiszman, Dongwook Shin, Bartomiej Wilkowski, Thomas Rindflesch |
999 |
paper |
64.97 |
75 |
Rohan & Lenny #2: Convolutional Neural Networks |
Lenny Khazan |
744 |
tutorial |
64.94 |
76 |
Open Machine Learning Course. Topic 5. Bagging and Random Forest |
Yury Kashnitskiy |
711 |
resource |
64.91 |