Connectionists: Keeping Track of Latest Developments in AI with aipano.cse.ust.hk

Sat Feb 9 23:20:25 EST 2019

The website**aipano.cse.ust.hkprovides a topic-based index to research 
papers published since 2000 at major AI and Machine Learning venues. It 
lets you keep track of the latest developments in AI and offers a fast 
way to find recent highly cited papers on various topics. Here are few 
screenshots of a session with the system to illustrate its functionalities.

*Knowing the General Trends*: At the front page, you can perform sort by 
“popularity: past year” to find the topics with the largest numbers of 
papers in the past year (2018):

If you sort the topics by “trend: 3 years”, you will find the topics 
with the fastest increase in popularity the past three years:

It is interesting to find the topic “causal, causality, causal-model, 
causal-inference, …” at the 6^th position although the total number of 
papers on the topic is relatively small.

**

*Keeping Track of the Latest Developments on a Topic*: By cicking on a 
topic, you will see its trend curve and, more importantly, papers on the 
topic that you might want to add to your reading list. Here is the trend 
curve of the topic “layer, convolutional, convolutional-neural, cnn, 
pretrained, …”:

Nearly 3,000 papers were published on the topic in 2018! Here are the 
most cited ones, which are good candidates for a reading list:

CVPR

2018

Squeeze-and-Excitation Networks 
<http://openaccess.thecvf.com/content_cvpr_2018/papers/Hu_Squeeze-and-Excitation_Networks_CVPR_2018_paper.pdf>

425

CVPR

2018

Interpretable Convolutional Neural Networks 
<http://openaccess.thecvf.com/content_cvpr_2018/papers/Zhang_Interpretable_Convolutional_Neural_CVPR_2018_paper.pdf>

303

JMLR

2018

Quantized Neural Networks: Training Neural Networks with Low Precision 
Weights and Activations 
<http://www.jmlr.org/papers/volume18/16-456/16-456.pdf>

266

CVPR

2018

Learning Transferable Architectures for Scalable Image Recognition 
<http://openaccess.thecvf.com/content_cvpr_2018/papers/Zoph_Learning_Transferable_Architectures_CVPR_2018_paper.pdf>

252

ICML

2018

Obfuscated Gradients Give a False Sense of Security: Circumventing 
Defenses to Adversarial Examples 
<http://proceedings.mlr.press/v80/athalye18a/athalye18a.pdf>

176

It is possible to narrow down the candidates by requiring key words. 
Here are the most cited papers in 2018 that contain the key word 
“adversarial” in title.

ICML

2018

Obfuscated Gradients Give a False Sense of Security: Circumventing 
Defenses to Adversarial Examples 
<http://proceedings.mlr.press/v80/athalye18a/athalye18a.pdf>

176

ICML

2018

CyCADA: Cycle-Consistent Adversarial Domain Adaptation 
<http://proceedings.mlr.press/v80/hoffman18a/hoffman18a.pdf>

111

ICLR

2018

Towards Deep Learning Models Resistant to Adversarial Attacks 
<https://openreview.net/pdf?id=rJzIBfZAb>

77

ICML

2018

Provable Defenses against Adversarial Examples via the Convex Outer 
Adversarial Polytope <http://proceedings.mlr.press/v80/wong18a/wong18a.pdf>

68

CVPR

2018

AttnGAN: Fine-Grained Text to Image Generation With Attentional 
Generative Adversarial Networks 
<http://openaccess.thecvf.com/content_cvpr_2018/papers/Xu_AttnGAN_Fine-Grained_Text_CVPR_2018_paper.pdf>

45

If you are interested in reinforcement learning, you might want to take 
a look at the topic“agent, mdp, reward, action, reinforcement-learning, …”:

Here are the most cited papers on the topic published in 2018:

_ACL_

_2018_

_Deep Reinforcement Learning for NLP <http://aclweb.org/anthology/P18-5007>_

_295_

_AAAI_

_2018_

_Deep Reinforcement Learning That Matters 
<https://aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/16669/16677>_

_133_

_AAAI_

_2018_

_Rainbow: Combining Improvements in Deep Reinforcement Learning 
<https://aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/17204/16680>_

_104_

_AAAI_

_2018_

_Counterfactual Multi-Agent Policy Gradients 
<https://aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/17193/16614>_

_79_

_AAAI_

_2018_

_Emergence of Grounded Compositional Language in Multi-Agent Populations 
<https://aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/17007/15846>_

_76_

**

*An Investigation into Work on Causality*: Here is the trend curve of 
the topic “causal, causality, causal-model, causal-inference, …”:

Here are the most cited papers on the topic in 2018, 2017 and since 2000:

*Series*

*Year*

*Title*

*Cited*

AAAI

2018

Measuring Conditional Independence by Independent Residuals: Theoretical 
Results and Application in Causal Discovery 
<https://aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/16798/15910>

201

AAAI

2018

Fair Inference on Outcomes 
<https://aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/16683/15898>

31

JMLR

2018

Learning Certifiably Optimal Rule Lists for Categorical Data 
<http://www.jmlr.org/papers/volume18/17-716/17-716.pdf>

29

ICML

2018

Neural Relational Inference for Interacting Systems 
<http://proceedings.mlr.press/v80/kipf18a/kipf18a.pdf>

20

JMLR

2018

Uncovering Causality from Multivariate Hawkes Integrated Cumulants 
<http://www.jmlr.org/papers/volume18/17-284/17-284.pdf>

15

ICLR

2017

A Compositional Object-Based Approach to Learning Physical Dynamics 
<https://openreview.net/pdf?id=Bkab5dqxe>

40

EMNLP

2017

A causal framework for explaining the predictions of black-box 
sequence-to-sequence models <http://aclweb.org/anthology/D17-1042>

29

ICLR

2017

Revisiting Classifier Two-Sample Tests 
<https://openreview.net/pdf?id=SJkXfE5xx>

25

NIPS

2017

Avoiding Discrimination through Causal Reasoning 
<http://papers.nips.cc/paper/6668-avoiding-discrimination-through-causal-reasoning.pdf>

24

NIPS

2017

Counterfactual Fairness 
<http://papers.nips.cc/paper/6995-counterfactual-fairness.pdf>

22

UAI

2001

Direct and Indirect Effects <https://dslpitt.org/papers/01/p411-pearl.pdf>

889

UAI

2001

Causes and Explanations: A Structural-Model Approach --- Part 1: Causes 
<https://dslpitt.org/papers/01/p194-halpern.pdf>

648

JMLR

2006

A Linear Non-Gaussian Acyclic Model for Causal Discovery 
<http://www.jmlr.org/papers/volume7/shimizu06a/shimizu06a.pdf>

585

UAI

2007

Causal Bounds and Instruments 
<https://dslpitt.org/papers/07/p310-ramsahai.pdf>

567

JMLR

2007

Estimating High-Dimensional Directed Acyclic Graphs with the 
PC-Algorithm <http://www.jmlr.org/papers/volume8/kalisch07a/kalisch07a.pdf>

500

*Appearng Smart in Front of Students*: A student is interested in 
zero-shot learning. I searched for “zero-shot” within several relevant 
topics and found the following papers:

_From topic “transfer-learning, multitask-learning, multitask, …”:_

ICML

2015

An embarrassingly simple approach to zero-shot learning 
<http://jmlr.org/proceedings/papers/v37/romera-paredes15.pdf>

287

CVPR

2016

Synthesized Classifiers for Zero-Shot Learning 
<http://ieeexplore.ieee.org/stampPDF/getPDF.jsp?arnumber=7780944>

90

AAAI

2016

Transductive Zero-Shot Recognition via Shared Model Space Learning 
<http://www.aaai.org/ocs/index.php/AAAI/AAAI16/paper/view/11975/12120>

42

IJCAI

2016

Using Task Features for Zero-Shot Knowledge Transfer in Lifelong 
Learning <http://ijcai.org/Proceedings/16/Papers/232.pdf>

32

CVPR

2017

Learning a Deep Embedding Model for Zero-Shot Learning 
<http://openaccess.thecvf.com/content_cvpr_2017/papers/Zhang_Learning_a_Deep_CVPR_2017_paper.pdf>

28

_From topic “lstm, recurrent-neural, rnn, recurrent, …”:_

CVPR

2016

Multi-cue Zero-Shot Learning with Strong Supervision 
<http://ieeexplore.ieee.org/stampPDF/getPDF.jsp?arnumber=7780383>

39

CVPR

2017

Learning a Deep Embedding Model for Zero-Shot Learning 
<http://openaccess.thecvf.com/content_cvpr_2017/papers/Zhang_Learning_a_Deep_CVPR_2017_paper.pdf>

28

ICML

2017

Zero-Shot Task Generalization with Multi-Task Deep Reinforcement 
Learning <http://proceedings.mlr.press/v70/oh17a/oh17a.pdf>

16

EMNLP

2017

Zero-Shot Activity Recognition with Verb Attribute Induction 
<http://aclweb.org/anthology/D17-1099>

10

CVPR

2018

Multi-Label Zero-Shot Learning With Structured Knowledge Graphs 
<http://openaccess.thecvf.com/content_cvpr_2018/papers/Lee_Multi-Label_Zero-Shot_Learning_CVPR_2018_paper.pdf>

6

_From topic “layer, convolutional, convolutional-neural, cnn, …”:_

NIPS

2013

Zero-Shot Learning Through Cross-Modal Transfer 
<http://papers.nips.cc/paper/5027-zero-shot-learning-through-cross-modal-transfer.pdf>

532

ICLR

2014

Zero-Shot Learning by Convex Combination of Semantic Embeddings 
<http://arxiv.org/abs/1312.5650>

222

ACL

2015

Hubness and Pollution: Delving into Cross-Space Mapping for Zero-Shot 
Learning <http://aclweb.org/anthology/P15-1027>

67

IJCAI

2015

Semantic Concept Discovery for Large-Scale Zero-Shot Event Detection 
<http://ijcai.org/Proceedings/15/Papers/316.pdf>

56

CVPR

2016

Multi-cue Zero-Shot Learning with Strong Supervision 
<http://ieeexplore.ieee.org/stampPDF/getPDF.jsp?arnumber=7780383>

39

*Other Uses*: Do your ever wonder how AIJ and JMLR differ in terms of 
topics? Here is what I found on aipanofor the past 5 years.

In summary, aipanoprovides a panoramic view of the AI literature. It 
lets you keep track of the latest developments in AI and offers a fast 
way to find recent highly cited papers on various topics.**

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