Deep Learning Day

Monday, August 20, 2018

KDD 2018 Deep Learning Day Call for Papers

The impact of deep learning in data science has of course been nothing less than transformative. Powered by the surge in modern compute capacities, widespread data availability, and advances in coding frameworks, deep neural networks are now ubiquitous. Deep methods yield state-of-the-art performance on many domains (computer vision, speech recognition and generation, natural language processing), and are still widening their lead as more research appears daily. But the last couple years, the field has matured, just as it was expanding rapidly. A debate is now happening between practitioners and theorists. Methods have clearly gotten more abstract, and the focus is shifting towards more rigorous and robust experiments, and more interpretative theory in order to further develop the great empirical successes that we see.

For the first time at KDD, we are organizing a full Deep Learning Day as a key event, specifically dedicated to the field. The goal will be to provide a clear, wide overview of recent developments in deep learning, including emerging topics deserving of more attention, such as graph convolutional neural networks or computational optimal transport. During the day, we will welcome many diverse and exciting invited speakers from world class research institutions.

Confirmed Speakers

Confirmed speakers for the DL day include:

Yan Liu
Yan Liu
University of Southern California
Andrej Karpathy
Andrej Karpathy
Tesla
Richard Socher
Richard Socher
Salesforce
Tamara Broderick
Tamara Broderick
MIT
Oriol Vinyals
Oriol Vinyals
Deepmind
Ali Rahimi
Ali Rahimi
Google
Kyunghyun Cho
Kyunghyun Cho
NYU
Le Song
Le Song
Georgia Tech
Qiaozhu Mei
Qiaozhu Mei
University of Michigan
Soumith Chintala
Soumith Chintala
Facebook

Schedule

8:00am - 8:30amTamara Broderick (MIT)
8:30am - 9:15amKeynote: Andrej Karpathy (Tesla)
9:15am - 9:45amQiaozhu Mei (UMich)
9:45am - 10:15amCoffee Break
10:15am - 10:45amKyunghyun Cho (NYU / Facebook)
10:45am - 11:30amKeynote: Oriol Vinyals (DeepMind)
11:30am - 12:00pmYan Liu (USC)
12:00pm - 1:00pmLunch
1:00pm - 1:30pmAli Rahimi (Google)
1:30pm - 2:15pmKeynote: Richard Socher (Salesforce / Stanford)
2:15pm - 2:30pmCoffee Break
2:30pm - 3:00pmSoumith Chintala (Facebook)
3:00pm - 3:30pmLe Song (GaTech)
3:30pm - 3:45pmCoffee Break
3:45pm - 4:30pmContributed Spotlights
7:00pm - 9:30pmPoster Session (With Research Track Posters)

Deep Learning Day Accepted Papers

  • A Peek Into the Hidden Layers of a Convolutional Neural Network Through a Factorization Lens

    Uday Singh Saini and Evangelos Papalexakis

    Download (PDF)

  • MTNet: A Neural Approach for Cross-Domain Recommendation with Unstructured Text

    Guangneng Hu

    Download (PDF)

  • Abstractive and Extractive Text Summarization using Document Context Vector and Recurrent Neural Networks

    Chandra Khatri, Gyanit Singh and Nish Parikh

    Download (PDF)

  • Predicting the Popularity of Online Content with Knowledge-enhanced Neural Networks

    Hongjian Dou, Wayne Xin Zhao, Yuanpei Zhao, Daxiang Dong, Ji-Rong Wen and Edward Y. Chang

    Download (PDF)

  • Five lessons from building a deep neural network recommender for marketplaces

    Simen Eide, Audun Mathias Øygard and Ning Zhou

    Download (PDF)

  • Deep Sleep: Convolutional Neural Networks for Predictive Modeling of Human Sleep Time-Signals

    Sarun Paisarnsrisomsuk, Michael Sokolovsky, Francisco Guerrero, Carolina Ruiz and Sergio Alvarez

    Download (PDF)

  • SGR: Self-Supervised Spectral Graph Representation Learning

    Anton Tsitsulin, Davide Mottin, Panagiotis Karras, Alexander Bronstein and Emmanuel Müller

    Download (PDF)

  • Representation Learning for Visual-Relational Knowledge Graphs

    Daniel Oñoro-Rubio, Mathias Niepert Niepert and Alberto Garcia-Duran

    Download (PDF)

  • Arrhythmia Detection from 2-lead ECG using Convolutional Denoising Autoencoders

    Keiichi Ochiai, Shu Takahashi and Yusuke Fukazawa

    Download (PDF)

  • Dropout-GAN: Learning from a Dynamic Ensemble of Discriminators

    Gonçalo Mordido, Haojin Yang and Christoph Meinel

    Download (PDF)

  • Understanding Ecommerce Clickstreams: A Tale of Two States

    Humphrey Sheil, Omer Rana and Ronan Reilly

    Download (PDF)

  • Demonstrating AI-enabled SQL Queries over Relational Data using a Cognitive Database

    Jose Luis Neves and Rajesh Bordawekar

    Download (PDF)

  • End-to-end Deep Learning from Raw Sensor Data: Atrial Fibrillation Detection using Wearables

    Igor Gotlibovych, Jessie Li, Stuart Crawford, Dileep Goyal, Jackie Liu, Yaniv Kerem, David Benaron, Defne Yilmaz and Gregory Marcus

    Download (PDF)

  • Variational Bi-domain Triplet Autoencoder

    Rita Kuznetsova and Oleg Bakhteev

    Download (PDF)

  • Integrative Analysis of Patient Health Records and Neuroimages via Memory-based Graph Convolutional Network

    Xi Zhang, Jingyuan Chou and Fei Wang

    Download (PDF)

  • Adaptive Image Stream Classification via Convolutional Neural Network with Intrinsic Similarity Metrics

    Yang Gao, Swarup Chandra, Zhuoyi Wang and Latifur Khan

    Download (PDF)

  • A Generic Approach to Scale Graph Embedding Methods

    Jiongqian Liang, Saket Gurukar and Srinivasan Parthasarathy

    Download (PDF)

  • Learning Graph Representations with Recurrent Neural Network Autoencoders (spotlight)

    Aynaz Taheri, Kevin Gimpel and Tanya Berger-Wolf

    Download (PDF)

  • On the Flip Side: Identifying Counterexamples in Visual Question Answering

    Gabriel Grand, Aron Szanto, Yoon Kim and Alexander Rush

    Download (PDF)

  • Usability Study of Distributed Deep Learning Frameworks For Convolutional Neural Networks

    Jiayi Liu, Jayanta Dutta, Nanxiang Li, Unmesh Kurup and Mohak Shah

    Download (PDF)

  • Interweaving Convolutions: An application to Audio Classification

    Harsh Sinha and Pawan Ajmera

    Download (PDF)

  • Bounded Information Rate Variational Autoencoders

    Daniel Braithwaite and Bastiaan Kleijn

    Download (PDF)

  • Graph Convolutional Matrix Completion (Spotlight)

    Rianne van den Berg, Thomas Kipf and Max Welling

    Download (PDF)

  • Defensive denoising methods against adversarial attack

    Sungyoon Lee, Saerom Park and Jaewook Lee

    Download (PDF)

  • Neural Attention Reader for Video Comprehension

    Ashish Gupta, Rishabh Mehrotra and Manish Gupta

    Download (PDF)

  • Intelligent code reviews using deep learning

    Anshul Gupta and Neel Sundaresan

    Download (PDF)

  • Action Permissibility in Deep Reinforcement Learning and Application to Autonomous Driving (spotlight)

    Sahisnu Mazumder, Bing Liu, Shuai Wang, Yingxuan Zhu, Lifeng Liu and Jian Li

    Download (PDF)

  • Hierarchical Graph Representation Learning with Differentiable Pooling (Spotlight)

    Rex Ying, Jiaxuan You, Christopher Morris, Xiang Ren, William L. Hamilton and Jure Leskovec

    Download (PDF)

  • A Comprehensive Study of StaQC for Deep Code Summarization

    Jayavardhan Reddy Peddamail, Ziyu Yao, Zhen Wang and Huan Sun

    Download (PDF)

  • A Hybrid Variational Autoencoder for Collaborative Filtering

    Kilol Gupta, Mukund Yelahanka Raghuprasad and Pankhuri Kumar

    Download (PDF)

  • Revisiting LSTM Networks for Semi-Supervised Text Classification (spotlight)

    Devendra Singh Sachan, Manzil Zaheer and Ruslan Salakhutdinov

    Download (PDF)

  • Hierarchical Classification with Hierarchical Attention Networks

    Tengke Xiong and Putra Manggala

    Download (PDF)

  • Chairs

    Anima Anandkumar
    Anima Anandkumar
    CALTECH/Amazon
    Jure Leskovec
    Jure Leskovec
    Stanford/Pinterest
    Yuxiao Dong
    Yuxiao Dong
    Microsoft Research
    Xia “Ben” Hu
    Xia “Ben” Hu
    Texas A&M University
    Joan Bruna
    Joan Bruna
    NYU

    Organizing Committee

    Pierre Richemond
    Pierre Richemond
    IMPERIAL COLLEGE LONDON
    Douglas Mcilwraith
    Douglas Mcilwraith
    IMPERIAL COLLEGE LONDON
    Kevin Webster
    Kevin Webster
    IMPERIAL COLLEGE LONDON

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    KDD 2018 - London, United Kingdom. 19 - 23 August 2018

    The annual KDD conference is the premier interdisciplinary conference bringing together researchers and practitioners from data science, data mining, knowledge discovery, large-scale data analytics, and big data.

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