Self-Supervised Relative Depth Learning for Urban Scene Understanding

Apr 02, 2018

Huaizu Jiang, Erik Learned-Miller, Gustav Larsson, Michael Maire, Greg Shakhnarovich

Apr 02, 2018

Huaizu Jiang, Erik Learned-Miller, Gustav Larsson, Michael Maire, Greg Shakhnarovich

**Click to Read Paper**

One-to-many face recognition with bilinear CNNs

Mar 28, 2016

Aruni RoyChowdhury, Tsung-Yu Lin, Subhransu Maji, Erik Learned-Miller

Mar 28, 2016

Aruni RoyChowdhury, Tsung-Yu Lin, Subhransu Maji, Erik Learned-Miller

* Published version at WACV 2016

**Click to Read Paper**

The Intel Collaborative Research Institute for Computational Intelligence (ICRI-CI) has been heavily supporting Machine Learning and Deep Learning research from its foundation in 2012. We have asked six leading ICRI-CI Deep Learning researchers to address the challenge of "Why & When Deep Learning works", with the goal of looking inside Deep Learning, providing insights on how deep networks function, and uncovering key observations on their expressiveness, limitations, and potential. The output of this challenge resulted in five papers that address different facets of deep learning. These different facets include a high-level understating of why and when deep networks work (and do not work), the impact of geometry on the expressiveness of deep networks, and making deep networks interpretable.

* This paper is the preface part of the "Why & When Deep Learning works looking inside Deep Learning" ICRI-CI paper bundle

* This paper is the preface part of the "Why & When Deep Learning works looking inside Deep Learning" ICRI-CI paper bundle

**Click to Read Paper**
Deep Meta-Learning: Learning to Learn in the Concept Space

Feb 10, 2018

Fengwei Zhou, Bin Wu, Zhenguo Li

Feb 10, 2018

Fengwei Zhou, Bin Wu, Zhenguo Li

**Click to Read Paper**

**Click to Read Paper**

How deep learning works --The geometry of deep learning

Oct 30, 2017

Xiao Dong, Jiasong Wu, Ling Zhou

Oct 30, 2017

Xiao Dong, Jiasong Wu, Ling Zhou

* 16 pages, 13 figures

**Click to Read Paper**

Online Deep Learning: Learning Deep Neural Networks on the Fly

Nov 10, 2017

Doyen Sahoo, Quang Pham, Jing Lu, Steven C. H. Hoi

Nov 10, 2017

Doyen Sahoo, Quang Pham, Jing Lu, Steven C. H. Hoi

**Click to Read Paper**

**Click to Read Paper**

Deep Reinforcement Learning for Conversational AI

Sep 15, 2017

Mahipal Jadeja, Neelanshi Varia, Agam Shah

Sep 15, 2017

Mahipal Jadeja, Neelanshi Varia, Agam Shah

* SCAI'17-Search-Oriented Conversational AI (@ICTIR)

**Click to Read Paper**

Deep Learning is one of the newest trends in Machine Learning and Artificial Intelligence research. It is also one of the most popular scientific research trends now-a-days. Deep learning methods have brought revolutionary advances in computer vision and machine learning. Every now and then, new and new deep learning techniques are being born, outperforming state-of-the-art machine learning and even existing deep learning techniques. In recent years, the world has seen many major breakthroughs in this field. Since deep learning is evolving at a huge speed, its kind of hard to keep track of the regular advances especially for new researchers. In this paper, we are going to briefly discuss about recent advances in Deep Learning for past few years.

* 31 pages including bibliography

* 31 pages including bibliography

**Click to Read Paper**
Deep Models Under the GAN: Information Leakage from Collaborative Deep Learning

Sep 14, 2017

Briland Hitaj, Giuseppe Ateniese, Fernando Perez-Cruz

Sep 14, 2017

Briland Hitaj, Giuseppe Ateniese, Fernando Perez-Cruz

* ACM CCS'17, 16 pages, 18 figures

**Click to Read Paper**

Accelerating Deep Learning with Shrinkage and Recall

Sep 19, 2016

Shuai Zheng, Abhinav Vishnu, Chris Ding

Sep 19, 2016

Shuai Zheng, Abhinav Vishnu, Chris Ding

* The 22nd IEEE International Conference on Parallel and Distributed Systems (ICPADS 2016)

**Click to Read Paper**

Leveraging Crowdsourcing Data For Deep Active Learning - An Application: Learning Intents in Alexa

Mar 12, 2018

Jie Yang, Thomas Drake, Andreas Damianou, Yoelle Maarek

Mar 12, 2018

Jie Yang, Thomas Drake, Andreas Damianou, Yoelle Maarek

**Click to Read Paper**

A Biologically Plausible Learning Rule for Deep Learning in the Brain

Nov 05, 2018

Isabella Pozzi, Sander Bohté, Pieter Roelfsema

Nov 05, 2018

Isabella Pozzi, Sander Bohté, Pieter Roelfsema

**Click to Read Paper**

Integrating Learning and Reasoning with Deep Logic Models

Jan 14, 2019

Giuseppe Marra, Francesco Giannini, Michelangelo Diligenti, Marco Gori

Jan 14, 2019

Giuseppe Marra, Francesco Giannini, Michelangelo Diligenti, Marco Gori

**Click to Read Paper**

Geometrization of deep networks for the interpretability of deep learning systems

Jan 13, 2019

Xiao Dong, Ling Zhou

How to understand deep learning systems remains an open problem. In this paper we propose that the answer may lie in the geometrization of deep networks. Geometrization is a bridge to connect physics, geometry, deep network and quantum computation and this may result in a new scheme to reveal the rule of the physical world. By comparing the geometry of image matching and deep networks, we show that geometrization of deep networks can be used to understand existing deep learning systems and it may also help to solve the interpretability problem of deep learning systems.
Jan 13, 2019

Xiao Dong, Ling Zhou

* 9 pages, draft version

**Click to Read Paper**

Deep Learning At Scale and At Ease

Mar 25, 2016

Wei Wang, Gang Chen, Haibo Chen, Tien Tuan Anh Dinh, Jinyang Gao, Beng Chin Ooi, Kian-Lee Tan, Sheng Wang

Mar 25, 2016

Wei Wang, Gang Chen, Haibo Chen, Tien Tuan Anh Dinh, Jinyang Gao, Beng Chin Ooi, Kian-Lee Tan, Sheng Wang

* submitted to TOMM (under review)

**Click to Read Paper**

Building Program Vector Representations for Deep Learning

Sep 11, 2014

Lili Mou, Ge Li, Yuxuan Liu, Hao Peng, Zhi Jin, Yan Xu, Lu Zhang

Sep 11, 2014

Lili Mou, Ge Li, Yuxuan Liu, Hao Peng, Zhi Jin, Yan Xu, Lu Zhang

* This paper was submitted to ICSE'14

**Click to Read Paper**

Greedy Deep Dictionary Learning

Jan 31, 2016

Snigdha Tariyal, Angshul Majumdar, Richa Singh, Mayank Vatsa

Jan 31, 2016

Snigdha Tariyal, Angshul Majumdar, Richa Singh, Mayank Vatsa

**Click to Read Paper**

In the era of big data, transformation of biomedical big data into valuable knowledge has been one of the most important challenges in bioinformatics. Deep learning has advanced rapidly since the early 2000s and now demonstrates state-of-the-art performance in various fields. Accordingly, application of deep learning in bioinformatics to gain insight from data has been emphasized in both academia and industry. Here, we review deep learning in bioinformatics, presenting examples of current research. To provide a useful and comprehensive perspective, we categorize research both by the bioinformatics domain (i.e., omics, biomedical imaging, biomedical signal processing) and deep learning architecture (i.e., deep neural networks, convolutional neural networks, recurrent neural networks, emergent architectures) and present brief descriptions of each study. Additionally, we discuss theoretical and practical issues of deep learning in bioinformatics and suggest future research directions. We believe that this review will provide valuable insights and serve as a starting point for researchers to apply deep learning approaches in their bioinformatics studies.

* Accepted for Briefings in Bioinformatics (18-Jun-2016)

* Accepted for Briefings in Bioinformatics (18-Jun-2016)

**Click to Read Paper**