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Year Published

388 Results

May 22, 2017

IVD: Automatic Learning and Enforcement of Authorization Rules in Online Social Networks

IEEE Symposium on Security and Privacy (IEEE S&P)

In this paper, we propose Invariant Detector (IVD), a defense-in-depth system that automatically learns authorization rules from normal data manipulation patterns and distills them into likely invariants.

By: Paul Marinescu, Chad Parry, Marjori Pomarole, Yuan Tian, Patrick Tague, Ioannis Papagiannis
May 21, 2017

CAN: Creative Adversarial Networks

IEEE International Conference on Communications (ICCC)

We propose a new system for generating art. The system generates art by looking at art and learning about style; and becomes creative by increasing the arousal potential of the generated art by deviating from the learned styles. We build over Generative Adversarial Networks (GAN), which have shown the ability to learn to generate novel images simulating a given distribution.

By: Ahmed Elgammal, Bingchen Liu, Mohamed Elhoseiny, Marian Mazzone
May 16, 2017

Cultural Diffusion and Trends in Facebook Photographs

The International AAAI Conference on Web and Social Media (ICWSM)

Online social media is a social vehicle in which people share various moments of their lives with their friends, such as playing sports, cooking dinner or just taking a selfie for fun, via visual means, i.e., photographs. Our study takes a closer look at the popular visual concepts illustrating various cultural lifestyles from aggregated, de-identified photographs.

By: Quenzeng You, Dario Garcia, Manohar Paluri, Jiebo Luo, Jungseock Joo
May 6, 2017

Paradigm shift from Human Computer Interaction to Integration

Computer Human Interaction (CHI)

In 1960, JCR Licklider forecast three phases: human- computer interaction, human-computer symbiosis, and ultra-intelligent machines. Human-computer symbiosis or what we call “integration” is already well under way. This SIG will discuss how the CHI community should think about the paradigm shift from interaction to integration as designers, practitioners, researchers, and as a society.

By: Jonathan T. Grudin, Umer Farooq
May 2, 2017

Better Computer Go Player with Neural Network and Long-Term Prediction

International Conference on Learning Representations (ICLR)

Competing with top human players in the ancient game of Go has been a longterm goal of artificial intelligence. Recent works [Maddison et al. (2015); Clark & Storkey (2015)] show that search is not strictly necessary for machine Go players. A pure pattern-matching approach, based on a Deep Convolutional Neural Network (DCNN) that predicts the next move, can perform as well as Monte Carlo Tree Search (MCTS)-based open source Go engines such as Pachi [Baudis & Gailly (2012)] if its search budget is limited. We extend this idea in our bot named darkforest, which relies on a DCNN designed for long-term predictions.

By: Yuandong Tian, Yan Zhu
April 27, 2017

Passive Realtime Datacenter Fault Detection

USENIX Symposium on Networked Systems Design and Implementation (NSDI) 2017

We describe how to expedite the process of detecting and localizing partial datacenter faults using an end-host method generalizable to most datacenter applications.

By: Arjun Roy, James Hongyi Zeng, Jasmeet Bagga, Alex C. Snoeren
April 24, 2017

Towards Principled Methods for Training Generative Adversarial Networks

International Conference on Learning Representations (ICLR) 2017

The goal of this paper is not to introduce a single algorithm or method, but to make theoretical steps towards fully understanding the training dynamics of generative adversarial networks.

By: Leon Bottou, Martin Arjovsky
April 24, 2017

Improving Neural Language Models with a Continuous Cache

International Conference on Learning Representations (ICLR) 2017

We propose an extension to neural network language models to adapt their prediction to the recent history. Our model is a simplified version of memory augmented networks, which stores past hidden activations as memory and accesses them through a dot product with the current hidden activation.

By: Armand Joulin, Edouard Grave, Nicolas Usunier
April 24, 2017

Revisiting Classifier Two-Sample Tests for GAN Evaluation and Causal Discovery

International Conference on Learning Representations (ICLR) 2017

In this paper, we aim to revive interest in the use of binary classifiers for two-sample testing. To this end, we review their fundamentals, previous literature on their use, compare their performance against alternative state-of-the-art two-sample tests, and propose them to evaluate generative adversarial network models applied to image synthesis.

By: David Lopez-Paz, Maxime Oquab
April 24, 2017

CommAI: Evaluating the First Steps Towards a Useful General AI

ICLR 2017 Workshop

We propose a set of concrete desiderata for general AI, together with a platform to test machines on how well they satisfy such desiderata, while keeping all further complexities to a minimum.

By: Marco Baroni, Armand Joulin, Allan Jabri, Germán Kruszewski, Angeliki Lazaridou, Klemen Simonic, Tomas Mikolov