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435 Results

June 13, 2018

Efficient Evaluation of Coding Strategies for Transcutaneous Language Communication

Eurohaptics 2018

Communication of natural language via the skin has seen renewed interest with the advent of mobile devices and wearable technology. Efficient evaluation of candidate haptic encoding algorithms remains a significant challenge. We present 4 algorithms along with our methods for evaluation, which are based on discriminability, learnability, and generalizability. Advantageously, mastery of an extensive vocabulary is not required.

By: Robert Turcott, Jennifer Chen, Pablo Castillo, Brian Knott, Wahyudinata Setiawan, Forrest Briggs, Keith Klumb, Freddy Abnousi, Prasad Chakka, Frances Lau, Ali Israr
June 1, 2018

The Immersive VR Self: Performance, Embodiment and Presence in Immersive Virtual Reality Environments

Book chapter from A Networked Self and Human Augmentics, AI, Sentience

Virtual avatars are a common way to present oneself in online social interactions. From cartoonish emoticons to hyper-realistic humanoids, these online representations help us portray a certain image to our respective audiences.

By: Raz Schwartz, William Steptoe
June 1, 2018

QuickEdit: Editing Text & Translations by Crossing Words Out

Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)

We propose a framework for computer-assisted text editing. It applies to translation post-editing and to paraphrasing. Our proposal relies on very simple interactions: a human editor modifies a sentence by marking tokens they would like the system to change.

By: David Grangier, Michael Auli
May 7, 2018

Advances in Pre-Training Distributed Word Representations

Language Resources and Evaluation Conference (LREC)

In this paper, we show how to train high-quality word vector representations by using a combination of known tricks that are however rarely used together.

By: Tomas Mikolov, Edouard Grave, Piotr Bojanowski, Christian Puhrsch, Armand Joulin
May 2, 2018

Exploring the Limits of Weakly Supervised Pretraining

ArXiv

In this paper, we present a unique study of transfer learning with large convolutional networks trained to predict hashtags on billions of social media images.

By: Dhruv Mahajan, Ross Girshick, Vignesh Ramanathan, Kaiming He, Manohar Paluri, Yixuan Li, Ashwin Bharambe, Laurens van der Maaten
April 30, 2018

Countering Adversarial Images Using Input Transformations

International Conference on Learning Representations (ICLR)

This paper investigates strategies that defend against adversarial-example attacks on image-classification systems by transforming the inputs before feeding them to the system. Specifically, we study applying image transformations such as bit-depth reduction, JPEG compression, total variance minimization, and image quilting before feeding the image to a convolutional network classifier.

By: Chuan Guo, Mayank Rana, Moustapha Cisse, Laurens van der Maaten
April 30, 2018

Intrinsic Motivation and Automatic Curricula via Asymmetric Self-Play

International Conference on Learning Representations (ICLR)

We describe a simple scheme that allows an agent to learn about its environment in an unsupervised manner. Our scheme pits two versions of the same agent, Alice and Bob, against one another.

By: Sainbayar Sukhbaatar, Zeming Lin, Ilya Kostrikov, Gabriel Synnaeve, Arthur Szlam, Rob Fergus
April 30, 2018

Multi-Scale Dense Networks for Resource Efficient Image Classification

International Conference on Learning Representations (ICLR)

In this paper we investigate image classification with computational resource limits at test time. Two such settings are: 1. anytime classification, where the network’s prediction for a test example is progressively updated, facilitating the output of a prediction at any time; and 2. budgeted batch classification, where a fixed amount of computation is available to classify a set of examples that can be spent unevenly across “easier” and “harder” inputs.

By: Gao Huang, Danlu Chen, Tianhong Li, Felix Wu, Laurens van der Maaten, Kilian Weinberger
April 30, 2018

Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks

International Conference on Learning Representations (ICLR)

We consider the problem of detecting out-of-distribution images in neural networks. We propose ODIN, a simple and effective method that does…

By: Shiyu Liang, Yixuan Li, R. Srikant
April 30, 2018

An Evaluation of Fisher Approximations Beyond Kronecker Factorization

International Conference on Learning Representations (ICLR)

We study two coarser approximations on top of a Kronecker factorization (K-FAC) of the Fisher Information Matrix, to scale up Natural Gradient to deep and wide Convolutional Neural Networks (CNNs). The first considers the feature maps as spatially uncorrelated while the second considers only correlations among groups of channels

By: Cesar Laurent, Thomas George, Xavier Bouthillier, Nicolas Ballas, Pascal Vincent