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110 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
May 16, 2018

Glow: Graph Lowering Compiler Techniques for Neural Networks

arXiv

This paper presents the design of Glow, a machine learning compiler for heterogeneous hardware. It is a pragmatic approach to compilation that enables the generation of highly optimized code for multiple targets. Glow lowers the traditional neural network dataflow graph into a two-phase strongly-typed intermediate representation.

By: Saleem Abdulrasool, Summer Deng, Roman Dzhabarov, Jordan Fix, James Hegeman, Roman Levenstein, Bert Maher, Satish Nadathur, Jakob Olesen, Jongsoo Park, Artem Rakhov, Nadav Rotem, Misha Smelyanskiy
May 8, 2018

Optimization Methods for Large-Scale Machine Learning

SIAM Review

This paper provides a review and commentary on the past, present, and future of numerical optimization algorithms in the context of machine learning applications.

By: Leon Bottou, Frank E. Curtis, Jorge Nocedal
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

When is a Convolutional Filter Easy to Learn?

International Conference on Learning Representations (ICLR)

We analyze the convergence of (stochastic) gradient descent algorithm for learning a convolutional filter with Rectified Linear Unit (ReLU) activation function. Our analysis does not rely on any specific form of the input distribution and our proofs only use the definition of ReLU, in contrast with previous works that are restricted to standard Gaussian input.

By: Simon S. Du, Jason D. Lee, Yuandong Tian
April 30, 2018

Building Generalizable Agents with a Realistic and Rich 3D Environment

International Conference on Learning Representations (ICLR)

Teaching an agent to navigate in an unseen 3D environment is a challenging task, even in the event of simulated environments. To generalize to unseen environments, an agent needs to be robust to low-level variations (e.g. color, texture, object changes), and also high-level variations (e.g. layout changes of the environment). To improve overall generalization, all types of variations in the environment have to be taken under consideration via different level of data augmentation steps.

By: Yi Wu, Yuxin Wu, Georgia Gkioxari, Yuandong Tian
April 30, 2018

Identifying Analogies Across Domains

International Conference on Learning Representations (ICLR)

In this paper, we tackle this very task of finding exact analogies between datasets i.e. for every image from domain A find an analogous image in domain B. We present a matching-by-synthesis approach: AN-GAN, and show that it outperforms current techniques.

By: Yedid Hoshen, Lior Wolf
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
April 30, 2018

Graph Attention Networks

International Conference on Learning Representations (ICLR)

We present graph attention networks (GATs), novel neural network architectures that operate on graph-structured data, leveraging masked self-attentional layers to address the shortcomings of prior methods based on graph convolutions or their approximations.

By: Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, Yoshua Bengio