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527 Results

March 14, 2018

Learning to Compute Word Embeddings On the Fly

ArXive

We provide a method for predicting embeddings of rare words on the fly from small amounts of auxiliary data with a network trained end-to-end for the downstream task. We show that this improves results against baselines where embeddings are trained on the end task for reading comprehension, recognizing textual entailment and language modeling.

By: Dzmitry Bahdanau, Tom Bosc, Stanislaw Jastrzebski, Edward Grefenstette, Pascal Vincent, Yoshua Bengio
March 12, 2018

Geometrical Insights for Implicit Generative Modeling

arXiv

Learning algorithms for implicit generative models can optimize a variety of criteria that measure how the data distribution differs from the implicit model distribution, including the Wasserstein distance, the Energy distance, and the Maximum Mean Discrepancy criterion.

By: Leon Bottou, Martin Arjovsky, David Lopez-Paz, Maxime Oquab
March 8, 2018

Generative Street Addresses from Satellite Imagery

ISPRS International Journal of Geo-Information

We describe our automatic generative algorithm to create street addresses from satellite images by learning and labeling roads, regions, and address cells. Currently, 75% of the world’s roads lack adequate street addressing systems. Recent geocoding initiatives tend to convert pure latitude and longitude information into a memorable form for unknown areas. However, settlements are identified by streets, and such addressing schemes are not coherent with the road topology.

By: Ilke Demir, Forest Hughes, Aman Raj, Kaunil Dhruv, Suryanarayana Murthy Muddala, Sanyam Garg, Barrett Doo, Ramesh Raskar
February 2, 2018

StarSpace: Embed All The Things!

Conference on Artificial Intelligence (AAAI)

We present StarSpace, a general-purpose neural embedding model that can solve a wide variety of problems: labeling tasks such as text classification, ranking tasks such as information retrieval/web search, collaborative filtering-based or content-based recommendation, embedding of multi-relational graphs, and learning word, sentence or document level embeddings.

By: Ledell Wu, Adam Fisch, Sumit Chopra, Keith Adams, Antoine Bordes, Jason Weston
February 2, 2018

Efficient Large-Scale Multi-Modal Classification

Conference on Artificial Intelligence (AAAI)

We investigate various methods for performing multi-modal fusion and analyze their trade-offs in terms of classification accuracy and computational efficiency.

By: Douwe Kiela, Edouard Grave, Armand Joulin, Tomas Mikolov
February 2, 2018

Efficient K-Shot Learning with Regularized Deep Networks

AAAI Conference on Artificial Intelligence (AAAI)

The problem of sub-optimality and over-fitting, is due in part to the large number of parameters (≈ 106) used in a typical deep convolutional neural network. To address these problems, we propose a simple yet effective regularization method for fine-tuning pre-trained deep networks for the task of k-shot learning.

By: Donghyun Yoo, Haoqi Fan, Vishnu Naresh Boddeti, Kris M. Kitani
January 27, 2018

An Inverse-Kinematic Approach to Dual-Stage Servo Control for an Optical Pointing System

SPIE Photonics West

In free-space optical communication systems, narrow beam diameters necessitate precision pointing over a relatively wide field of regard. Control systems with such large dynamic range requirements sometimes employ multi-stage architectures, where a “coarse” mechanism provides low-bandwidth control over a large range of travel, and a “fine” mechanism provides high-bandwidth disturbance rejection over a limited range of travel. In such systems, the two stages must be coordinated in their dynamic response, so as to avoid undesirable coupling and even interference with each other. This topic has been studied in various literature – especially in the field of hard disk drives, but also for optical pointing systems. However, most of this literature considers systems where the output of the two stages combines by simple linear sum. Dual-stage control becomes even more challenging when the two stages combine via nonlinear kinematic relationships, as they would in an optical pointing system employing multiple gimbaled steering mirrors. This paper presents an approach for handling these nonlinear kinematic effects, and demonstrates the viability of this approach via simulation.

By: Eric D. Miller
January 14, 2018

Experimental Demonstration of Digital Pre-Distortion for Millimeter Wave Power Amplifiers with GHz Bandwidth

IEEE Radio Wireless Week (RWW)

This paper presents an experimental demonstration of digital pre-distortion (DPD) on E-band power amplifiers (PA) with GHz channel bandwidth.

By: Qi Tang, Hongyu Zhou, Abhishek Tiwari, Joseph Stewart, Qi Qu, Dawei Zhang, Hamid Hemmati
December 15, 2017

Mapping the world population one building at a time

ArXive

Here, we present a computer vision method based on machine learning to create population maps from satellite imagery at a global scale, with a spatial sensitivity corresponding to individual buildings and suitable for global deployment.

By: Tobias Tiecke, Xianming Liu, Amy Zhang, Andreas Gros, Nan Li, Gregory Yetman, Talip Kilic, Siobhan Murray
December 12, 2017

Supporting Diverse Dynamic Intent-based Policies using Janus

International Conference on emerging Networking EXperiments and Technologies (CoNEXT)

In this paper we propose Janus, a system which makes two major contributions to network policy abstractions. First, we extend the prior policy graph abstraction model to represent complex QoS and dynamic stateful/temporal policies. Second, we convert the policy configuration problem into an optimization problem with the goal of maximizing the number of satisfied and configured policies, and minimizing the number of path changes under dynamic environments.

By: Anubhavnidhi Abhashkumar, Joon-Myung Kang, Sujata Banerjee, Aditya Akella, Ying Zhang, Wenfei Wu