Open AccessProceedings Article
2011 international joint conference on neural networks (ijcnn)
About:
This article is published in International Joint Conference on Neural Network.The article was published on 2011-01-01 and is currently open access. It has received 335 citations till now.read more
Citations
More filters
Proceedings ArticleDOI
Convolutional neural networks for human activity recognition using multiple accelerometer and gyroscope sensors
Sojeong Ha,Seungjin Choi +1 more
TL;DR: This paper employs both partial weight sharing and full weight sharing for the CNN models in such a way that modality-specific characteristics as well as common characteristics across modalities are learned from multi-modal (or multi-sensor) data and are eventually aggregated in upper layers.
Proceedings ArticleDOI
Exploring the space of adversarial images
Pedro Tabacof,Eduardo Valle +1 more
TL;DR: This work formalizes the problem of adversarial images given a pretrained classifier, showing that even in the linear case the resulting optimization problem is nonconvex and that a shallow classifier seems more robust to adversarial pictures than a deep convolutional network.
Proceedings ArticleDOI
Resistive memory device requirements for a neural algorithm accelerator
Sapan Agarwal,Steven J. Plimpton,David Russell Hughart,Alexander H. Hsia,Isaac Richter,Jonathan A. Cox,Conrad D. James,Matthew J. Marinella +7 more
TL;DR: A general purpose neural architecture that can accelerate many different algorithms and determine the device properties that will be needed to run backpropagation on the neural architecture is proposed.
Proceedings ArticleDOI
Sentic LDA: Improving on LDA with semantic similarity for aspect-based sentiment analysis
TL;DR: Sentic LDA exploits common-sense reasoning to shift LDA clustering from a syntactic to a semantic level and leverages on the semantics associated with words and multi-word expressions to improve clustering and, hence, outperform state-of-the-art techniques for aspect extraction.
Proceedings ArticleDOI
Memristor crossbar deep network implementation based on a Convolutional neural network
TL;DR: This paper presents a simulated memristor crossbar implementation of a deep Convolutional Neural Network (CNN) that is capable of operating with zero loss in classification accuracy if the memristors utilized are able to store at least 16 unique values.
References
More filters
Proceedings ArticleDOI
Convolutional neural networks for human activity recognition using multiple accelerometer and gyroscope sensors
Sojeong Ha,Seungjin Choi +1 more
TL;DR: This paper employs both partial weight sharing and full weight sharing for the CNN models in such a way that modality-specific characteristics as well as common characteristics across modalities are learned from multi-modal (or multi-sensor) data and are eventually aggregated in upper layers.
Proceedings ArticleDOI
Exploring the space of adversarial images
Pedro Tabacof,Eduardo Valle +1 more
TL;DR: This work formalizes the problem of adversarial images given a pretrained classifier, showing that even in the linear case the resulting optimization problem is nonconvex and that a shallow classifier seems more robust to adversarial pictures than a deep convolutional network.
Proceedings ArticleDOI
Resistive memory device requirements for a neural algorithm accelerator
Sapan Agarwal,Steven J. Plimpton,David Russell Hughart,Alexander H. Hsia,Isaac Richter,Jonathan A. Cox,Conrad D. James,Matthew J. Marinella +7 more
TL;DR: A general purpose neural architecture that can accelerate many different algorithms and determine the device properties that will be needed to run backpropagation on the neural architecture is proposed.
Proceedings ArticleDOI
Sentic LDA: Improving on LDA with semantic similarity for aspect-based sentiment analysis
TL;DR: Sentic LDA exploits common-sense reasoning to shift LDA clustering from a syntactic to a semantic level and leverages on the semantics associated with words and multi-word expressions to improve clustering and, hence, outperform state-of-the-art techniques for aspect extraction.
Proceedings ArticleDOI
Memristor crossbar deep network implementation based on a Convolutional neural network
TL;DR: This paper presents a simulated memristor crossbar implementation of a deep Convolutional Neural Network (CNN) that is capable of operating with zero loss in classification accuracy if the memristors utilized are able to store at least 16 unique values.