Mark Sandler

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Google ∙ Queen Mary University of London

Professor of Signal Processing , Director of qMedia at Queen Mary University of London

Searching for MobileNetV3

We present the next generation of MobileNets based on a combination of c...
05/06/2019 ∙ by Andrew Howard, et al. ∙ 53 ∙ share

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Structured Multi-Hashing for Model Compression

Despite the success of deep neural networks (DNNs), state-of-the-art mod...
11/25/2019 ∙ by Elad Eban, et al. ∙ 19 ∙ share

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Information-Bottleneck Approach to Salient Region Discovery

We propose a new method for learning image attention masks in a semi-sup...
07/22/2019 ∙ by Andrey Zhmoginov, et al. ∙ 6 ∙ share

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Non-discriminative data or weak model? On the relative importance of data and model resolution

We explore the question of how the resolution of the input image ("input...
09/07/2019 ∙ by Mark Sandler, et al. ∙ 3 ∙ share

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CycleGAN: a Master of Steganography

CycleGAN is one of the latest successful approaches to learn a correspon...
12/08/2017 ∙ by Casey Chu, et al. ∙ 0 ∙ share

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Convolutional Recurrent Neural Networks for Music Classification

We introduce a convolutional recurrent neural network (CRNN) for music t...
09/14/2016 ∙ by Keunwoo Choi, et al. ∙ 0 ∙ share

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Explaining Deep Convolutional Neural Networks on Music Classification

Deep convolutional neural networks (CNNs) have been actively adopted in ...
07/08/2016 ∙ by Keunwoo Choi, et al. ∙ 0 ∙ share

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Towards Playlist Generation Algorithms Using RNNs Trained on Within-Track Transitions

We introduce a novel playlist generation algorithm that focuses on the q...
06/07/2016 ∙ by Keunwoo Choi, et al. ∙ 0 ∙ share

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The Power of Sparsity in Convolutional Neural Networks

Deep convolutional networks are well-known for their high computational ...
02/21/2017 ∙ by Soravit Changpinyo, et al. ∙ 0 ∙ share

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Inverting face embeddings with convolutional neural networks

Deep neural networks have dramatically advanced the state of the art for...
06/14/2016 ∙ by Andrey Zhmoginov, et al. ∙ 0 ∙ share

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Towards Music Captioning: Generating Music Playlist Descriptions

Descriptions are often provided along with recommendations to help users...
08/17/2016 ∙ by Keunwoo Choi, et al. ∙ 0 ∙ share

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Inverted Residuals and Linear Bottlenecks: Mobile Networks forClassification, Detection and Segmentation

In this paper we describe a new mobile architecture, MobileNetV2, that i...
01/13/2018 ∙ by Mark Sandler, et al. ∙ 0 ∙ share

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Inverted Residuals and Linear Bottlenecks: Mobile Networks for Classification, Detection and Segmentation

In this paper we describe a new mobile architecture, MobileNetV2, that i...
01/13/2018 ∙ by Mark Sandler, et al. ∙ 0 ∙ share

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Transfer learning for music classification and regression tasks

In this paper, we present a transfer learning approach for music classif...
03/27/2017 ∙ by Keunwoo Choi, et al. ∙ 0 ∙ share

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A Tutorial on Deep Learning for Music Information Retrieval

Following their success in Computer Vision and other areas, deep learnin...
09/13/2017 ∙ by Keunwoo Choi, et al. ∙ 0 ∙ share

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A Comparison on Audio Signal Preprocessing Methods for Deep Neural Networks on Music Tagging

Deep neural networks (DNN) have been successfully applied for music clas...
09/06/2017 ∙ by Keunwoo Choi, et al. ∙ 0 ∙ share

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The Effects of Noisy Labels on Deep Convolutional Neural Networks for Music Tagging

Deep neural networks (DNN) have been successfully applied to music class...
06/07/2017 ∙ by Keunwoo Choi, et al. ∙ 0 ∙ share

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Does k Matter? k-NN Hubness Analysis for Kernel Additive Modelling Vocal Separation

Kernel Additive Modelling (KAM) is a framework for source separation aim...
04/06/2018 ∙ by Delia Fano Yela, et al. ∙ 0 ∙ share

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K For The Price Of 1: Parameter Efficient Multi-task And Transfer Learning

We introduce a novel method that enables parameter-efficient transfer an...
10/25/2018 ∙ by Pramod Kaushik Mudrakarta, et al. ∙ 0 ∙ share

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Similarity measures for vocal-based drum sample retrieval using deep convolutional auto-encoders

The expressive nature of the voice provides a powerful medium for commun...
02/14/2018 ∙ by Adib Mehrabi, et al. ∙ 0 ∙ share

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Visibility graphs for robust harmonic similarity measures between audio spectra

Graph theory is emerging as a new source of tools for time series analys...
03/05/2019 ∙ by Delia Fano Yela, et al. ∙ 0 ∙ share

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Efficient On-line Computation of Visibility Graphs

A visibility algorithm maps time series into complex networks following ...
05/08/2019 ∙ by Delia Fano Yela, et al. ∙ 0 ∙ share

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Adversarial Attacks in Sound Event Classification

Adversarial attacks refer to a set of methods that perturb the input to ...
07/04/2019 ∙ by Vinod Subramanian, et al. ∙ 0 ∙ share

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Mark Sandler

Featured Co-authors

Kyunghyun Cho

Quoc V. Le

Hao Wu

Bo Chen

Ning Xu

Liang-Chieh Chen

Hartwig Adam

Emmanouil Benetos

Ruoming Pang

Miguel Á. Carreira-Perpiñán

Dan Stowell

Searching for MobileNetV3

Structured Multi-Hashing for Model Compression

Information-Bottleneck Approach to Salient Region Discovery

Non-discriminative data or weak model? On the relative importance of data and model resolution

CycleGAN: a Master of Steganography

Convolutional Recurrent Neural Networks for Music Classification

Explaining Deep Convolutional Neural Networks on Music Classification

Towards Playlist Generation Algorithms Using RNNs Trained on Within-Track Transitions

The Power of Sparsity in Convolutional Neural Networks

Inverting face embeddings with convolutional neural networks

Towards Music Captioning: Generating Music Playlist Descriptions

Inverted Residuals and Linear Bottlenecks: Mobile Networks forClassification, Detection and Segmentation

Inverted Residuals and Linear Bottlenecks: Mobile Networks for Classification, Detection and Segmentation

Transfer learning for music classification and regression tasks

A Tutorial on Deep Learning for Music Information Retrieval

A Comparison on Audio Signal Preprocessing Methods for Deep Neural Networks on Music Tagging

The Effects of Noisy Labels on Deep Convolutional Neural Networks for Music Tagging

Does k Matter? k-NN Hubness Analysis for Kernel Additive Modelling Vocal Separation

K For The Price Of 1: Parameter Efficient Multi-task And Transfer Learning

Similarity measures for vocal-based drum sample retrieval using deep convolutional auto-encoders

Visibility graphs for robust harmonic similarity measures between audio spectra

Efficient On-line Computation of Visibility Graphs

Adversarial Attacks in Sound Event Classification