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Deep Learning (DL) has attracted a lot of attention for its ability to reach state-of-the-art performance in many machine learning tasks.
80 Million Tiny Images: A Large Data Set for Nonparametric Object and Scene Recognition
Torralba, A.; Fergus, R.; Freeman, W.T · 1970
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Solving multiclass learning problems via error-correcting output codes
Dietterich, T.G.; Bakiri, G · 1994
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Laplacian eigenmaps for dimensionality reduction and data representation
Belkin, M.; Niyogi, P · 2003
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Learning multiple layers of features from tiny images
Krizhevsky, A.; Hinton, G · 2009
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Imagenet classification with deep convolutional neural networks
Krizhevsky, A.; Sutskever, I.; Hinton, G.E · 2012
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The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains
Shuman, D.I.; Narang, S.K.; Frossard, P.; Ortega, A.; Vandergheynst, P · 2013
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Distilling the knowledge in a neural network
Hinton, G.; Vinyals, O.; Dean, J · 2014
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Explaining and harnessing adversarial examples
Goodfellow, I.J.; Shlens, J.; Szegedy, C · 2014
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Deep learning
LeCun, Y.; Bengio, Y.; Hinton, G · 2015
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Fitnets: Hints for thin deep nets
Romero, A.; Ballas, N.; Kahou, S.E.; Chassang, A.; Gatta, C.; Bengio, Y · 2015
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Deep Learning
Goodfellow, I.; Bengio, Y.; Courville, A · 2016
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Deep residual learning for image recognition
He, K.; Zhang, X.; Ren, S.; Sun, J · 2016
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Semi-supervised classification with graph convolutional networks
Kipf, T.N.; Welling, M · 2016
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Rethinking the inception architecture for computer vision
Szegedy, C.; Vanhoucke, V.; Ioffe, S.; Shlens, J.; Wojna, Z · 2016
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Zagoruyko, S.; Komodakis, N · 2016
Cited alongside, same era.
Parseval networks: Improving robustness to adversarial examples
Cisse, M.; Bojanowski, P.; Grave, E.; Dauphin, Y.; Usunier, N · 2017
Cited alongside, same era.
In defense of the triplet loss for person re-identification
Hermans, A.; Beyer, L.; Leibe, B · 2017
Cited alongside, same era.
Influential sample selection: A graph signal processing approach
Anirudh, R.; Bremer, P.; Sridhar, R.; Thiagarajan, J · 2017
Cited alongside, same era.
Veličković, P.; Cucurull, G.; Casanova, A.; Romero, A.; Lio, P.; Bengio, Y · 2017
Cited alongside, same era.
Relational Knowledge Distillation
Park, W.; Kim, D.; Lu, Y.; Cho, M · 2019
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LIT: Learned intermediate representation training for model compression
Koratana, A.; Kang, D.; Bailis, P.; Zaharia, M · 2019
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Introducing Graph Smoothness Loss for Training Deep Learning Architectures
Bontonou*, M.; Lassance*, C.; Hacene, G.B.; Gripon, V.; Tang, J.; Ortega, A · 2019
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PeerNets: Exploiting Peer Wisdom Against Adversarial Attacks
Svoboda, J.; Masci, J.; Monti, F.; Bronstein, M.; Guibas, L · 2019
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L2-Nonexpansive Neural Networks
Qian, H.; Wegman, M.N · 2019
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Knowledge Distillation via Instance Relationship Graph
Liu, Y.; Cao, J.; Li, B.; Yuan, C.; Hu, W.; Li, Y.; Duan, Y · 2019
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Ruder, S · 2017
Cited alongside, same era.
Understanding back-translation at scale
Edunov, S.; Ott, M.; Auli, M.; Grangier, D · 2018
Cited alongside, same era.
The Power and Limits of Deep Learning
LeCun, Y · 2018
Cited alongside, same era.
On convolution of graph signals and deep learning on graph domains
Vialatte, J.C · 2018
Cited alongside, same era.
An Inside Look at Deep Neural Networks using Graph Signal Processing
Gripon, V.; Ortega, A.; Girault, B · 2018
Cited alongside, same era.
Towards Deep Learning Models Resistant to Adversarial Attacks
Madry, A.; Makelov, A.; Schmidt, L.; Tsipras, D.; Vladu, A · 2018
Cited alongside, same era.
Efficientnet: Rethinking model scaling for convolutional neural networks
Tan, M.; Le, Q.V · 2019
Cited alongside, same era.
Graph-based knowledge distillation by multi-head attention network
Lee, S.; Song, B · 2019
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Processing and learning deep neural networks on chip
Hacene, G.B · 2019
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The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks
Frankle, J.; Carbin, M · 2019
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Large Scale Graph Learning From Smooth Signals
Kalofolias, V.; Perraudin, N · 2019
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Deep geometric knowledge distillation with graphs
Lassance, C.; Bontonou, M.; Hacene, G.B.; Gripon, V.; Tang, J.; Ortega, A · 2020
Closest in time.
Exploiting Unsupervised Inputs for Accurate Few-Shot Classification
Hu, Y.; Gripon, V.; Pateux, S · 2020
Closest in time.
Graph Construction from Data by Non-Negative Kernel Regression
Shekkizhar, S.; Ortega, A · 2020
Closest in time.