Referenced Papers (44)
Receptive fields of single neurones in the cat's striate cortex
D H Hubel, T N Wiesel
J. Physiol., 1959
"Cited as early foundational work on receptive fields in the cat's striate cortex, inspiring later models."
Receptive fields, binocular interaction and functional architecture in the cat's visual cortex
D H Hubel, T N Wiesel
J. Physiol., 1962
"Cited as early foundational work on binocular interaction and functional architecture in the cat's visual cortex."
Gradient-based learning applied to document recognition
Y Lecun, L Bottou, Y Bengio, P Haffner
Proc. IEEE Inst. Electr. Electron. Eng., 1998
"Introduced as the first work to apply gradient-based learning to train convolutional networks like LeNet-5 end-to-end using supervised learning, particularly for document recognition."
Improving neural networks by preventing co-adaptation of feature detectors
Geoffrey E Hinton, Nitish Srivastava, Alex Krizhevsky, Ilya Sutskever, Ruslan R Salakhutdinov
arXiv [cs.NE], 2012
"This paper demonstrated ImageNet classification using scaled-up deep convolutional neural networks trained on GPUs, marking a significant performance jump in computer vision."
Deep residual learning for image recognition
Kaiming He, X Zhang, Shaoqing Ren, Jian Sun
Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., 2015
"Presented as a foundational work in residual networks and later highlighted for its refined identity mapping block design, and its success in ImageNet 2015."
CNN features off-the-shelf: An astounding baseline for recognition
Ali Sharif Razavian, Hossein Azizpour, Josephine Sullivan, Stefan Carlsson
arXiv [cs.CV], 2014
"Showcased that CNN features pre-trained on large datasets provide an astounding baseline for generic visual recognition tasks through transfer learning."
DeCAF: A deep convolutional activation feature for generic visual recognition
Jeff Donahue, Yangqing Jia, Oriol Vinyals, Judy Hoffman, Ning Zhang, Eric Tzeng
arXiv [cs.CV], 2013
"Demonstrated that deep convolutional features can be used for generic visual recognition across various tasks."
DeepFace: Closing the gap to human-level performance in face verification
Yaniv Taigman, Ming Yang, Marc'aurelio Ranzato, Lior Wolf
, 2014
"Cited as an application of convolutional neural networks for face verification."
Generative Adversarial Networks
Ian J Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair
arXiv [stat.ML], 2014
"Cited as an application of convolutional neural networks for tasks like house number recognition and Google Photos search."
Mitosis detection in breast cancer histology images with deep neural networks
Dan C Cireşan, Alessandro Giusti, Luca M Gambardella, Jürgen Schmidhuber
Med. Image Comput. Comput. Assist. Interv., 2013
"Cited as an application of convolutional neural networks for medical image diagnosis."
Convolutional networks can learn to generate affinity graphs for image segmentation
Srinivas C Turaga, Joseph F Murray, Viren Jain, Fabian Roth, Moritz Helmstaedter, Kevin Briggman
Neural Comput., 2010
"Cited as an application of convolutional neural networks for recognizing Chinese characters."
Recurrent models of visual attention
Volodymyr Mnih, Nicolas Heess, Alex Graves, Koray Kavukcuoglu
arXiv [cs.LG], 2014
"Cited as an application of convolutional neural networks for whale recognition, often seen in Kaggle competitions."
WaveNet: A generative model for raw audio
Aaron van den Oord, Sander Dieleman, Heiga Zen, Karen Simonyan, Oriol Vinyals, Alex Graves
arXiv [cs.SD], 2016
"Cited as an application of convolutional neural networks for generating music and speech."
Show and tell: A neural image caption generator
Oriol Vinyals, Alexander Toshev, Samy Bengio, Dumitru Erhan
, 2015
"Cited as an application of convolutional neural networks for image captioning."
Playing Atari with deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra
arXiv [cs.LG], 2013
"Cited as an application of convolutional neural networks for playing Atari games in reinforcement learning."
The predictron: End-to-end learning and planning
David Silver, Hado van Hasselt, Matteo Hessel, Tom Schaul, Arthur Guez, Tim Harley
arXiv [cs.LG], 2016
"Cited as an application of convolutional neural networks for playing Go in reinforcement learning."
A neural algorithm of artistic style
Leon A Gatys, Alexander S Ecker, Matthias Bethge
arXiv [cs.CV], 2015
"Cited as an application of convolutional neural networks for transferring artistic styles between images."
Deep neural networks rival the representation of primate IT cortex for core visual object recognition
Charles F Cadieu, Ha Hong, Daniel L K Yamins, Nicolas Pinto, Diego Ardila, Ethan A Solomon
PLoS Comput. Biol., 2014
"Cited as research showing that deep neural networks developed for computer vision may converge to representations similar to those in the primate visual cortex."
Understanding neural networks through deep visualization
J Yosinski, J Clune, Anh Totti Nguyen, Thomas J Fuchs, Hod Lipson
ArXiv, 2015
"Explains techniques for creating optimized images to understand what specific neurons in the network are looking for."
Very deep convolutional networks for large-scale image recognition
Karen Simonyan, Andrew Zisserman
arXiv [cs.CV], 2014
"Introduced VGGNet, a very simple and homogeneous architecture that achieved strong performance on ImageNet 2014."
Going deeper with convolutions
Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov
arXiv [cs.CV], 2014
"Presented as the winning architecture of ImageNet 2014, featuring the Inception module for efficient computation."
Deep networks with stochastic depth
Gao Huang, Yu Sun, Zhuang Liu, Daniel Sedra, Kilian Q Weinberger
ECCV, 2016
"Introduced a regularization technique called Swapout that interpolates between plain nets, ResNets, and dropout."
Wide Residual Networks
Sergey Zagoruyko, N Komodakis
Br Mach Vis Conf, 2016
"Proposed using wider and shallower residual networks as an alternative to very deep ones, showing comparable or better performance."
Swapout: Learning an ensemble of deep architectures
Saurabh Singh, Derek Hoiem, D Forsyth
Neural Inf Process Syst, 2016
"Introduced a regularization technique called Swapout that interpolates between plain nets, ResNets, and dropout."
FractalNet: Ultra-deep neural networks without residuals
Gustav Larsson, M Maire, Gregory Shakhnarovich
Int Conf Learn Represent, 2016
"Introduced FractalNet, an ultra-deep neural network that doesn't rely on residuals."
Densely connected convolutional networks
Gao Huang, Zhuang Liu, Kilian Q Weinberger
Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., 2016
"Mentioned as an active area of research in convolutional neural networks."
Soft fault diagnosis of analog circuits based on classification of GAF_RP images with ResNet
Xuanzhong Tang, Xin Zhou, Wenhai Liang
Circuits Systems Signal Process., 2023
"Mentioned as an active area of research in convolutional neural networks."
Deeply-Fused Nets
Jingdong Wang, Zhen Wei, Ting Zhang, Wenjun Zeng
ArXiv, 2016
"Mentioned as an active area of research in convolutional neural networks."
Weighted residuals for very deep networks
Falong Shen, Gang Zeng
arXiv [cs.CV], 2016
"Mentioned as an active area of research in convolutional neural networks."
Residual networks of residual networks: Multilevel residual networks
Ke Zhang, Miao Sun, Tony X Han, Xingfang Yuan, Liru Guo, Tao Liu
IEEE Trans. Circuits Syst. Video Technol., 2018
"Mentioned as an active area of research in convolutional neural networks."
Schwing Johnson Zemel Maas ResNet in ResNet: Generalizing Residual Networks
"Cited as a related work to residual networks focusing on generalization."
LeCun Bottou Bengio Haffner Learning from Supervised to Unsupervised: Learning with Deep Learning
"Cited as a paper related to learning with deep learning, transitioning from supervised to unsupervised approaches."
Kingma Maaten On Random Initialization and Batch Normalization for Training Very Deep Convolutional Networks
"Cited as a paper discussing random initialization and batch normalization for training deep convolutional networks."
Auto-Encoding Variational Bayes
Diederik P Kingma, Max Welling
arXiv [stat.ML], 2013
"Cited as works on variational autoencoders, which include a reparameterization layer for generative modeling."
DenseCap: Fully convolutional localization networks for dense captioning
Justin Johnson, A Karpathy, Li Fei-Fei
Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., 2015
"Cited as a paper on dense image captioning, combining object detection and description at a local level."
Cloud computing and comparison based on service and performance between Amazon AWS, Microsoft azure, and Google cloud
Prakarsh Kaushik, Ashwin Murali Rao, Devang Pratap Singh, Swati Vashisht, Shubhi Gupta
, 2021
"Mentioned as a cloud GPU provider, though noted for having less powerful GPUs (K520)."
A comparison of Amazon Web Services and Microsoft Azure cloud platforms for high performance computing
Charlotte Kotas, Thomas Naughton, Neena Imam
, 2018
"Mentioned as an upcoming cloud GPU provider offering K80 GPUs."
Theano: A Python framework for fast computation of mathematical expressions
Rami Al-Rfou, Guillaume Alain, Amjad Almahairi, Christof Angermueller, Dzmitry Bahdanau
arXiv [cs.SC], 2016
"Listed as a common deep learning framework, serving as a backend for Keras."
Comparative characteristics of keras and lasagne machine learning packages
V M Sineglazov, M O Omelchenko, V P Hotsyanivskyy
Electron. Contr. Syst., 2017
"Listed as a common deep learning framework."
TensorFlow: Large-scale machine learning on heterogeneous distributed systems
Martín Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro
arXiv [cs.DC], 2016
"Listed as a common deep learning framework, serving as a backend for Keras."
MXNet: A flexible and efficient machine learning library for heterogeneous distributed systems
Tianqi Chen, Mu Li, Yutian Li, Min Lin, Naiyan Wang, Minjie Wang
arXiv [cs.DC], 2015
"Listed as a common deep learning framework."
ChaInNet: Deep chain instance segmentation network for panoptic segmentation
Lin Mao, Fengzhi Ren, Dawei Yang, Rubo Zhang
Neural Process. Lett., 2022
"Listed as a common deep learning framework."
Phi-4-Mini technical report: Compact yet powerful multimodal language models via mixture-of-LoRAs
Microsoft, Abdelrahman Abouelenin, Atabak Ashfaq, Adam Atkinson, Hany Awadalla, Nguyen Bach
arXiv [cs.CL], 2025
"Listed as a common deep learning framework (CNTK)."
Large scale distributed deep networks
J Dean, G Corrado, R Monga, Kai Chen, M Devin, Quoc V Le
Neural Inf Process Syst, 2012
"Cited as a paper discussing different approaches to distributed deep learning, including data and model parallelism."