Large-scale deep unsupervised learning using graphics processors

Keywords

Computer science, Unsupervised learning, Artificial intelligence, Deep learning, Machine learning

References

• High Performance Convolutional Neural Networks for Document ProcessingKumar Chellapilla, Sidd Puri, Patrice Simard — 2006
• Microprocessors for the new millennium: Challenges, opportunities, and new frontiersPatrick P. Gelsinger — 2001
• Power-constrained CMOS scaling limitsD.J. Frank — 2002
• Feature selection, L1 vs. L2 regularization, and rotational invarianceAndrew Y. Ng — 2004
• Many-core GPU computing with NVIDIA CUDAMark Harris — 2008
• Learning Sparse Overcomplete Codes for ImagesJoseph F. Murray, Kenneth Kreutz-Delgado — 2006
• PATHWISE COORDINATE OPTIMIZATIONJerome H. Friedman, Trevor Hastie, Holger Höfling — 2007
• High-performance implementation of the level-3 BLASKazushige Goto, Robert A. Geijn — 2008
• Large Language Models in Machine TranslationThorsten Brants, Ashok C. Popat, Peng Xu — 2007
• Training Products of Experts by Minimizing Contrastive DivergenceGeoffrey E. Hinton — 2002
• Self-taught learning: transfer learning from unlabeled dataRajat Raina, Alexis Battle, Honglak Lee — 2007
• Convolutional deep belief networks for scalable unsupervised learning of hierarchical representationsHonglak Lee, Roger Grosse, Rajesh Ranganath — 2009
• Differentiable Sparse CodingJ. Andrew Bagnell, David M. Bradley — 2008
• Sparse deep belief net model for visual area V2Honglak Lee, Chaitanya Ekanadham, Andrew Y. Ng — 2007
• Regression Shrinkage and Selection via the LassoRobert Tibshirani — 1996
• A Fast Learning Algorithm for Deep Belief NetsGeoffrey E. Hinton, Simon Osindero, Yee‐Whye Teh — 2006
• Emergence of simple-cell receptive field properties by learning a sparse code for natural imagesBruno A. Olshausen, David J. Field — 1996
• Scaling to Very Very Large Corpora for Natural Language DisambiguationMichele Banko, Eric Brill — 2001
• Fast support vector machine training and classification on graphics processorsBryan Catanzaro, Narayanan Sundaram, Kurt Keutzer — 2008
• Semi-supervised learning of compact document representations with deep networksMarc ' Aurelio Ranzato, Martin Szummer — 2008

Cited by

• User modeling and optimization for environmental planning system designVidya Bhushan Singh — 2014
• Learning in modular systemsJ. Andrew Bagnell, David M. Bradley — 2010
• A Nonparametric Bayesian Approach toward Stacked Convolutional Independent Component AnalysisSotirios Chatzis, Dimitrios Kosmopoulos — 2014
• Quantum Machine Learning: What Quantum Computing Means to Data MiningPéter Wittek — 2014
• CUDAMat: a CUDA-based matrix class for PythonVolodymyr Mnih — 2009
• Thought Recognition: Predicting and Decoding Brain Activity Using the Zero-Shot Learning ModelTom M. Mitchell, Mark Palatucci — 2011
• Apprentissage de représentations et robotique développementale : quelques apports de l'apprentissage profond pour la robotique autonome. (Representation learning and developmental robotics : on the use of deep learning for autonomous robots)Alain Droniou — 2015
• Improving the speed of neural networks on CPUsVincent Vanhoucke, Andrew Senior, M. Mao — 2011
• Learning from heterogeneously distributed data sets using artificial neural networks and genetic algorithmsDiego Peteiro-Barral, Bertha Guijarro‐Berdiñas, Beatriz Pérez‐Sánchez — 2012
• A Survey on Unsupervised Machine Learning Algorithms for Automation, Classification and MaintenanceMemoona Khanum, Tahira Mahboob, Warda Imtiaz — 2015
• Learning Contextualized Music Semantics from Tags via a Siamese NetworkUbai Sandouk, Ke Chen — 2015
• Influence of interaction techniques on vims in virtual environments : estimation et prédictionMohammad Ali Mirzaei — 2014
• CNNTracker: Online discriminative object tracking via deep convolutional neural networkYan Chen, Xiangnan Yang, Bineng Zhong — 2016
• Generative probabilistic models for object segmentationS. M. Ali Eslami — 2014
• Foundations and Advances in Deep LearningKyunghyun Cho — 2014
• Sparse Matrix-Vector Multiplication on GPUArash Ashari — 2014
• Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learningBabak Alipanahi, Andrew Delong, Matthew T. Weirauch — 2015
• Modeling the intronic regulation of Alternative Splicing using Deep Convolutional Neural NetsJohannes Linder — 2015
• Project Adam: Building an Efficient and Scalable Deep Learning Training SystemTrishul Chilimbi, Yutaka Suzue, Johnson Apacible — 2014
• Convergence rates for pretraining and dropout: Guiding learning parameters using network structureVamsi Krishna Ithapu, Sathya N. Ravi, Vikas Singh — 2015

Related papers

• A Review on Deep Learning Algorithms for Speech and Facial Emotion RecognitionCharlyn Pushpa Latha, Mohana Priya — 2016
• Deep Architecture for Traffic Flow Prediction: Deep Belief Networks With Multitask LearningWenhao Huang, Guojie Song, Haikun Hong — 2014
• Deep Learning-Based Detection of Fake Task Injection in Mobile CrowdsensingAnkkita Sood, Murat Şimşek, Yueqian Zhang — 2019
• A survey of deep neural network architectures and their applicationsWeibo Liu, Zidong Wang, Xiaohui Liu — 2017
• Introduction to Deep Learning and its related case studiesRokade Sakshi, Girija — 2022
• Analysis of recent advancement in unsupervised deep learningN. Jeenath Shafana, A. Senthilselvi — 2022
• A survey on deep learning and its applicationsShi Dong, Ping Wang, Khushnood Abbas — 2021
• Research advances in fault diagnosis and prognostic based on deep learningGuangquan Zhao, Guohui Zhang, Qiangqiang Ge — 2016