DjiNN and Tonic: DNN as a Service and Its Implications for Future Warehouse Scale Computers Johann Hauswald Yiping Kang Michael A. Laurenzano Quan Chen Cheng Li Trevor Mudge Ronald G. Dreslinski Jason Mars Lingjia Tang C

Source URL: web.eecs.umich.edu

• Computing
• Computer architecture
• GPGPU
• Parallel computing
• Computer engineering
• Computational neuroscience
• Graphics hardware
• Deep learning
• DNN
• Graphics processing unit
• Artificial neural network
• Convolutional neural network

Segment-Phrase Table for Semantic Segmentation, Visual Entailment and Paraphrasing Hamid Izadinia† Fereshteh Sadeghi† Santosh K. Divvala‡,† Hannaneh Hajishirzi† Yejin Choi† †

Source URL: ai2-website.s3.amazonaws.com

• Computational linguistics
• Image segmentation
• Conference on Computer Vision and Pattern Recognition
• Semantic similarity
• Artificial intelligence
• Market segmentation
• Deep learning
• Natural language processing
• Computational science
• Science and technology

An Analysis of Single-Layer Networks in Unsupervised Feature Learning Adam Coates Stanford University Computer Science Dept.

Source URL: ai.stanford.edu

• Machine learning
• Statistics
• Artificial intelligence
• Learning
• Feature learning
• Deep learning
• K-means clustering
• Pattern recognition
• Supervised learning
• Statistical classification
• Artificial neural network
• Cluster analysis

Multimodal Deep Learning Jiquan Ngiam1 Aditya Khosla1

Source URL: cs.stanford.edu

Machine Learning Techniques (機器學習技法) Lecture 13: Deep Learning Hsuan-Tien Lin (林軒田)

Source URL: www.csie.ntu.edu.tw

arXiv:1604.00066v1 [cs.CV] 31 MarTo Fall Or Not To Fall: A Visual Approach to Physical Stability Prediction

Source URL: arxiv.org

• Operations research
• Simulation
• Rendering
• Convolutional neural network
• Deep learning
• Computer vision
• 3D computer graphics

Simple Evolutionary Optimization Can Rival Stochastic Gradient Descent in Neural Networks In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCONew York, NY: ACM Nominated for Best Paper Aw

Source URL: eplex.cs.ucf.edu

• Artificial neural networks
• Applied mathematics
• Artificial intelligence
• Machine learning
• Neuroevolution
• Evolutionary algorithm
• Deep learning
• Backpropagation
• Stochastic gradient descent
• HyperNEAT
• Genetic algorithm
• Multilayer perceptron

Artificial neural networks and deep learning Jack Baker February 20, 2015 Artificial neural networks and deep learning

Source URL: www.lancaster.ac.uk

Going Deeper with Convolutions Christian Szegedy1 , Wei Liu2 , Yangqing Jia1 , Pierre Sermanet1 , Scott Reed3 , Dragomir Anguelov1 , Dumitru Erhan1 , Vincent Vanhoucke1 , Andrew Rabinovich4 1 Google Inc. 2 University of

Source URL: www.cs.unc.edu

• Artificial neural networks
• Computational neuroscience
• Artificial intelligence
• Computer vision
• Convolutional neural network
• Deep learning
• Machine learning
• MNIST database
• Vanishing gradient problem
• Receptive field
• Network architecture
• OSI model

ADVANCES IN OPTIMIZING RECURRENT NETWORKS Yoshua Bengio, Nicolas Boulanger-Lewandowski and Razvan Pascanu arXiv:1212.0901v2 [cs.LG] 14 DecU. Montreal

Source URL: arxiv.org

• Artificial neural networks
• Applied mathematics
• Machine learning
• Cybernetics
• Recurrent neural network
• Deep learning
• Long short-term memory
• Stochastic gradient descent
• Convolutional neural network
• Rectifier
• Backpropagation
• Gradient descent