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Neural Networks

11300 directly classified papers

Papers per year

Papers

PerforatedCNNs: Acceleration through Elimination of Redundant Convolutions NIPS 2016
A Theoretically Grounded Application of Dropout in Recurrent Neural Networks NIPS 2016
Exponential expressivity in deep neural networks through transient chaos NIPS 2016
Interaction Networks for Learning about Objects, Relations and Physics NIPS 2016
An Architecture for Deep, Hierarchical Generative Models NIPS 2016
Phased LSTM: Accelerating Recurrent Network Training for Long or Event-based Sequences NIPS 2016
Synthesizing the preferred inputs for neurons in neural networks via deep generator networks NIPS 2016
Backprop KF: Learning Discriminative Deterministic State Estimators NIPS 2016
Using Fast Weights to Attend to the Recent Past NIPS 2016
Learning What and Where to Draw NIPS 2016
Toward Deeper Understanding of Neural Networks: The Power of Initialization and a Dual View on Expressivity NIPS 2016
Unsupervised Domain Adaptation with Residual Transfer Networks NIPS 2016
Tensor Switching Networks NIPS 2016
Dense Associative Memory for Pattern Recognition NIPS 2016
Deep Learning without Poor Local Minima NIPS 2016
On Multiplicative Integration with Recurrent Neural Networks NIPS 2016
Composing graphical models with neural networks for structured representations and fast inference NIPS 2016
Architectural Complexity Measures of Recurrent Neural Networks NIPS 2016
Integrated perception with recurrent multi-task neural networks NIPS 2016
Automatic Neuron Detection in Calcium Imaging Data Using Convolutional Networks NIPS 2016
Convolutional Neural Fabrics NIPS 2016
Image Restoration Using Very Deep Convolutional Encoder-Decoder Networks with Symmetric Skip Connections NIPS 2016
Joint Line Segmentation and Transcription for End-to-End Handwritten Paragraph Recognition NIPS 2016
Combinatorial Energy Learning for Image Segmentation NIPS 2016
Spatiotemporal Residual Networks for Video Action Recognition NIPS 2016