← Optimization & Theory
Deep Learning › Optimization & Theory ›

Efficient Computing

1253 directly classified papers

Papers per year

Papers

TernGrad: Ternary Gradients to Reduce Communication in Distributed Deep Learning NIPS 2017
Flexpoint: An Adaptive Numerical Format for Efficient Training of Deep Neural Networks NIPS 2017
On-the-fly Operation Batching in Dynamic Computation Graphs NIPS 2017
Efficient Use of Limited-Memory Accelerators for Linear Learning on Heterogeneous Systems NIPS 2017
The Reversible Residual Network: Backpropagation Without Storing Activations NIPS 2017
NeuralPower: Predict and Deploy Energy-Efficient Convolutional Neural Networks ACML 2017
On Compressing Deep Models by Low Rank and Sparse Decomposition CVPR 2017
EAST: An Efficient and Accurate Scene Text Detector CVPR 2017
HOPE: Hierarchical Object Prototype Encoding for Efficient Object Instance Search in Videos CVPR 2017
Xception: Deep Learning With Depthwise Separable Convolutions CVPR 2017
Deep Roots: Improving CNN Efficiency With Hierarchical Filter Groups CVPR 2017
Local Binary Convolutional Neural Networks CVPR 2017
Sharp Models on Dull Hardware: Fast and Accurate Neural Machine Translation Decoding on the CPU EMNLP 2017
More Is Less: A More Complicated Network With Less Inference Complexity CVPR 2017
Deep Learning With Low Precision by Half-Wave Gaussian Quantization CVPR 2017
ECO: Efficient Convolution Operators for Tracking CVPR 2017
Efficient Attention using a Fixed-Size Memory Representation EMNLP 2017
Learning Sensor Multiplexing Design through Back-propagation NIPS 2016
Short-Dot: Computing Large Linear Transforms Distributedly Using Coded Short Dot Products NIPS 2016
Maximal Sparsity with Deep Networks? NIPS 2016
Quantized Random Projections and Non-Linear Estimation of Cosine Similarity NIPS 2016
CNNpack: Packing Convolutional Neural Networks in the Frequency Domain NIPS 2016
FPNN: Field Probing Neural Networks for 3D Data NIPS 2016
Quantized Convolutional Neural Networks for Mobile Devices CVPR 2016
Yggdrasil: An Optimized System for Training Deep Decision Trees at Scale NIPS 2016