
Training with Quantization Noise for Extreme Model Compression
We tackle the problem of producing compact models, maximizing their accu...
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Training with Quantization Noise for Extreme FixedPoint Compression
We tackle the problem of producing compact models, maximizing their accu...
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C3DPO: Canonical 3D Pose Networks for NonRigid Structure From Motion
We propose C3DPO, a method for extracting 3D models of deformable object...
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And the Bit Goes Down: Revisiting the Quantization of Neural Networks
In this paper, we address the problem of reducing the memory footprint o...
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Equinormalization of Neural Networks
Modern neural networks are overparametrized. In particular, each rectif...
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Unsupervised learning with sparse spaceandtime autoencoders
We use spatiallysparse two, three and four dimensional convolutional au...
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The iisignature library: efficient calculation of iteratedintegral signatures and log signatures
Iteratedintegral signatures and log signatures are vectors calculated f...
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3D Semantic Segmentation with Submanifold Sparse Convolutional Networks
Convolutional networks are the defacto standard for analyzing spatiote...
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LargeScale 3D Shape Reconstruction and Segmentation from ShapeNet Core55
We introduce a largescale 3D shape understanding benchmark using data a...
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Submanifold Sparse Convolutional Networks
Convolutional network are the defacto standard for analysing spatiotem...
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LowPrecision BatchNormalized Activations
Artificial neural networks can be trained with relatively lowprecision ...
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Confusing Deep Convolution Networks by Relabelling
Deep convolutional neural networks have become the gold standard for ima...
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Fractional MaxPooling
Convolutional networks almost always incorporate some form of spatial po...
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Spatiallysparse convolutional neural networks
Convolutional neural networks (CNNs) perform well on problems such as ha...
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Sparse arrays of signatures for online character recognition
In mathematics the signature of a path is a collection of iterated integ...
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Benjamin Graham
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