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Found 6 papers, 2 papers with code
Reducing Neural Architecture Search Spaces with Training-Free Statistics and Computational Graph Clustering
The computational demands of neural architecture search (NAS) algorithms are usually directly proportional to the size of their target search spaces.
Training Quantised Neural Networks with STE Variants: the Additive Noise Annealing Algorithm
When testing ANA on the CIFAR-10 image classification benchmark, we find that the major impact on task accuracy is not due to the qualitative shape of the regularisations but to the proper synchronisation of the different STE variants used in a network, in accordance with the theoretical results.
ZippyPoint: Fast Interest Point Detection, Description, and Matching through Mixed Precision Discretization
Efficient detection and description of geometric regions in images is a prerequisite in visual systems for localization and mapping.
Proceedings of the DATE Friday Workshop on System-level Design Methods for Deep Learning on Heterogeneous Architectures (SLOHA 2021)
This volume contains the papers accepted at the first DATE Friday Workshop on System-level Design Methods for Deep Learning on Heterogeneous Architectures (SLOHA 2021), held virtually on February 5, 2021.
Analytical aspects of non-differentiable neural networks
Research in computational deep learning has directed considerable efforts towards hardware-oriented optimisations for deep neural networks, via the simplification of the activation functions, or the quantization of both activations and weights.
Additive Noise Annealing and Approximation Properties of Quantized Neural Networks
We present a theoretical and experimental investigation of the quantization problem for artificial neural networks.
