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Found 3 papers, 1 papers with code
Learning and Inference in Sparse Coding Models with Langevin Dynamics
Moreover we show that Langevin dynamics lead to an efficient procedure for sampling from the posterior distribution in the 'L0 sparse' regime, where latent variables are encouraged to be set to zero as opposed to having a small L1 norm.
Design of optical neural networks with component imprecisions
For the benefit of designing scalable, fault resistant optical neural networks (ONNs), we investigate the effects architectural designs have on the ONNs' robustness to imprecise components.
3DQ: Compact Quantized Neural Networks for Volumetric Whole Brain Segmentation
Model architectures have been dramatically increasing in size, improving performance at the cost of resource requirements.
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