Deep Learning
New computational algorithms make it possible to build neural networks with many input nodes and many layers, and distinguish "deep learning" of these networks from previous work on artificial neural nets.
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AT opological Filter for Learning with Label Noise Pengxiang Wu
Neural Information Processing Systems
Noisy labels can impair the performance of deep neural networks.
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On the distance between two neural networks and the stability of learning Jeremy Bernstein Caltech
Neural Information Processing Systems
Python code used in this paper is here: https://github.com/jxbz/fromage .
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Differentiable Spike: Rethinking Gradient-Descent for Training Spiking Neural Networks Y uhang Li
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Spiking neural networks (SNNs) have emerged as a biology-inspired method mimicking the spiking nature of brain neurons.
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ContraGAN: Contrastive Learning for Conditional Image Generation
Neural Information Processing Systems
The discriminator of ContraGAN discriminates the authenticity of given samples and minimizes a contrastive objective to learn the relations between training images.
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