papers | Deep Learning JP - Part 38

Towards Transparent and Explainable Attention Models

Sharpness-Aware Minimization for Efficiently Improving Generalization

Scaling Laws for Neural Language Models

The frontier of simulation-based inference

Unsupervised Representation Disentanglement Using Cross Domain Features and Adversarial Learning in Variational Autoencoder Based Voice Conversion

Learning to combine top down and bottom-up signals in recurrent neural networks with attention over modules

Toward Fast and Stabilized GAN Training for High-fidelity Few-shot Image Synthesis

Memory Optimization for Deep Networks

wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations (NeurIPS, 2020)

AutoDropout: Learning Dropout Patterns to Regularize Deep Networks

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