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Here's a list of 7 top research on arXiV on AI/deep learning for August 2019 as per Daniel Gutierrez

1) A Probabilistic Representation of Deep Learning

Link: https://arxiv.org/pdf/1908.09772v1.pdf

2) Inception-inspired LSTM for Next-frame Video Prediction
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Link: https://arxiv.org/pdf/1909.05622.pdf

3) Systematic Analysis of Image Generation using GANs

Link: https://arxiv.org/ftp/arxiv/papers/1908/1908.11863.pdf
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4) Dynamic Stale Synchronous Parallel Distributed Training for Deep Learning

Link: https://arxiv.org/pdf/1908.11848.pdf

5) Discovering Reliable Correlations in Categorical Data

Link: https://arxiv.org/pdf/1908.11682.pdf

6) Smaller Models, Better Generalization

Link: https://arxiv.org/pdf/1908.11250.pdf

7) An Auto-ML Framework Based on GBDT for Lifelong Learning

Link: https://arxiv.org/pdf/1908.11033.pdf

Source: https://insidebigdata.com/2019/09/18/best-of-arxiv-org-for-ai-machine-learning-and-deep-learning-august-2019/


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Deep Multi-Agent Reinforcement Learning

Jakob N. Foerster : https://ora.ox.ac.uk/objects/uuid:a55621b3-53c0-4e1b-ad1c-92438b57ffa4
HYPE: A Benchmark for Human eYe Perceptual Evaluation of Generative Models

Human evaluation for generative models have been ad-hoc.

They propose a standard human benchmark for generative realism that is grounded in psychophysics research in perception.

https://arxiv.org/abs/1904.01121
https://hype.stanford.edu/
CoSQL: A Conversational Text-to-SQL Challenge Towards Cross-Domain Natural Language Interfaces to Databases
Yu et al.: https://arxiv.org/abs/1909.05378
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Stanford Machine Learning Notes
Andrew Ng
https://www.holehouse.org/mlclass/