A Theoretical Analysis of Deep Q-Learning https://arxiv.org/abs/1901.00137
arXiv.org
A Theoretical Analysis of Deep Q-Learning
Despite the great empirical success of deep reinforcement learning, its theoretical foundation is less well understood. In this work, we make the first attempt to theoretically understand the deep...
An Experimental Course on Operating Systems https://web.stanford.edu/class/cs140e/
A Comprehensive Survey on Graph Neural Networks https://arxiv.org/abs/1901.00596
arXiv.org
A Comprehensive Survey on Graph Neural Networks
Deep learning has revolutionized many machine learning tasks in recent years, ranging from image classification and video processing to speech recognition and natural language understanding. The...
Forwarded from Deep Learning
NeurIPS 2018 Paper Summary and Categorization on Reinforcement Learning 👉🏻
Medium
NeurIPS 2018 Reinforcement Learning Summary
This is your one stop shop for everything RL at NeurIPS 2018
Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context -- SOTA on 5 datasets https://arxiv.org/abs/1901.02860
arXiv.org
Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context
Transformers have a potential of learning longer-term dependency, but are limited by a fixed-length context in the setting of language modeling. We propose a novel neural architecture...
Panoptic Feature Pyramid Networks https://arxiv.org/abs/1901.02446
arXiv.org
Panoptic Feature Pyramid Networks
The recently introduced panoptic segmentation task has renewed our community's interest in unifying the tasks of instance segmentation (for thing classes) and semantic segmentation (for stuff...
Auto-DeepLab: Hierarchical Neural Architecture Search for Semantic Image Segmentation https://arxiv.org/abs/1901.02985
arXiv.org
Auto-DeepLab: Hierarchical Neural Architecture Search for Semantic...
Recently, Neural Architecture Search (NAS) has successfully identified neural network architectures that exceed human designed ones on large-scale image classification. In this paper, we study NAS...
Forwarded from Valery Kirichenko
YouTube
The most unexpected answer to a counting puzzle
Solution: https://youtu.be/6dTyOl1fmDo
Light-based solution: https://youtu.be/brU5yLm9DZM
Help fund future projects: https://www.patreon.com/3blue1brown
An equally valuable form of support is to simply share some of the videos.
Special thanks to these supporters:…
Light-based solution: https://youtu.be/brU5yLm9DZM
Help fund future projects: https://www.patreon.com/3blue1brown
An equally valuable form of support is to simply share some of the videos.
Special thanks to these supporters:…
RetinaMask: Learning to predict masks improves state-of-the-art single-shot detection for free https://arxiv.org/abs/1901.03353
arXiv.org
RetinaMask: Learning to predict masks improves state-of-the-art...
Recently two-stage detectors have surged ahead of single-shot detectors in
the accuracy-vs-speed trade-off. Nevertheless single-shot detectors are
immensely popular in embedded vision...
the accuracy-vs-speed trade-off. Nevertheless single-shot detectors are
immensely popular in embedded vision...