*《Stabilizing Transformers for Reinforcement Learning》E Parisotto, H. F Song, J W. Rae, R Pascanu, C Gulcehre, S M. Jayakumar, M Jaderberg, R L Kaufman, A Clark, S Noury, M M. Botvinick, N Heess, R Hadsell [DeepMind] (2019)

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《Auto-Sizing the Transformer Network: Improving Speed, Efficiency, and Performance for Low-Resource Machine Translation》K Murray, J Kinnison, T Q. Nguyen, W Scheirer, D Chiang [University of Notre Dame] (2019)

Auto-Sizing the Transformer Network Improving Speed, Efficiency, and Performance for Low-Resource Machine Translation.pdf
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【深度学习视频分析/多模态学习资源大列表】'Awesome Deep Learning for Video Analysis - Papers, code and datasets about deep learning for video analysis, multi-modal learning' by Huaizheng GitHub ​​​​

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This is an open sourced book on deep learning. This book is supposed to be mathematically light and caters to the readers who have no experience with deep learning or a strong mathematics background. This book is meant to help readers take their "First Step" towards Deep Learning.

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The tutorial is written for those who would like an introduction to reinforcement learning (RL). The aim is to provide an intuitive presentation of the ideas rather than concentrate on the deeper mathematics underlying the topic. RL is generally used to solve the so-called Markov decision problem (MDP). In other words, the problem that you are attempting to solve with RL should be an MDP or its variant. The theory of RL relies on dynamic programming (DP) and artificial intelligence (AI). We will begin with a quick description of MDPs. We will discuss what we mean by “complex” and “large-scale” MDPs. Then we will explain why RL is needed to solve complex and large-scale MDPs. The semi-Markov decision problem (SMDP) will also be covered.

The tutorial is meant to serve as an introduction to these topics and is based mostly on the book: “Simulation-based optimization: Parametric Optimization techniques and reinforcement learning” [4]. The book discusses this topic in greater detail in the context of simulators. There are at least two other textbooks that I would recommend you to read: (i) Neuro-dynamic programming [2] (lots of details on convergence analysis) and (ii) Reinforcement Learning: An Introduction [11] (lots of details on underlying AI concepts). A more recent tutorial on this topic is [8]. This tutorial has 2 sections: • Section 2 discusses MDPs and SMDPs. • Section 3 discusses RL. By the end of this tutorial, you should be able to • Identify problem structures that can be set up as MDPs / SMDPs. • Use some RL algorithms.

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The State of Machine Learning Frameworks in 2019

In 2019, the war for ML frameworks has two remaining main contenders: PyTorch and TensorFlow. My analysis suggests that researchers are abandoning TensorFlow and flocking to PyTorch in droves. Meanwhile in industry, Tensorflow is currently the platform of choice, but that may not be true for long.

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机器学习可解释性,Interpretability and Explainability in Machine Learning

  • Overview As machine learning models are increasingly being employed to aid decision makers in high-stakes settings such as healthcare and criminal justice, it is important to ensure that the decision makers (end users) correctly understand and consequently trust the functionality of these models. This graduate level course aims to familiarize students with the recent advances in the emerging field of interpretable and explainable ML. In this course, we will review seminal position papers of the field, understand the notion of model interpretability and explainability, discuss in detail different classes of interpretable models (e.g., prototype based approaches, sparse linear models, rule based techniques, generalized additive models), post-hoc explanations (black-box explanations including counterfactual explanations and saliency maps), and explore the connections between interpretability and causality, debugging, and fairness. The course will also emphasize on various applications which can immensely benefit from model interpretability including criminal justice and healthcare.
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Differentiable Graphics with TensorFlow 2.0

Deep learning has introduced a profound paradigm change in the recent years, allowing to solve significantly more complex perception problems than previously possible. This paradigm shift has positively impacted a tremendous number of fields with a giant leap forward in computer vision and computer graphics algorithms. The development of public libraries such as Tensorflow are in a large part responsible for the massive growth of AI. These libraries made deep learning easily accessible to every researchers and engineers allowing fast advances in developing deep learning techniques in the industry and academia. We will start this course with an introduction to deep learning and present the newly released TensorFlow 2.0 with a focus on best practices and new exciting functionalities. We will then show different tips, tools, and algorithms to visualize and interpret complex neural networks by using TensorFlow. Finally, we will introduce a novel TensorFlow library containing a set of graphics inspired differentiable layers allowing to build structured neural networks to solve various two and three dimensional perception tasks. To make the course interactive we will punctuate the presentations with real time demos in the form of Colab notebooks. Basic prior familiarity with deep learning will be assumed.** Deep learning has introduced a profound paradigm change in the recent years, allowing to solve significantly more complex perception problems than previously possible. This paradigm shift has positively impacted a tremendous number of fields with a giant leap forward in computer vision and computer graphics algorithms. The development of public libraries such as Tensorflow are in a large part responsible for the massive growth of AI. These libraries made deep learning easily accessible to every researchers and engineers allowing fast advances in developing deep learning techniques in the industry and academia. We will start this course with an introduction to deep learning and present the newly released TensorFlow 2.0 with a focus on best practices and new exciting functionalities. We will then show different tips, tools, and algorithms to visualize and interpret complex neural networks by using TensorFlow. Finally, we will introduce a novel TensorFlow library containing a set of graphics inspired differentiable layers allowing to build structured neural networks to solve various two and three dimensional perception tasks. To make the course interactive we will punctuate the presentations with real time demos in the form of Colab notebooks. Basic prior familiarity with deep learning will be assumed.

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