Hype | MachLearning

[share]271	@hardmaru	2021-01-18
Before doing a deep dive into a paper: [twitter]

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Get as Jupyter Notebook @a2c.fyi

2021-01-17

[R] Evolving Reinforcement Learning Algorithms[reddit]https://arxiv.org/abs/2101.03958/r/MachineLearningThe set of environments we use for both training and testing could also be expanded to include a more diverse set of problem types

We propose a method for meta-learning reinforcement learning algorithms by searching over the space of computational graphs which compute the loss function for a value-based model-free RL agent to optimize. The learned algorithms are domain-agnostic and can generalize to new environments not seen during training. Our method can both learn from scratch and bootstrap off known existing algorithms, like DQN, enabling interpretable modifications which improve performance. Learning from scratch on simple classical control and gridworld tasks, our method rediscovers the temporal-difference (TD) algorithm. Bootstrapped from DQN, we highlight two learned algorithms which obtain good generalization performance over other classical control tasks, gridworld type tasks, and Atari games. The analysis of the learned algorithm behavior shows resemblance to recently proposed RL algorithms that address overestimation in value-based methods.

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2021-01-17

What do you do when the dataset for the problem you're trying to solve does not exist?[reddit]/r/datasets

Hello, I am currently working on a final year project. I have decided that I want to work with summarising textbooks using seq2seq.

I have found some implementations of seq2seq text summarization using TensorFlow on GitHub, so I have examples of working solutions I can learn from.

I have run into the problem of not being able to find any datasets specifically where textbooks are given with summaries.

However, I have managed to find datasets for other types of texts; TensorFlow's Scientific papers dataset. Where papers are given with abstracts (which can be treated as a summary).

I am not an expert in ML, but I am considering using these datasets and then fine-tuning the model using a small dataset I will manually create with Textbooks and summaries.

There are some similar characteristics between the data, so I would assume that the model would be able to transfer some things it learns from the scientific papers to the textbooks.

I do not have the slightest clue about how reasonable or successful this might turn out. I am looking any advice, resources or alternatives to what I have mentioned above.

What do you do when the dataset for the problem you're trying to solve does not exist?

[share]291	@TensorFlow	2021-01-17
Art imitates life @jason_mayes is joined by @emilyxxie to learn how she brought the Mona Lisa painting to life using a combination of TensorFlow.js and TensorFlow Core. [goo.gle] #MadewithTFJS [twitter]

[share]226	@ylecun	2021-01-17
Inspiring speech by Nobel laureate, Caltech professor, and newly-appointed Presidential Science Advisor Frances Arnold @francesarnold "Science is an act of love for our planet and for people" I'm so thrilled by the new team at OSTP! #ScienceIsBack [msn.com]

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2021-01-17

[D] Significance of Data Structures and Algorithms in the era of Machine Learning?[reddit]/r/MachineLearning

Hi, I am a computer science major and I hear the hype about Machine Learning among my fellow students. However, jumping directly to machine learning without understanding data structure and algorithms seems futile.

What do you think?

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2021-01-17

Covid chest imaging dataset for AI training. Need to request access.[reddit][gov.uk]/r/datasets

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2021-01-17

[R][P] graphkit-learn: A Python Library for Graph Kernels Based on Linear Patterns[reddit][sciencedirect.com]/r/MachineLearning

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