This is related to a theorem that I have proved and its relation (or not) to an existing result. Essentially, I have shown that PAC-learning is undecidable in the Turing sense. The arxiv link to the paper is https://arxiv.org/abs/1808.06324 I am told that this is provable as a corollary of existing results. I was hinted that the fundamental theorem of statistical machine learning that relates the VC dimension and PAC-learning could be used to prove the undecidability of PAC-learning.
Recently, Neural Architecture Search is coming back to the research spotlight. For example, there is Weight Agnostic Neural Network (WANN) https://arxiv.org/abs/1906.04358 that demonstrates that Neural Architectures can be more significant than the weights of the network. Are researchers just making up new Neural Architecture Search methods for publication, or is there really a big difference? Are there any work that focused on a detailed comparison for Neural Architecture Search.
If you have trouble reading this email, see it on a web browser. Work in the AI field is moving forward very quickly. Today Papers with Code announced their partnership with arXiv, where code links are now shown on arXiv articles, and authors can submit code through arXiv, making it a great addition to avid researchers and practitioners. NeurIPS also announced a cool challenge, the 2020 ML Reproducibility Challenge sponsored by Papers with Code, encouraging people who work with ML to participate (including enthusiasts!). If you'd like to learn more, check out their announcement, it sounds pretty neat.
Abstract: We show how to teach machines to paint like human painters, who can use a few strokes to create fantastic paintings. By combining the neural renderer and model-based Deep Reinforcement Learning (DRL), our agent can decompose texture-rich images into strokes and make long-term plans. For each stroke, the agent directly determines the position and color of the stroke. Excellent visual effect can be achieved using hundreds of strokes. The training process does not require experience of human painting or stroke tracking data.