Learning Plannable Representations with Causal InfoGAN · HackerPapers discussions

Learning Plannable Representations with Causal InfoGAN — mentioned by Two_hands

Mon, 28 Oct 2024 07:01:29 GMT

I've been working a lot on my blog, which entails training and implementing neural networks. My next two posts are going to be about: - https://arxiv.org/pdf/1912.04958 - StyleGAN2 - https://arxiv.org/pdf/1807.09341 - Causal InfoGAN, written in TinyGrad https://ym2132.github.io

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Learning Plannable Representations with Causal InfoGAN — mentioned by guicho271828

Tue, 14 May 2019 05:02:10 GMT

Author here. IBM is currently focusing on the combination of machine learning and reasoning, also called neural-symbolic AI [1], to combine the best of both worlds (noise robustness and explainability, etc.). I am from Tokyo and will join MIT-IBM lab soon to perform this line of research. I have been working on Latplan [AAAI-2018, 2,3,4,5,6], a neural-symbolic classical planning system that learns symbolic representations of noisy images and perform efficient symbolic planning in the latent space. Later, several other groups have started working in this field, like [7]. The problem with…

Paper and discussion history. A mention is not an endorsement.

Learning Plannable Representations with Causal InfoGAN — HN submission

Mon, 30 Jul 2018 17:12:11 GMT

Learning Plannable Representations with Causal InfoGAN [pdf]

3 points; 1 discussion reply on HN. Paper and source history. A submission is not an endorsement.