On the contrary: it's by viewing causal theories as a type of inductive bias that ML types can become comfortable with using them. (As I said before, there's a taxonomy of inductive biases; by placing a bias in the appropriate node you don't confuse, you clarify.)
MACHINE LEARNING
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Contrived Datasets and Knowledge Transfer in Causal Analysis
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That's a very contrived data set, which makes it very unpersuasive. You then have to transfer knowledge to this problem from similar ones with better data, which is what the "causal bias" is really doing.
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Guided Image Inpainting for Video Object Removal
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Ever wondered how object removal can be done on a video?
— Satya Mallick (@LearnOpenCV) 14 novembre 2022
Here is an interesting one on Guided Image Inpainting
Paper: https://t.co/I1SBGjzEgh
Code: https://t.co/ssHayfP8EW #learnopencv #opencv #computervision #github #artificialintelligence #deeplearning #machinelearning #ai pic.twitter.com/TWgsUFdf4LEver wondered how object removal can be done on a video?
Here is an interesting one on Guided Image Inpainting Paper: https://
arxiv.org/pdf/2204.07845
.pdf
… Code: https://
github.com/runwayml/guide
d-inpainting
… #learnopencv #opencv #computervision #github #artificialintelligence #deeplearning #machinelearning #ai -
Learning Causality Does Not Require Different Training and Test Domains
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Surely showing you can learn (what looks like) causality does not require the training and test examples to be in different domains. That's an orthogonal issue.
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Machine Learning Model for Soldier Casualty Prediction from Shot Data
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Easy. The data is vectors of "shot/not shot" (one input variable per soldier) and "dead/alive" (output). Any learner that induces that num. shots > 0 => dead answers the question correctly.
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Inductive Biases in Machine Learning: Beyond Curve-Fitting
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1. Many inductive biases aren’t curve-fitting (e.g., ILP, causal theories).
2. You can’t learn at any level without inductive biases.
3. We also call everything that doesn’t work “inductive biases”. ML is the search for the ones that do, in any form. -
Top IoT Sensor Types: Analytics, Machine Learning and AI
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Great share @Analytics_699 ! Thanks for the mention! Top #IoT Sensor Types #Analytics #MachineLearning #AI #Python #BigData #5G #CyberSecurity #IoT #IIoT #InternetOfThings #SmartCities #programming #Coding #100DaysOfCode http://
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Machine Learning Progress with Minimal Data and Inductive Biases
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Indeed, but the interesting ML question is how far you can get with how little. Find the right inductive biases, and you could get very far very quickly.
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Robot Grasping: Singulated Objects and Dex-Net Comparison
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Interesting! I'm probably wrong but are all of these examples singulated objects with upward-facing planar surfaces? Curious how it compares with Dex-Net: https://t.co/xGN576WZQU https://t.co/aajdKWx3Nl
— Ken Goldberg (@Ken_Goldberg) 13 novembre 2022Interesting! I'm probably wrong but are all of these examples singulated objects with upward-facing planar surfaces? Curious how it compares with Dex-Net: https://
roboticsbusinessreview.com/news/how-ambid
extrous-plans-to-grow-its-robot-grasping-company/
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Infinite Data and Finite Representations of True Distributions
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“Infinite data” means knowing the true distribution, which may have a finite representation. Translating from an infinite representation to an equivalent finite one is not inductive bias.