A review of the state-of-the-art deep learning techniques. Nov 21.
You can attend for FREE! Here is what you'll learn: • Experiment and iterate on models
• Evaluate results
• Operationalize them https://
eventbrite.com/e/ai-assisted-
data-science-tony-stark-and-jarvis-tickets-458646302437
…
MACHINE LEARNING
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Free Webinar on Deep Learning Techniques and Model Operationalization
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CRINGE Loss: Learning What Language Not to Model
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The CRINGE Loss: Learning what language not to model Adolphs et al.: https://
arxiv.org/abs/2211.05826 #ArtificialIntelligence #DeepLearning #MachineLearning -
New Brain Model Paves Way for Conscious AI
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A New Brain Model Could Pave the Way for Conscious AI https://
scitechdaily.com/a-new-brain-mo
del-could-pave-the-way-for-conscious-ai/
… @SciTechDaily1 #Healthcare #AI #MachineLearning #BigData #Analytics #DataScience #100DaysofCode #IoT #serverless #DEVCommunity #womenwhocode #DeepLearning #DigitalTransformation #Robots #Python -
AI and Computer Vision Transform Self-Checkout Experience
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Something Unexpected in Bagging Area? #AI #Cloud #ComputerVision Transforms Self
Checkout #CX & Reduces #waste @insightdottech @Wintec_China https://
insight.tech/retail/ai-and-
computer-vision-accelerate-self-checkout?utm_source=twitter&utm_medium=organic&utm_campaign=2022-tdc-eaves
… #Retail #IntelPartner #SupplyChain #fintech #POS #Foodie @ljbloomfield1 #AgriTech #SDGs #IoT @fpcfreshtalk -
Need for Comprehensive Inductive Bias Zoo Reference
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I cover the highest levels in "The Master Algorithm". Below that there are subfield-specific ones. But someone should really do a serious Inductive Bias Zoo, a la Scott Aaronson's Complexity Zoo.
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Causal Theories as Inductive Bias in Machine Learning
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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.)
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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.