To be precise, convnets have the built-in ability to be translation-invariant, ignoring image-boundary effects. They can also learn partly or non-invariant features, which is good, but depending on the data may result in not learning invariances that are present.
MACHINE LEARNING
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Symmetry Reduces Parameters Through Parameter Tying
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Symmetry can also exponentially reduce the number of parameters by parameter tying.
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Simulating All Possibilities to Avoid Being Wrong
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This is why I simulate all possibilities, so I’m technically never wrong.
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South Jersey Gas Completes AI Boot Camp in Atlantic City
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South Jersey Gas wraps artificial intelligence boot camp in Atlantic City – Press of Atlantic City Read more here: https://
ift.tt/wSZQN5v #ArtificialIntelligence #AI #DataScience #100DaysOfCode #Python #MachineLearning #BigData #DeepLearning #NLP #Robots #IoT -
DiffusionDB: 2 Million Stable Diffusion Images Dataset
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Meet This Artificial Intelligence (AI) Image Dataset Called 'DIFFUSIONDB,' That Consists of 2 Million Stable Diffusion Images, And Their Text Prompts And Hyperparameters – MarkTechPost Read more here: https://
ift.tt/QPqDWRJ #ArtificialIntelligence #AI #DataScience #100DaysOf… -
ConvNets Translation-Invariance and Layer Equivariance Properties
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The output of a convnet is translation-invariant by design. Individual intermediate layers are equivariant. (Transformers may or may not be invariant/equivariant, btw.)
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Full Distribution Access Versus Limited Sample Data
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Ignore "infinite". What we really mean is having access to the the full distribution rather than just a sample.
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Innate Priors Shape AI Learning: Evolution Over Minimalism
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– you still need some prior to organize that infinite data and decide how to generalize from it
– the choice to minimize is an aesthetic choice, not a scientific one; evolution has clearly endowed many animals with significant priors (eg baby ibex climbing down a mountain). -
Deep Learning and AGI: Expectations vs Reality
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Complaining deep learning is not AGI is like complaining a car doesn't fly.
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Inductive Biases: Making Implicit Assumptions About Data Explicit
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Inductive biases are always assumptions about the world generating the data. They may, however, be expressed very opaquely as network structure or even arbitrary hacks (easy and common but bad; you should make your assumptions explicit and then test/modify/refute them).