[HTML][HTML] Deep learning in optical metrology: a review
With the advances in scientific foundations and technological implementations, optical
metrology has become versatile problem-solving backbones in manufacturing, fundamental …
metrology has become versatile problem-solving backbones in manufacturing, fundamental …
Backpropagation and the brain
During learning, the brain modifies synapses to improve behaviour. In the cortex, synapses
are embedded within multilayered networks, making it difficult to determine the effect of an …
are embedded within multilayered networks, making it difficult to determine the effect of an …
Stablerep: Synthetic images from text-to-image models make strong visual representation learners
We investigate the potential of learning visual representations using synthetic images
generated by text-to-image models. This is a natural question in the light of the excellent …
generated by text-to-image models. This is a natural question in the light of the excellent …
A review and evaluation of the state-of-the-art in PV solar power forecasting: Techniques and optimization
Integration of photovoltaics into power grids is difficult as solar energy is highly dependent
on climate and geography; often fluctuating erratically. This causes penetrations and voltage …
on climate and geography; often fluctuating erratically. This causes penetrations and voltage …
[CYTOWANIE][C] An introduction to variational autoencoders
An Introduction to Variational Autoencoders Page 1 An Introduction to Variational Autoencoders
Page 2 Other titles in Foundations and Trends R in Machine Learning Computational Optimal …
Page 2 Other titles in Foundations and Trends R in Machine Learning Computational Optimal …
Data-efficient image recognition with contrastive predictive coding
O Henaff - International conference on machine learning, 2020 - proceedings.mlr.press
Human observers can learn to recognize new categories of images from a handful of
examples, yet doing so with artificial ones remains an open challenge. We hypothesize that …
examples, yet doing so with artificial ones remains an open challenge. We hypothesize that …
[HTML][HTML] Fundamentals, materials, and machine learning of polymer electrolyte membrane fuel cell technology
Polymer electrolyte membrane (PEM) fuel cells are electrochemical devices that directly
convert the chemical energy stored in fuel into electrical energy with a practical conversion …
convert the chemical energy stored in fuel into electrical energy with a practical conversion …
The Tolman-Eichenbaum machine: unifying space and relational memory through generalization in the hippocampal formation
The hippocampal-entorhinal system is important for spatial and relational memory tasks. We
formally link these domains, provide a mechanistic understanding of the hippocampal role in …
formally link these domains, provide a mechanistic understanding of the hippocampal role in …
[KSIĄŻKA][B] Neural networks and deep learning
CC Aggarwal - 2018 - Springer
“Any AI smart enough to pass a Turing test is smart enough to know to fail it.”–*** Ian
McDonald Neural networks were developed to simulate the human nervous system for …
McDonald Neural networks were developed to simulate the human nervous system for …
Dreamcoder: Bootstrap** inductive program synthesis with wake-sleep library learning
We present a system for inductive program synthesis called DreamCoder, which inputs a
corpus of synthesis problems each specified by one or a few examples, and automatically …
corpus of synthesis problems each specified by one or a few examples, and automatically …