ImageNet Classification with Deep Convolutional Neural Networks

https://arxiv.org/abs/1603.05027 Recurrent Neural Network Regularization - https://arxiv.org/abs/1409.2329 Deep Speech 2: End-to-End Speech Recognition in English and Mandarin - https://arxiv.org/abs/1512.02595 Order Matters: Sequence to Sequence for Sets - https://arxiv.org/abs/1511.06391 Neural Machine Translation by Jointly Learning to Align and Translate - https://arxiv.org/abs/1409.0473 Pointer Networks - https://arxiv.org/abs/1506.03134 Attention Is All You Need - https://arxiv.org/abs/1706.03762 The Annotated Transformer - https://nlp.seas.harvard.edu/annotated-transformer/ Neural Turing Machines - https://arxiv.org/abs/1410.5401 A Simple Neural Network Module for Relational Reasoning - https://arxiv.org/abs/1706.01427 Relational Recurrent Neural Networks - https://arxiv.org/abs/1806.01822 Neural Message Passing for Quantum Chemistry - https://arxiv.org/abs/1704.01212 Scaling Laws for Neural Language Models - https://arxiv.org/abs/2001.08361 GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism - https://arxiv.org/abs/1811.06965 Keeping Neural Networks Simple by Minimizing the Description Length of the Weights - https://www.cs.toronto.edu/~hinton/absps/colt93.pdf A Tutorial Introduction to the Minimum Description Length Principle - https://arxiv.org/abs/math/0406077 The First Law of Complexodynamics - https://scottaaronson.blog/?p=762 Quantifying the Rise and Fall of Complexity in Closed Systems: The Coffee Automaton - https://arxiv.org/abs/1405.6903 Kolmogorov Complexity - https://onlinelibrary.wiley.com/doi/book/10.1002/047174882X Variational Lossy Autoencoder - https://arxiv.org/abs/1611.02731 Machine Super Intelligence - https://www.vetta.org/documents/Machine_Super_Intelligence.p...

architectural tweaks and optimization (incl. handwritten GPU kernels) to get a CNN to work at this scale. The introduction to the AlexNet paper clearly sets out what prior work existed and what their own contributions were in taking it to this level. https://proceedings.neurips.cc/paper/2012/file/c399862d3b9d6... There's nothing wrong with Schmidhuber expecting prior work to get recognition, but his own work built on that of others as did work that came later. I'm sure he'd have loved to enter ImageNet in 2012, but Hinton's team beat him to it and opened everyone's eyes to the possibility of training neural nets at that scale.

not much. The main area where GAs have seen use in the field so far is optimization problems with a lot of parameters related in unknown or hard to model ways or where a closed form solution is unknown or computationally too expensive (NP). These include shipping and air travel routing (traveling salesman with multiple optimization goals like distance + time + fuel + depreciation), circuit board and IC layout, antenna design for exotic RF modulations, drug discovery, materials science, etc. Problems like these are fairly easy to map to a GA, and current GAs are pretty good at finding local maxima in these functions. Still those are a far cry from "design ex nihilo," which is really the promise that evolutionary computation carries. Those applications are using GAs as a bigger brother to things like Monte Carlo and simulated annealing. One area that I'd look into if I were doing this now would be a hybrid approach where a GA is used to design deep learning architectures and their associated parameters. Seems like it could be very powerful but damn would that ever take a lot of computing power. The fitness function of the GA would consist of N deep learning runs for each candidate. Luckily GAs are parallelizable to an almost unlimited degree.