Recently I had my first-ever experiment with training a neural net to generate images, when I trained StyleGAN2 to generate screenshots from The Great British Bakeoff. Although recognizable as the baking show, it was a highly distorted, nightmarish version - it would have helped had the neural net been able to keep track of how many faces humans have
Spatiotemporal structure of neural population dynamics in the motor system on Simons Foundation
Graph Neural Networks (GNNs) have shown great ability in modeling graph-structured data for various domains. However, GNNs are known as black-box models that lack interpretability. Without understanding their inner working, we cannot fully trust them, which largely limits their adoption in high-stake scenarios. Though some initial efforts have been taken to interpret the predictions of GNNs, they mainly focus on providing post-hoc explanations using an additional explainer, which could misrepresent the true
# Neural Variational Inference: Variational Autoencoders and Helmholtz machines So far we had a little of "neural" in our VI methods. Now it's time to fix it, as we're going to consider Variational Autoencoders (VAE), a paper by D. Kingma and M. Welling, which made a lot of buzz in ML community. It has 2 main contributions: a new approach (AEVB) to large-scale inference in non-conjugate models with continuous latent variables, and a probabilistic model of autoencoders as an example of this approach. We the
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Having looked in some detail at the Ising model, we are now well equipped to tackle a class of neuronal networks that has been studied by several authors in the sixties, seventies and early eighties of the last century, but has become popular by an article [1] published by J. Hopfield in 1982. The idea
Voice cloning technology relies on deep learning neural networks, particularly recurrent neural networks (RNNs) and convolutional neural networks
Blog Topics Advertise Join Newsletter Don’t Use Dropout in Convolutional Networks If you are wondering how to implement dropout, here is your answer - including an explanation on when to use dropout, an implementation example with Keras, batch normalization, and more. --> comments By Harrison Jansma . I have noticed that there is an abundance of resources for learning the what and why of deep learning. Unfortunately when it comes time to make a model, their are very few resources explaining the when and
# ensemble learning ## XGBoost vs Random Forest: Why They Win in Industry (2026 Guide) June 30, 2026June 30, 2026 by Pawan Kumar Fageria Machine Learning Series · Algorithm Deep-Dive XGBoost and Random Forest: Why These Algorithms Win in Industry (2026) 🌲 Tree Ensembles ⏱ 16 min read 🗓 Updated 2026 #1Default choice for tabular data > Deep LearningOn row-and-column business data 2 StylesBagging vs Boosting Here is something that surprises beginners obsessed with deep learning and neural networks
Covers SGD for training neural networks: mini-batch updates, learning rate selection, cosine annealing, warmup, Nesterov momentum, and loss landscape geometry