Although Computer Vision (CV) has only exploded recently (the breakthrough moment happened in 2012 when <a href="https://en.wikipedia.org/wiki/AlexNet" target="_blank">AlexNet won ImageNet</a>), it certainly isn’t a new scientific field.
We compare how intrinsic and recurrent temporal adaptation mechanisms in deep neural networks affect object recognition under challenging conditions. We find intrinsic adaptation is superior for recognizing simple, high-contrast objects in noise, whereas recurrent adaptation better maintains coherence under dynamic occlusion and improves novelty detection. These results indicate that robust object recognition likely depends on multiple parallel adaptation strategies
Understanding how neural networks transform inputs into outputs is crucial for interpreting and manipulating their behavior. Most existing approaches analyze internal
a dense model is an artificial neural network where every single parameter — the mathematical weights that hold the model's learned knowledge — participates in processing every single piece of information you give it
24 Nov Retweet this Share on Facebook 02 Jul Retweet this Share on Facebook The value of craft after software sounds rampant sometimes, expressing the freedom of escaping repetitive taps and clicks to accomplish some assumed tasks. Mixing media, electricity, electronics, mechanics and inert objects Graham Dunning has realised a structured track/performance/open script in his “ Mechanical Techno: Ghost in the Machine Music .” More than a proof of concept a machine music declination. 30 Jun Retweet this Sh
A classical neural network is just a function $f$ that takes in an array of inputs, $x_i$, and yields an output array, $y_j$. The input and output usually ha
TensorFlow is an open-source software library for data analysis and machine learning. In this blog post, we'll be using it to build a neural network for
Sobolev.space Random notes mostly on Machine Learning Neural Variational Inference: Importance Weighted Autoencoders July 14, 2016 Previously we covered Variational Autoencoders (VAE) — popular inference tool based on neural networks. In this post we'll consider, a followup work from Torronto by Y. Burda, R. Grosse and R. Salakhutdinov, Importance Weighted Autoencoders (IWAE). The crucial contribution of this work is introduction of a new lower-bound on the marginal log-likelihood $\log p(x)$ which
## Priorless Recurrent Networks Learn Curiously Jeff Mitchell , Jeffrey Bowers ##### Abstract Recently, domain-general recurrent neural networks, without explicit linguistic inductive biases, have been shown to successfully reproduce a range of human language behaviours, such as accurately predicting number agreement between nouns and verbs. We show that such networks will also learn number agreement within unnatural sentence structures, i.e. structures that are not found within any natural languages and
# Botober 2025: Terrible recipes from a tiny neural net After seeing generated text evolve from the days of tiny neural networks to today's ChatGPT-style large language models, I have to conclude: there's something special about the tiny guys. Maybe it's the way the tiny neural networks string together text letter by letter just based on what you've given it, rather than drawing from prior internet training. It's not secretly drawing on some dark corner of the internet, it's just mashing together statisti