Abstract page for arXiv paper 2110.07641: Non-deep Networks
This paper investigates Bayesian neural network posteriors using full-batch HMC to reveal insights on performance, robustness, and model limitations
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QuantDare Daring to quantify the markets | The scientific blog of ETS Asset Management Factory Unlock the Power of Quantitative Strategies: Explore Our Cutting-Edge Website Today! All Generative Adversarial Networks: A rivalry that strengthens Miguel Ángel Hoyo Abascal 22/03/2023 How does ChatGPT work? What is behind deep fake images of celebrities? How do we deal with the lack of data in finance? All these issues have in common the same underlying concept; they are based on generative models. Generative
Lech Nowak's personal website showcasing AI, ML, and cloud projects.
Ph. D. Thesis 6. Results � Multivariate Calibrations 6.9. PCA-NN ## 6.9. PCA-NN The combination of a principal component analysis with neural networks is a fast and efficient way of compressing the information fed to the neural networks. Yet, the decision how many principal components to use for the neural networks remains a problem as this determines the extent of compression and the extent of information loss similar to the PLS (see section 2.5 ). Thus, neural networks with 6 hidden neurons in 1 hidden
Rigorous treatment of Kolmogorov-Arnold Networks: the 1957 representation theorem, the spline-on-edge architecture, the approximation bound, and what KANs do and do not win at compared to MLPs
Can neural networks systematically capture discrete, compositional task structure despite their continuous, distributed nature? The impressive capabilities of large scale neural networks suggest that the answer to this question is yes. Howe
ᛜ Light of Baldr ᛊᚨᛉᚲᛜᚱ # Research We explore the structure of machine cognition. Understanding how neural networks think, reason, and represent knowledge. ## Research Areas ### Mechanistic Interpretability Understanding neural networks by reverse-engineering their internal computations. Finding the circuits that implement specific behaviors. ### Sparse Autoencoders Training networks to decompose neural activations into interpretable features. Making the latent space legible. ### Model