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
David L. Poole & Alan K. Mackworth Artificial Intelligence 3E foundations of computational agents 8.5 Neural Models for Sequences Fully-connected networks, perhaps including convolutional layers, can handle fixed-size images and sequences. It is also useful to consider sequential data consisting of variable-length sequences. Sequences arise in natural language processing, biology, and any domain involving time, such as the controllers of Chapter 2 . Here, natural language is used as the canonical example of
Skip to content Search this site Open Source Platforms Infrastructure Systems Physical Infrastructure Video Engineering & AR/VR Artificial Intelligence Watch Videos POSTED ON MAY 9, 2017 TO AI Research , ML Applications A novel approach to neural machine translation Language translation is important to Facebook’s mission of making the world more open and connected, enabling everyone to consume posts or videos in their preferred language — all at the highest possible accuracy and speed. Today, the
Author summary Electrophysiological recordings from cortical circuits reveal strongly irregular and highly complex temporal patterns of in-vivo neural activity. In the last decades, a large number of theoretical studies have speculated on the possible sources of fluctuations in neural assemblies, pointing out the possibility of self-sustained irregularity, intrinsically generated by network mechanisms. In particular, a seminal study showed that purely deterministic, but randomly connected rate networks intr
Deep Learning Course Lecture 01: Introduction to Neural Networks Lecture 02: Training Neural Networks Lecture 03: Architecture Lecture 04: Tuning Lecture 05: CNN Section 1: Convolutional Neural Networks (CNN) & Transfer Learning Lecture 06: NLP and Representation Learning Section 1: Representation Learning and Text Representations Lecture 07: Recurrent Neural Networks Section 1: Recurrent Neural Networks Lecture 08: Attention and Transformers Section 1: Attention Section 1: Attention Section 1 Questions Mot
Tuning hyperparameters for machine learning models like neural networks can be tedious and time-consuming. Traditional grid search or random search lacks efficiency in high-dimensional or non-linear search spaces. Genetic Algorithms (GAs) offer a compelling alternative by navigating the hyperparameter space with adaptive and evolutionary pressure. In this post, we’ll walk through using a Genetic Algorithm
A paper introducing a unified framework combining model size, training time, and data volume to predict neural network performance and establishing the concept of scale-time equivalence
Artificial neural network is a very important part in the new industry of artificial intelligence. In China, there are many researches on artificial neural