--> Learning Rates as a Function of Batch Size: A Random Matrix Theory Approach to Neural Network Training Diego Granziol, Stefan Zohren, Stephen Roberts. Year: 2022, Volume: 23 , Issue: 173, Pages: 1−65 Abstract We study the effect of mini-batching on the loss landscape of deep neural networks using spiked, field-dependent random matrix theory. We demonstrate that the magnitude of the extremal values of the batch Hessian are larger than those of the empirical Hessian. We also derive similar results for
IEI Concepts What Are Imagination Engines? Learn about artificial neural nets that generate ideas. What Are Creativity Machines? Learn about brainstorming artificial neural nets. What Are STANNOs?" Learn Self-Training Artificial Neural Network Objects. What Are Supernets? Learn how artificial neural nets connect into networks of networks. What is DABUS? Learn about a whole new school of artificial neural nets that truly conceive ideas. Artificial Inventors Learn about our efforts to grant machines patents
Abstract page for arXiv paper 2602.17530: Provably Explaining Neural Additive Models
Who Invented Deep Residual Learning? -->. Jürgen Schmidhuber (September 2025) Pronounce: You_again Shmidhoobuh Technical Report IDSIA-09-25, IDSIA AI Blog @SchmidhuberAI arXiv:2509.24732 Who invented deep residual learning? Modern AI is based on deep artificial neural networks (NNs). [DLH] As of 2025, the most cited scientific article of the 21st century is an NN paper on deep residual learning with residual connections. [MOST25,25b] Who invented this? Here is the timeline of the evolution of deep residual
There has been an increasing interest in inferring future links on temporal knowledge graphs (KG). While links on temporal KGs vary continuously over time, the existing approaches model the temporal KGs in discrete state spaces. To this end, we propose a novel continuum model by extending the idea of neural ordinary differential equations (ODEs) to multi-relational graph convolutional networks. The proposed model preserves the continuous nature of dynamic multi-relational graph data and encodes both tempora
Neural computations are currently investigated using two separate approaches: sorting neurons into functional subpopulations or examining the low-dimensional dynamics of collective activity. Whether and how these two aspects interact to shape computations is currently unclear. Using a novel approach to extract computational mechanisms from networks trained on neuroscience tasks, here we show that the dimensionality of the dynamics and subpopulation structure play fundamentally complementary roles. Although
In the previous blog post, we discussed about perceptrons. We learnt how to train a perceptron in Python to achieve a simple classification task. If you need a quick refresher on perceptrons, you can check out that blog post before proceeding further. In a way, perceptron is a single layer neural network with a single
Unlocking the Secrets of Neural Comparison Comparing brains and AI models is a fascinating endeavor, but it's not as simple as measuring likeness. We're on a quest to understand the intricate mechanisms that make two neural systems tick in similar ways. This journey is riddled with challenges, from
Understand Word2Vec's key insight — words appearing in similar contexts have similar meanings — and how CBOW and Skip-gram learn word vectors.
So … why aren’t deep neural nets better at language? They’ve done well at object recognition, a sort-of-analogous process in the visual system, where you synthesize lower-level features into higher