Carlo Vittorio Cannistraci

Cannistraci-Hebb Training on Ultra-Sparse Spiking Neural Networks

Alignment-Enhanced Integration of Connectivity and Spectral Sparsity in Dynamic Sparse Training of LLM

A Generalized Geometric Theoretical Framework of Centroid Discriminant Analysis for Linear Classification of Multi-dimensional Data

Latent Geometry-Driven Network Automata for Complex Network Dismantling

From Transformer to Transponder: Introducing Contextual Modulation Training for Residual Learning in LLMs

A Dendritic-Inspired Network Science Generative Model for Topological Initialization of Connectivity in Sparse Artificial Neural Networks

Network Shape Automata: a brain network inspired collaborative filter for link prediction in bipartite complex networks and recommendation systems

Conversion of sparse Artificial Neural Network to sparse Spiking Neural Network can save up to 99% of energy

Brain network science modelling of sparse neural networks enables Transformers and LLMs to perform as fully connected

Adaptive Cannistraci-Hebb Network Automata Modelling of Complex Networks for Path-based Link Prediction

Sparse Spectral Training and Inference on Euclidean and Hyperbolic Neural Networks

Pivoting Factorization: A Compact Meta Low-Rank Representation of Sparsity for Efficient Inference in Large Language Models

Sparse Spectral Training and Inference on Euclidean and Hyperbolic Neural Networks