193 citations · 502 across the 17 of their papers we have counts for
6 papers · 1 filter
Brain network science modelling of sparse neural networks enables Transformers and LLMs to perform as fully connected
Yingtao Zhang, Diego Cerretti, Jialin Zhao +4
Dynamic sparse training (DST) can reduce the computational demands in ANNs, but faces difficulties in keeping peak performance at high sparsity levels. The Cannistraci-Hebb trainin…
Pivoting Factorization: A Compact Meta Low-Rank Representation of Sparsity for Efficient Inference in Large Language Models
Jialin Zhao, Yingtao Zhang, Carlo Vittorio Cannistraci
The rapid growth of Large Language Models has driven demand for effective model compression techniques to reduce memory and computation costs. Low-rank pruning has gained attention…
Sparse Spectral Training and Inference on Euclidean and Hyperbolic Neural Networks
Jialin Zhao, Yingtao Zhang, Xinghang Li +2
The growing demands on GPU memory posed by the increasing number of neural network parameters call for training approaches that are more memory-efficient. Previous memory reduction…
Measuring group-separability in geometrical space for evaluation of pattern recognition and embedding algorithms
A. Acevedo, S. Ciucci, MJ. Kuo +2
Evaluating data separation in a geometrical space is fundamental for pattern recognition. A plethora of dimensionality reduction (DR) algorithms have been developed in order to rev…
Angular separability of data clusters or network communities in geometrical space and its relevance to hyperbolic embedding
Alessandro Muscoloni, Carlo Vittorio Cannistraci
Analysis of 'big data' characterized by high-dimensionality such as word vectors and complex networks requires often their representation in a geometrical space by embedding. Recen…
Latent Geometry Inspired Graph Dissimilarities Enhance Affinity Propagation Community Detection in Complex Networks
Carlo Vittorio Cannistraci, Alessandro Muscoloni
Affinity propagation is one of the most effective unsupervised pattern recognition algorithms for data clustering in high-dimensional feature space. However, the numerous attempts…