2 citations · 3 across the 8 of their papers we have counts for
8 papers
Language Diffusion Models are Associative Memories Capable of Retrieving Unseen Data
Bao Pham, Mohammed J. Zaki, Luca Ambrogioni +2
When do language diffusion models memorize their training data, and how to quantitatively assess their true generative regime? We address these questions by showing that Uniform-ba…
Benchmarking Graph Neural Networks in Solving Hard Constraint Satisfaction Problems
Geri Skenderi, Lorenzo Buffoni, Francesco D'Amico +6
Graph neural networks (GNNs) are increasingly applied to hard optimization problems, often claiming superiority over classical heuristics. However, such claims risk being unsolid d…
Pseudo-likelihood produces associative memories able to generalize, even for asymmetric couplings
Francesco D'Amico, Dario Bocchi, Luca Maria Del Bono +2
Energy-based probabilistic models learned by maximizing the likelihood of the data are limited by the intractability of the partition function. A widely used workaround is to maxim…
Statistical mechanics of vector Hopfield network near and above saturation
Flavio Nicoletti, Francesco D'Amico, Matteo Negri
We study analytically and numerically a Hopfield fully-connected network with -dimensional vector spins. These networks are models of associative memory that generalize the stan…
Memorization to Generalization: Emergence of Diffusion Models from Associative Memory
Bao Pham, Gabriel Raya, Matteo Negri +3
Dense Associative Memories (DenseAMs) are generalizations of Hopfield networks, which have superior information storage capacity and can store training data points (memories) at lo…
Implicit bias produces neural scaling laws in learning curves, from perceptrons to deep networks
Francesco D'Amico, Dario Bocchi, Matteo Negri
Scaling laws in deep learning -- empirical power-law relationships linking model performance to resource growth -- have emerged as simple yet striking regularities across architect…
Self-attention as an attractor network: transient memories without backpropagation
Francesco D'Amico, Matteo Negri
Transformers are one of the most successful architectures of modern neural networks. At their core there is the so-called attention mechanism, which recently interested the physics…
Random Features Hopfield Networks generalize retrieval to previously unseen examples
Silvio Kalaj, Clarissa Lauditi, Gabriele Perugini +3
It has been recently shown that a learning transition happens when a Hopfield Network stores examples generated as superpositions of random features, where new attractors correspon…