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Tommaso Salvatori

16 accepted papers

2026

Prototype Transformer: Towards Language Model Architectures Interpretable by Design

ICML 2026poster

While state-of-the-art language models (LMs) surpass the vast majority of humans in certain domains, their reasoning remains largely opaque, reducing trust and risking deception and hallucination. In this work, we introduce the Prototype Transformer (ProtoT)—an autoregressive LM architecture that re…

Cited by 0SourceScholar
2026

Towards the Training of Deeper Predictive Coding Neural Networks

ICML 2026poster

Predictive coding networks are neural models that perform inference through an iterative energy minimization process. While effective in shallow architectures, they suffer significant performance degradation beyond five to seven layers. In this work, we show that this degradation is caused by expone…

Cited by 5SourceScholar
2025

Benchmarking Predictive Coding Networks -- Made Simple

ICLR 2025spotlight

In this work, we tackle the problems of efficiency and scalability for predictive coding networks (PCNs) in machine learning. To do so, we propose a library that focuses on performance and simplicity, and use it to implement a large set of standard benchmarks for the community to use for their expe…

2024

A Stable, Fast, and Fully Automatic Learning Algorithm for Predictive Coding Networks

ICLR 2024poster

Predictive coding networks are neuroscience-inspired models with roots in both Bayesian statistics and neuroscience. Training such models, however, is quite inefficient and unstable. In this work, we show how by simply changing the temporal scheduling of the update rule for the synaptic weights lead…

Cited by 10SourcePDFScholar
2024

Divide-and-Conquer Predictive Coding: a structured Bayesian inference algorithm

NeurIPS 2024poster

Unexpected stimuli induce "error" or "surprise" signals in the brain. The theory of predictive coding promises to explain these observations in terms of Bayesian inference by suggesting that the cortex implements variational inference in a probabilistic graphical model. However, when applied to mach…

Cited by 1SourcePDFScholar
2024

Predictive Coding beyond Correlations

ICML 2024poster

Biologically plausible learning algorithms offer a promising alternative to traditional deep learning techniques, especially in overcoming the limitations of backpropagation in fast and low-energy neuromorphic implementations. To this end, there has been extensive research in understanding what thei…

Cited by 3SourcePDFScholar
2023

A Theoretical Framework for Inference and Learning in Predictive Coding Networks

ICLR 2023poster

Predictive coding (PC) is an influential theory in computational neuroscience, which argues that the cortex forms unsupervised world models by implementing a hierarchical process of prediction error minimization. PC networks (PCNs) are trained in two phases. First, neural activities are updated to o…

2023

Backpropagation at the Infinitesimal Inference Limit of Energy-Based Models: Unifying Predictive Coding, Equilibrium Propagation, and Contrastive Hebbian Learning

ICLR 2023poster

How the brain performs credit assignment is a fundamental unsolved problem in neuroscience. Many `biologically plausible' algorithms have been proposed, which compute gradients that approximate those computed by backpropagation (BP), and which operate in ways that more closely satisfy the constraint…

2023

Mathematical Capabilities of ChatGPT

NeurIPS 2023poster

We investigate the mathematical capabilities of two versions of ChatGPT (released 9-January-2023 and 30-January-2023) and of GPT-4 by testing them on publicly available datasets, as well as hand-crafted ones, using a novel evaluation scheme. In contrast to formal mathematics, where large databases o…

Cited by 660SourcePDFScholar
2022

Learning on Arbitrary Graph Topologies via Predictive Coding

NeurIPS 2022accept

Training with backpropagation (BP) in standard deep learning consists of two main steps: a forward pass that maps a data point to its prediction, and a backward pass that propagates the error of this prediction back through the network. This process is highly effective when the goal is to minimize a…

Cited by 39SourcePDFScholar
2022

Predictive Coding beyond Gaussian Distributions

NeurIPS 2022accept

A large amount of recent research has the far-reaching goal of finding training methods for deep neural networks that can serve as alternatives to backpropagation~(BP). A prominent example is predictive coding (PC), which is a neuroscience-inspired method that performs inference on hierarchical Gaus…

Cited by 13SourcePDFScholar
2022

Predictive Coding: Towards a Future of Deep Learning beyond Backpropagation?

IJCAI 2022poster

The backpropagation of error algorithm (BP) used to train deep neural networks has been fundamental to the successes of deep learning. However, it requires sequential backwards updates and non-local computations which make it challenging to parallelize at scale and is unlike how learning works in th…

Cited by 58SourcePDFScholar
2022

Reverse Differentiation via Predictive Coding

AAAI 2022technical

Deep learning has redefined AI thanks to the rise of artificial neural networks, which are inspired by neurological networks in the brain. Through the years, this dualism between AI and neuroscience has brought immense benefits to both fields, allowing neural networks to be used in a plethora of app…

Cited by 43SourcePDFScholar
2022

Universal Hopfield Networks: A General Framework for Single-Shot Associative Memory Models

ICML 2022spotlight

A large number of neural network models of associative memory have been proposed in the literature. These include the classical Hopfield networks (HNs), sparse distributed memories (SDMs), and more recently the modern continuous Hopfield networks (MCHNs), which possess close links with self-attentio…

2021

Associative Memories via Predictive Coding

NeurIPS 2021poster

Associative memories in the brain receive and store patterns of activity registered by the sensory neurons, and are able to retrieve them when necessary. Due to their importance in human intelligence, computational models of associative memories have been developed for several decades now. In this p…

Cited by 84SourcePDFScholar
2020

BoxE: A Box Embedding Model for Knowledge Base Completion

NeurIPS 2020spotlight

Knowledge base completion (KBC) aims to automatically infer missing facts by exploiting information already present in a knowledge base (KB). A promising approach for KBC is to embed knowledge into latent spaces and make predictions from learned embeddings. However, existing embedding models are su…