Fabio Peruzzo

After a PhD in applied mathematics with a focus on complex systems, Fabio felt that, for his taste, the academic environment was not focused enough on solving the urgent problems of our society. Fascinated by the possibilities offered by the new machine learning revolution, he began his journey into AI engineering. He has worked for a few startups in the UK, and since 2021 he is part of appliedAI.

Fabio's work

Beyond neural scaling laws: beating power law scaling via data pruning

Large neural networks are very “data hungry”, but data pruning holds the promise to alleviate this dependence. A recent paper …

What Neural Networks Memorize and Why: Discovering the Long Tail via Influence Estimation

When data samples are difficult to learn, neural networks tend to memorize their labels rather than infer useful features. This has been …

Planning with Diffusion for Flexible Behavior Synthesis

Diffusion models are finding their way into more machine learning domains. This paper introduces a framework for applying them to …

Prompt-to-Prompt Image Editing with Cross Attention Control

Generating images from text is complicated by the fact that small changes in prompt gives very different results. This new study shows how …

pyDVL: the python Data Valuation Library

pyDVL strives to offer reference implementations of algorithms for data valuation with a common interface compatible with sklearn and a …

Neural Fields in Visual Computing and Beyond

A great entry-level review to coordinate-based neural networks (a.k.a. neural fields), a technique that has recently shown widespread …

Safe and efficient deep reinforcement learning

This 2-day dive into deep RL for decision and control is suitable for engineers who want to solve real-world control problems with efficient …

Methods and issues in explainable AI

A two-day workshop for ML practitioners wishing to make their models better understandable for themselves and decision makers.

Brax - A Differentiable Physics Engine for Large Scale Rigid Body Simulation

The development of versatile robots demands a better understanding of physics simulators. A recent line of research focuses on engines whose …

Simulation in Robotics Opportunities Challenges Suggestions

Despite great advancements in physics based simulators for robotics over the last few years, a big sim2real gap remains in the performance …

Denoising Diffusion and Score Based Generative Models

This pill gives a short overview of the development of diffusion models over the last 7 years. It contains several references useful for …

Progressive Distillation for Fast Sampling of Diffusion Models

The generation process of diffusion models can be sped up by using a slow teacher model for training a faster student - the latter …

Our take on ICLR 2022 - Part 1

ICLR is one of the most important and prestigious conferences in the ML community. It spans all ML topics, from embeddings to Graph neural …

Score-Based Generative Modeling with Critically-Damped Langevin Diffusion

In this rather technical paper, a new dynamics is proposed for the sampling of diffusion models. Instead of sampling the de-noised image …

Energy-Based Learning for Cooperative Games, with Applications to Valuation Problems in Machine Learning

A theoretical framework connecting data valuation scores, game theory and energy based models is presented. Common criteria like Shapley or …

Resolving Training Biases via Influence-based Data Relabeling

Influence functions are used to correct corrupt labels in a dataset that would significantly decrease a trained model’s performance. …