10 citations · 35 across the 19 of their papers we have counts for
12 papers · 1 filter
Jumping up and down: Denoiser diffusion models for discrete ordinal data
Yair Shenfeld, Ricardo Baptista, Stefano Peluchetti
Diffusion models are highly developed in continuous spaces for image and video domains. Recently, major advances have been made for discrete diffusion models for categorical data,…
Bridging Spherical Black-Box Optimizers
Johannes Ackermann, Stefano Peluchetti
When gradient information is unavailable, black-box optimization (BBO) methods provide a practical alternative. While Evolution Strategies (ES), Consensus-Based Optimization (CBO),…
Binomial flows: Denoising and flow matching for discrete ordinal data
Yair Shenfeld, Ricardo Baptista, Stefano Peluchetti
Flow-based generative modeling in continuous spaces exploit Tweedie's formula to express the denoiser (learned in training) as a score function (used in sampling). In contrast, thi…
Sparser, Faster, Lighter Transformer Language Models
Edoardo Cetin, Stefano Peluchetti, Emilio Castillo +3
Scaling autoregressive large language models (LLMs) has driven unprecedented progress but comes with vast computational costs. In this work, we tackle these costs by leveraging uns…
Latent-Augmented Discrete Diffusion Models
Dario Shariatian, Alain Durmus, Umut Simsekli +1
Discrete diffusion models have emerged as a powerful class of models and a promising route to fast language generation, but practical implementations typically rely on factored rev…
BM: Coupled Schrödinger Bridge Matching
Stefano Peluchetti
A Schrödinger bridge establishes a dynamic transport map between two target distributions via a reference process, simultaneously solving an associated entropic optimal transport p…
Function-Space MCMC for Bayesian Wide Neural Networks
Lucia Pezzetti, Stefano Favaro, Stefano Peluchetti
Bayesian Neural Networks represent a fascinating confluence of deep learning and probabilistic reasoning, offering a compelling framework for understanding uncertainty in complex p…
Non-Denoising Forward-Time Diffusions
Stefano Peluchetti
The scope of this paper is generative modeling through diffusion processes. An approach falling within this paradigm is the work of Song et al. (2021), which relies on a time-rever…
Large-width asymptotics for ReLU neural networks with -Stable initializations
Stefano Favaro, Sandra Fortini, Stefano Peluchetti
There is a recent and growing literature on large-width asymptotic properties of Gaussian neural networks (NNs), namely NNs whose weights are initialized as Gaussian distributions.…
Deep Stable neural networks: large-width asymptotics and convergence rates
Stefano Favaro, Sandra Fortini, Stefano Peluchetti
In modern deep learning, there is a recent and growing literature on the interplay between large-width asymptotic properties of deep Gaussian neural networks (NNs), i.e. deep NNs w…
Learning Stochastic Optimal Policies via Gradient Descent
Stefano Massaroli, Michael Poli, Stefano Peluchetti +3
We systematically develop a learning-based treatment of stochastic optimal control (SOC), relying on direct optimization of parametric control policies. We propose a derivation of…
An empirical study of pretrained representations for few-shot classification
Tiago Ramalho, Thierry Sousbie, Stefano Peluchetti
Recent algorithms with state-of-the-art few-shot classification results start their procedure by computing data features output by a large pretrained model. In this paper we system…