74 citations · 81 across the 9 of their papers we have counts for
9 papers
Unifying Self-Supervised Clustering and Energy-Based Models
Emanuele Sansone, Robin Manhaeve
Self-supervised learning excels at learning representations from large amounts of data. At the same time, generative models offer the complementary property of learning information…
Learning Symbolic Representations Through Joint GEnerative and DIscriminative Training
Emanuele Sansone, Robin Manhaeve
We introduce GEDI, a Bayesian framework that combines existing self-supervised learning objectives with likelihood-based generative models. This framework leverages the benefits of…
GEDI: GEnerative and DIscriminative Training for Self-Supervised Learning
Emanuele Sansone, Robin Manhaeve
Self-supervised learning is a popular and powerful method for utilizing large amounts of unlabeled data, for which a wide variety of training objectives have been proposed in the l…
VAEL: Bridging Variational Autoencoders and Probabilistic Logic Programming
Eleonora Misino, Giuseppe Marra, Emanuele Sansone
We present VAEL, a neuro-symbolic generative model integrating variational autoencoders (VAE) with the reasoning capabilities of probabilistic logic (L) programming. Besides standa…
LSB: Local Self-Balancing MCMC in Discrete Spaces
Emanuele Sansone
We present the Local Self-Balancing sampler (LSB), a local Markov Chain Monte Carlo (MCMC) method for sampling in purely discrete domains, which is able to autonomously adapt to th…
Leveraging Hidden Structure in Self-Supervised Learning
Emanuele Sansone
This work considers the problem of learning structured representations from raw images using self-supervised learning. We propose a principled framework based on a mutual informati…
Training Feedforward Neural Networks with Standard Logistic Activations is Feasible
Emanuele Sansone, Francesco G. B. De Natale
Training feedforward neural networks with standard logistic activations is considered difficult because of the intrinsic properties of these sigmoidal functions. This work aims at…
Efficient Training for Positive Unlabeled Learning
Emanuele Sansone, Francesco G. B. De Natale, Zhi-Hua Zhou
Positive unlabeled (PU) learning is useful in various practical situations, where there is a need to learn a classifier for a class of interest from an unlabeled data set, which ma…
Automatic Synchronization of Multi-User Photo Galleries
E. Sansone, K. Apostolidis, N. Conci +3
In this paper we address the issue of photo galleries synchronization, where pictures related to the same event are collected by different users. Existing solutions to address the…