11 citations · 11 across the 1 of their papers we have counts for
4 papers
Bayesian Tensor Factorisation for Bottom-up Hidden Tree Markov Models
Daniele Castellana, Davide Bacciu
Bottom-Up Hidden Tree Markov Model is a highly expressive model for tree-structured data. Unfortunately, it cannot be used in practice due to the intractable size of its state-tran…
Detecting Adversarial Examples through Nonlinear Dimensionality Reduction
Francesco Crecchi, Davide Bacciu, Battista Biggio
Deep neural networks are vulnerable to adversarial examples, i.e., carefully-perturbed inputs aimed to mislead classification. This work proposes a detection method based on combin…
Hidden Tree Markov Networks: Deep and Wide Learning for Structured Data
Davide Bacciu
The paper introduces the Hidden Tree Markov Network (HTN), a neuro-probabilistic hybrid fusing the representation power of generative models for trees with the incremental and disc…
DropIn: Making Reservoir Computing Neural Networks Robust to Missing Inputs by Dropout
Davide Bacciu, Francesco Crecchi, Davide Morelli
The paper presents a novel, principled approach to train recurrent neural networks from the Reservoir Computing family that are robust to missing part of the input features at pred…