Sapienza University of Rome

∙ Vico Nel Lazio , Italy ∙ 0 followers

51583 The Sapienza University of Rome, also called simply Sapienza or the University of Rome, is a collegiate research university that is located in Rome, Italy.

Why should we add early exits to neural networks?

Deep neural networks are generally designed as a stack of differentiable...
04/27/2020 ∙ by Simone Scardapane, et al. ∙ 39 ∙ share

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Out-domain examples for generative models

Deep generative models are being increasingly used in a wide variety of ...
03/07/2019 ∙ by Dario Pasquini, et al. ∙ 18 ∙ share

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Knowledge-Driven Learning via Experts Consult for Thyroid Nodule Classification

Computer-aided diagnosis (CAD) is becoming a prominent approach to assis...
05/28/2020 ∙ by Danilo Avola, et al. ∙ 13 ∙ share

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Anticipation and next action forecasting in video: an end-to-end model with memory

Action anticipation and forecasting in videos do not require a hat-trick...
01/11/2019 ∙ by Fiora Pirri, et al. ∙ 12 ∙ share

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LTLf Synthesis with Fairness and Stability Assumptions

In synthesis, assumptions are constraints on the environment that rule o...
12/17/2019 ∙ by Shufang Zhu, et al. ∙ 10 ∙ share

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Curse of Small Sample Size in Forecasting of the Active Cases in COVID-19 Outbreak

During the COVID-19 pandemic, a massive number of attempts on the predic...
11/06/2020 ∙ by Mert Nakıp, et al. ∙ 10 ∙ share

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Towards Recognizing Unseen Categories in Unseen Domains

Current deep visual recognition systems suffer from severe performance d...
07/23/2020 ∙ by Massimiliano Mancini, et al. ∙ 9 ∙ share

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Biological Random Walks: integrating heterogeneous data in disease gene prioritization

This work proposes a unified framework to leverage biological informatio...
02/14/2020 ∙ by Michele Gentili, et al. ∙ 7 ∙ share

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A High-Order Scheme for Image Segmentation via a modified Level-Set method

The method is based on an adaptive "filtered" scheme recently introduced...
12/07/2018 ∙ by Maurizio Falcone, et al. ∙ 4 ∙ share

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Kafnets: kernel-based non-parametric activation functions for neural networks

Neural networks are generally built by interleaving (adaptable) linear l...
07/13/2017 ∙ by Simone Scardapane, et al. ∙ 0 ∙ share

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Vision-based deep execution monitoring

Execution monitor of high-level robot actions can be effectively improve...
09/29/2017 ∙ by Francesco Puja, et al. ∙ 0 ∙ share

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What Automated Planning can do for Business Process Management

Business Process Management (BPM) is a central element of today organiza...
09/29/2017 ∙ by Andrea Marrella, et al. ∙ 0 ∙ share

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Computing the Shapley Value in Allocation Problems: Approximations and Bounds, with an Application to the Italian VQR Research Assessment Program

In allocation problems, a given set of goods are assigned to agents in s...
09/13/2017 ∙ by Francesco Lupia, et al. ∙ 0 ∙ share

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Temporal Overdrive Recurrent Neural Network

In this work we present a novel recurrent neural network architecture de...
01/18/2017 ∙ by Filippo Maria Bianchi, et al. ∙ 0 ∙ share

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Supervised Learning with Indefinite Topological Kernels

Topological Data Analysis (TDA) is a recent and growing branch of statis...
09/20/2017 ∙ by Tullia Padellini, et al. ∙ 0 ∙ share

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Persistence Flamelets: multiscale Persistent Homology for kernel density exploration

In recent years there has been noticeable interest in the study of the "...
09/20/2017 ∙ by Tullia Padellini, et al. ∙ 0 ∙ share

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Specifying Non-Markovian Rewards in MDPs Using LDL on Finite Traces (Preliminary Version)

In Markov Decision Processes (MDPs), the reward obtained in a state depe...
06/25/2017 ∙ by Ronen Brafman, et al. ∙ 0 ∙ share

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Learning activation functions from data using cubic spline interpolation

Neural networks require a careful design in order to perform properly on...
05/18/2016 ∙ by Simone Scardapane, et al. ∙ 0 ∙ share

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Preliminary results on Ontology-based Open Data Publishing

Despite the current interest in Open Data publishing, a formal and compr...
05/30/2017 ∙ by Gianluca Cima, et al. ∙ 0 ∙ share

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A Hierarchical Genetic Optimization of a Fuzzy Logic System for Flow Control in Micro Grids

Bio-inspired algorithms like Genetic Algorithms and Fuzzy Inference Syst...
04/16/2016 ∙ by Enrico De Santis, et al. ∙ 0 ∙ share

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Linear Differential Constraints for Photo-polarimetric Height Estimation

In this paper we present a differential approach to photo-polarimetric s...
08/25/2017 ∙ by Silvia Tozza, et al. ∙ 0 ∙ share

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Data-driven detrending of nonstationary fractal time series with echo state networks

In this paper, we propose a novel data-driven approach for removing tren...
10/24/2015 ∙ by Enrico Maiorino, et al. ∙ 0 ∙ share

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Stochastic Training of Neural Networks via Successive Convex Approximations

This paper proposes a new family of algorithms for training neural netwo...
06/15/2017 ∙ by Simone Scardapane, et al. ∙ 0 ∙ share

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Recursive Multikernel Filters Exploiting Nonlinear Temporal Structure

In kernel methods, temporal information on the data is commonly included...
06/12/2017 ∙ by Steven Van Vaerenbergh, et al. ∙ 0 ∙ share

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On the impact of topological properties of smart grids in power losses optimization problems

Power losses reduction is one of the main targets for any electrical ene...
01/19/2015 ∙ by Francesca Possemato, et al. ∙ 0 ∙ share

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Adaptation and learning over networks for nonlinear system modeling

In this chapter, we analyze nonlinear filtering problems in distributed ...
04/28/2017 ∙ by Simone Scardapane, et al. ∙ 0 ∙ share

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Canonical dual solutions to nonconvex radial basis neural network optimization problem

Radial Basis Functions Neural Networks (RBFNNs) are tools widely used in...
02/18/2013 ∙ by Vittorio Latorre, et al. ∙ 0 ∙ share

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Improving variational methods via pairwise linear response identities

Inference methods are often formulated as variational approximations: th...
11/02/2016 ∙ by Jack Raymond, et al. ∙ 0 ∙ share

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A Framework for Parallel and Distributed Training of Neural Networks

The aim of this paper is to develop a general framework for training neu...
10/24/2016 ∙ by Simone Scardapane, et al. ∙ 0 ∙ share

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Distributed Adaptive Learning of Graph Signals

The aim of this paper is to propose distributed strategies for adaptive ...
09/20/2016 ∙ by P. Di Lorenzo, et al. ∙ 0 ∙ share

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Bounded Situation Calculus Action Theories

In this paper, we investigate bounded action theories in the situation c...
09/07/2015 ∙ by Giuseppe De Giacomo, et al. ∙ 0 ∙ share

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Data Filtering for Cluster Analysis by ℓ_0-Norm Regularization

A data filtering method for cluster analysis is proposed, based on minim...
07/29/2016 ∙ by Andrea Cristofari, et al. ∙ 0 ∙ share

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Group Sparse Regularization for Deep Neural Networks

In this paper, we consider the joint task of simultaneously optimizing (...
07/02/2016 ∙ by Simone Scardapane, et al. ∙ 0 ∙ share

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Automatic Model Based Dataset Generation for Fast and Accurate Crop and Weeds Detection

Selective weeding is one of the key challenges in the field of agricultu...
12/09/2016 ∙ by Maurilio Di Cicco, et al. ∙ 0 ∙ share

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Classifying sequences by the optimized dissimilarity space embedding approach: a case study on the solubility analysis of the E. coli proteome

We evaluate a version of the recently-proposed classification system nam...
08/17/2014 ∙ by Lorenzo Livi, et al. ∙ 0 ∙ share

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Online Open World Recognition

As we enter into the big data age and an avalanche of images have become...
04/08/2016 ∙ by Rocco De Rosa, et al. ∙ 0 ∙ share

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Performance of a community detection algorithm based on semidefinite programming

The problem of detecting communities in a graph is maybe one the most st...
03/30/2016 ∙ by Adel Javanmard, et al. ∙ 0 ∙ share

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Modeling and Recognition of Smart Grid Faults by a Combined Approach of Dissimilarity Learning and One-Class Classification

Detecting faults in electrical power grids is of paramount importance, e...
07/25/2014 ∙ by Enrico De Santis, et al. ∙ 0 ∙ share

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Solving Visual Madlibs with Multiple Cues

This paper presents an approach for answering fill-in-the-blank multiple...
11/01/2016 ∙ by Tatiana Tommasi, et al. ∙ 0 ∙ share

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A deep representation for depth images from synthetic data

Convolutional Neural Networks (CNNs) trained on large scale RGB database...
09/30/2016 ∙ by Fabio Maria Carlucci, et al. ∙ 0 ∙ share

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Action-based Character AI in Video-games with CogBots Architecture: A Preliminary Report

In this paper we propose an architecture for specifying the interaction ...
07/11/2013 ∙ by Davide Aversa, et al. ∙ 0 ∙ share

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Quasi Conjunction, Quasi Disjunction, T-norms and T-conorms: Probabilistic Aspects

We make a probabilistic analysis related to some inference rules which p...
03/20/2013 ∙ by Angelo Gilio, et al. ∙ 0 ∙ share

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Electre Tri-Machine Learning Approach to the Record Linkage Problem

In this short paper, the Electre Tri-Machine Learning Method, generally ...
05/25/2015 ∙ by Renato De Leone, et al. ∙ 0 ∙ share

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Verification of Agent-Based Artifact Systems

Artifact systems are a novel paradigm for specifying and implementing bu...
01/12/2013 ∙ by Francesco Belardinelli, et al. ∙ 0 ∙ share

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Probabilistic entailment in the setting of coherence: The role of quasi conjunction and inclusion relation

In this paper, by adopting a coherence-based probabilistic approach to d...
01/06/2013 ∙ by Angelo Gilio, et al. ∙ 0 ∙ share

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Confidence driven TGV fusion

We introduce a novel model for spatially varying variational data fusion...
03/30/2016 ∙ by Valsamis Ntouskos, et al. ∙ 0 ∙ share

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Active Detection and Localization of Textureless Objects in Cluttered Environments

This paper introduces an active object detection and localization framew...
03/22/2016 ∙ by Marco Imperoli, et al. ∙ 0 ∙ share

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On the Complexity of Finding Second-Best Abductive Explanations

While looking for abductive explanations of a given set of manifestation...
04/26/2012 ∙ by Paolo Liberatore, et al. ∙ 0 ∙ share

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When Naïve Bayes Nearest Neighbours Meet Convolutional Neural Networks

Since Convolutional Neural Networks (CNNs) have become the leading learn...
11/12/2015 ∙ by Ilja Kuzborskij, et al. ∙ 0 ∙ share

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Hacking Smart Machines with Smarter Ones: How to Extract Meaningful Data from Machine Learning Classifiers

Machine Learning (ML) algorithms are used to train computers to perform ...
06/19/2013 ∙ by Giuseppe Ateniese, et al. ∙ 0 ∙ share

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Sapienza University of Rome

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