
Why should we add early exits to neural networks?
Deep neural networks are generally designed as a stack of differentiable...
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Outdomain examples for generative models
Deep generative models are being increasingly used in a wide variety of ...
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KnowledgeDriven Learning via Experts Consult for Thyroid Nodule Classification
Computeraided diagnosis (CAD) is becoming a prominent approach to assis...
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Anticipation and next action forecasting in video: an endtoend model with memory
Action anticipation and forecasting in videos do not require a hattrick...
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LTLf Synthesis with Fairness and Stability Assumptions
In synthesis, assumptions are constraints on the environment that rule o...
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Curse of Small Sample Size in Forecasting of the Active Cases in COVID19 Outbreak
During the COVID19 pandemic, a massive number of attempts on the predic...
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Towards Recognizing Unseen Categories in Unseen Domains
Current deep visual recognition systems suffer from severe performance d...
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Biological Random Walks: integrating heterogeneous data in disease gene prioritization
This work proposes a unified framework to leverage biological informatio...
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A HighOrder Scheme for Image Segmentation via a modified LevelSet method
The method is based on an adaptive "filtered" scheme recently introduced...
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Kafnets: kernelbased nonparametric activation functions for neural networks
Neural networks are generally built by interleaving (adaptable) linear l...
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Visionbased deep execution monitoring
Execution monitor of highlevel robot actions can be effectively improve...
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What Automated Planning can do for Business Process Management
Business Process Management (BPM) is a central element of today organiza...
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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...
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Temporal Overdrive Recurrent Neural Network
In this work we present a novel recurrent neural network architecture de...
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Supervised Learning with Indefinite Topological Kernels
Topological Data Analysis (TDA) is a recent and growing branch of statis...
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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 "...
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Specifying NonMarkovian Rewards in MDPs Using LDL on Finite Traces (Preliminary Version)
In Markov Decision Processes (MDPs), the reward obtained in a state depe...
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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...
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Preliminary results on Ontologybased Open Data Publishing
Despite the current interest in Open Data publishing, a formal and compr...
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A Hierarchical Genetic Optimization of a Fuzzy Logic System for Flow Control in Micro Grids
Bioinspired algorithms like Genetic Algorithms and Fuzzy Inference Syst...
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Linear Differential Constraints for Photopolarimetric Height Estimation
In this paper we present a differential approach to photopolarimetric s...
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Datadriven detrending of nonstationary fractal time series with echo state networks
In this paper, we propose a novel datadriven approach for removing tren...
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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...
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Recursive Multikernel Filters Exploiting Nonlinear Temporal Structure
In kernel methods, temporal information on the data is commonly included...
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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...
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Adaptation and learning over networks for nonlinear system modeling
In this chapter, we analyze nonlinear filtering problems in distributed ...
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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...
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Improving variational methods via pairwise linear response identities
Inference methods are often formulated as variational approximations: th...
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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...
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Distributed Adaptive Learning of Graph Signals
The aim of this paper is to propose distributed strategies for adaptive ...
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Bounded Situation Calculus Action Theories
In this paper, we investigate bounded action theories in the situation c...
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Data Filtering for Cluster Analysis by ℓ_0Norm Regularization
A data filtering method for cluster analysis is proposed, based on minim...
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Group Sparse Regularization for Deep Neural Networks
In this paper, we consider the joint task of simultaneously optimizing (...
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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...
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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 recentlyproposed classification system nam...
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Online Open World Recognition
As we enter into the big data age and an avalanche of images have become...
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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...
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Modeling and Recognition of Smart Grid Faults by a Combined Approach of Dissimilarity Learning and OneClass Classification
Detecting faults in electrical power grids is of paramount importance, e...
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Solving Visual Madlibs with Multiple Cues
This paper presents an approach for answering fillintheblank multiple...
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A deep representation for depth images from synthetic data
Convolutional Neural Networks (CNNs) trained on large scale RGB database...
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Actionbased Character AI in Videogames with CogBots Architecture: A Preliminary Report
In this paper we propose an architecture for specifying the interaction ...
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Quasi Conjunction, Quasi Disjunction, Tnorms and Tconorms: Probabilistic Aspects
We make a probabilistic analysis related to some inference rules which p...
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Electre TriMachine Learning Approach to the Record Linkage Problem
In this short paper, the Electre TriMachine Learning Method, generally ...
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Verification of AgentBased Artifact Systems
Artifact systems are a novel paradigm for specifying and implementing bu...
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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 coherencebased probabilistic approach to d...
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Confidence driven TGV fusion
We introduce a novel model for spatially varying variational data fusion...
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Active Detection and Localization of Textureless Objects in Cluttered Environments
This paper introduces an active object detection and localization framew...
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On the Complexity of Finding SecondBest Abductive Explanations
While looking for abductive explanations of a given set of manifestation...
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When Naïve Bayes Nearest Neighbours Meet Convolutional Neural Networks
Since Convolutional Neural Networks (CNNs) have become the leading learn...
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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 ...
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Sapienza University of Rome
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.