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Alberto Testolin
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2020 – today
- 2024
- [j19]Sina Shafiezadeh
, Gian Marco Duma
Calibrating Deep Learning Classifiers for Patient-Independent Electroencephalogram Seizure Forecasting. Sensors 24(9): 2863 (2024) - [c25]Sina Shafiezadeh, Marco Pozza, Alberto Testolin:
A Comparison of Recurrent and Convolutional Deep Learning Architectures for EEG Seizure Forecasting. BIOSTEC (1) 2024: 583-590 - [c24]Flavio Petruzzellis, Alberto Testolin, Alessandro Sperduti:
Benchmarking GPT-4 on Algorithmic Problems: A Systematic Evaluation of Prompting Strategies. LREC/COLING 2024: 2161-2177 - [c23]Flavio Petruzzellis, Alberto Testolin, Alessandro Sperduti:
A Neural Rewriting System to Solve Algorithmic Problems. ECAI 2024: 3112-3119 - [c22]Flavio Petruzzellis, Alberto Testolin, Alessandro Sperduti:
Assessing the Emergent Symbolic Reasoning Abilities of Llama Large Language Models. ICANN (5) 2024: 266-276 - [c21]Giovanni Donghi, Luca Pasa, Alberto Testolin, Marco Zorzi, Alessandro Sperduti, Nicolò Navarin:
Relative Local Signal Strength: The Impact of Normalization on the Analysis of Neuroimaging Data with Deep Learning. ICANN (8) 2024: 373-383 - [c20]Simone De Renzis, Dennis Dosso, Alberto Testolin:
Exploiting Large Language Models to Train Automatic Detectors of Sensitive Data. IRCDL 2024: 25-33 - [i19]Alberto Testolin, Kuinan Hou, Marco Zorzi:
Large-scale Generative AI Models Lack Visual Number Sense. CoRR abs/2402.03328 (2024) - [i18]Flavio Petruzzellis, Alberto Testolin, Alessandro Sperduti:
Benchmarking GPT-4 on Algorithmic Problems: A Systematic Evaluation of Prompting Strategies. CoRR abs/2402.17396 (2024) - [i17]Flavio Petruzzellis, Alberto Testolin, Alessandro Sperduti:
A Neural Rewriting System to Solve Algorithmic Problems. CoRR abs/2402.17407 (2024) - [i16]Flavio Petruzzellis, Alberto Testolin, Alessandro Sperduti:
Assessing the Emergent Symbolic Reasoning Abilities of Llama Large Language Models. CoRR abs/2406.06588 (2024) - [i15]Kuinan Hou, Marco Zorzi, Alberto Testolin:
Estimating the distribution of numerosity and non-numerical visual magnitudes in natural scenes using computer vision. CoRR abs/2409.11028 (2024) - 2023
- [j18]Hadar Shalev, Liav Nagar, Avi Abu, Alberto Testolin, Roee Diamant
AUV navigation using cues in the sand ripples. Auton. Robots 47(1): 95-107 (2023) - [j17]Matteo Zambra, Alberto Testolin
A Developmental Approach for Training Deep Belief Networks. Cogn. Comput. 15(1): 103-120 (2023) - [j16]Burla Nur Korkmaz, Roee Diamant, Gil Danino, Alberto Testolin:
Automated detection of dolphin whistles with convolutional networks and transfer learning. Frontiers Artif. Intell. 6 (2023) - [j15]Stefano Chioccarello
FORTE: Few Samples for Recognizing Hand Gestures with a Smartphone-attached Radar. Proc. ACM Hum. Comput. Interact. 7(EICS): 1-25 (2023) - [c19]Silvia Poletti, Alberto Testolin, Sebastian Tschiatschek:
Learning Constraints From Human Stop-Feedback in Reinforcement Learning. AAMAS 2023: 2328-2330 - [c18]Lorenzo Tausani
Investigating the Generative Dynamics of Energy-Based Neural Networks. BI 2023: 96-108 - [c17]Flavio Petruzzellis, Alberto Testolin, Alessandro Sperduti:
A Hybrid System for Systematic Generalization in Simple Arithmetic Problems. NeSy 2023: 289-301 - [c16]Flavio Petruzzellis, Ling Xuan Chen, Alberto Testolin:
Learning to solve arithmetic problems with a virtual abacus. NLDL 2023 - [i14]Flavio Petruzzellis, Ling Xuan Chen, Alberto Testolin:
Learning to solve arithmetic problems with a virtual abacus. CoRR abs/2301.06870 (2023) - [i13]Alberto Testolin:
Can neural networks do arithmetic? A survey on the elementary numerical skills of state-of-the-art deep learning models. CoRR abs/2303.07735 (2023) - [i12]Lorenzo Tausani, Alberto Testolin, Marco Zorzi:
Investigating the generative dynamics of energy-based neural networks. CoRR abs/2305.06745 (2023) - [i11]Flavio Petruzzellis, Alberto Testolin, Alessandro Sperduti:
A Hybrid System for Systematic Generalization in Simple Arithmetic Problems. CoRR abs/2306.17249 (2023) - 2022
- [j14]Alberto Testolin
Detecting Submerged Objects Using Active Acoustics and Deep Neural Networks: A Test Case for Pelagic Fish. IEEE Trans. Mob. Comput. 21(8): 2776-2788 (2022) - [c15]Delfina Irarte, Alberto Testolin
Prediction of Neuropsychological Scores from Functional Connectivity Matrices Using Deep Autoencoders. BI 2022: 140-151 - [c14]Samuel Cognolato, Alberto Testolin:
Transformers discover an elementary calculation system exploiting local attention and grid-like problem representation. IJCNN 2022: 1-8 - [c13]Silvester Sabathiel, Trygve Solstad, Alberto Testolin
Self-Communicating Deep Reinforcement Learning Agents Develop External Number Representations. NLDL 2022 - [i10]Samuel Cognolato, Alberto Testolin:
Transformers discover an elementary calculation system exploiting local attention and grid-like problem representation. CoRR abs/2207.02536 (2022) - [i9]Matteo Zambra, Alberto Testolin, Michele De Filippo De Grazia, Marco Zorzi:
A developmental approach for training deep belief networks. CoRR abs/2207.05473 (2022) - [i8]Burla Nur Korkmaz, Roee Diamant, Gil Danino, Alberto Testolin:
Automated Detection of Dolphin Whistles with Convolutional Networks and Transfer Learning. CoRR abs/2211.15406 (2022) - 2021
- [j13]Federico Calesella
A comparison of feature extraction methods for prediction of neuropsychological scores from functional connectivity data of stroke patients. Brain Informatics 8(1): 8 (2021) - [j12]Tommaso Boccato, Alberto Testolin
Learning Numerosity Representations with Transformers: Number Generation Tasks and Out-of-Distribution Generalization. Entropy 23(7): 857 (2021) - [j11]Federico Venturini
Distributed Reinforcement Learning for Flexible and Efficient UAV Swarm Control. IEEE Trans. Cogn. Commun. Netw. 7(3): 955-969 (2021) - [c12]Marco Zorzi
Assessment of Machine Learning Pipelines for Prediction of Behavioral Deficits from Brain Disconnectomes. BI 2021: 211-222 - [i7]Federico Venturini, Federico Mason, Francesco Pase, Federico Chiariotti, Alberto Testolin, Andrea Zanella, Michele Zorzi:
Distributed Reinforcement Learning for Flexible and Efficient UAV Swarm Control. CoRR abs/2103.04666 (2021) - 2020
- [j10]Matteo Zambra, Amos Maritan
Emergence of Network Motifs in Deep Neural Networks. Entropy 22(2): 204 (2020) - [j9]Alberto Testolin
Combining Denoising Autoencoders and Dynamic Programming for Acoustic Detection and Tracking of Underwater Moving Targets. Sensors 20(10): 2945 (2020) - [c11]Federico Calesella
A Systematic Assessment of Feature Extraction Methods for Robust Prediction of Neuropsychological Scores from Functional Connectivity Data. BI 2020: 29-40 - [c10]Federico Venturini, Federico Mason
Distributed reinforcement learning for flexible UAV swarm control with transfer learning capabilities. DroNet@MobiSys 2020: 10:1-10:6 - [c9]Alberto Cenzato, Alberto Testolin
Long-Term Prediction of Physical Interactions: A Challenge for Deep Generative Models. LOD (1) 2020: 83-94 - [i6]Mattia Lecci, Paolo Testolina, Mattia Rebato, Alberto Testolin, Michele Zorzi:
Machine Learning-aided Design of Thinned Antenna Arrays for Optimized Network Level Performance. CoRR abs/2001.09335 (2020)
2010 – 2019
- 2019
- [c8]Alberto Testolin
Underwater Acoustic Detection and Localization with a Convolutional Denoising Autoencoder. CAMSAP 2019: 281-285 - [c7]Paolo Testolina
Enabling Simulation-Based Optimization through Machine Learning: A Case Study on Antenna Design. GLOBECOM 2019: 1-6 - [c6]Andrea Zanetti
Numerosity Representation in InfoGAN: An Empirical Study. IWANN (2) 2019: 49-60 - [i5]Alberto Testolin, Serena Dolfi, Mathijs Rochus, Marco Zorzi:
Perception of visual numerosity in humans and machines. CoRR abs/1907.06996 (2019) - [i4]Alberto Cenzato, Alberto Testolin, Marco Zorzi:
On the difficulty of learning and predicting the long-term dynamics of bouncing objects. CoRR abs/1907.13494 (2019) - [i3]Paolo Testolina, Mattia Lecci, Mattia Rebato, Alberto Testolin, Jonathan Gambini, Roberto Flamini, Christian Mazzucco, Michele Zorzi:
Enabling Simulation-Based Optimization Through Machine Learning: A Case Study on Antenna Design. CoRR abs/1908.11225 (2019) - [i2]Matteo Zambra, Alberto Testolin, Amos Maritan:
Emergence of Network Motifs in Deep Neural Networks. CoRR abs/1912.12244 (2019) - 2018
- [j8]Michele De Filippo De Grazia
QoE Multi-Stage Machine Learning for Dynamic Video Streaming. IEEE Trans. Cogn. Commun. Netw. 4(1): 146-161 (2018) - [i1]Alberto Testolin, Michele Piccolini, Samir Suweis:
Deep learning systems as complex networks. CoRR abs/1809.10941 (2018) - 2017
- [j7]Zahra Sadeghi
Learning representation hierarchies by sharing visual features: a computational investigation of Persian character recognition with unsupervised deep learning. Cogn. Process. 18(3): 273-284 (2017) - [j6]Alberto Testolin
The Role of Architectural and Learning Constraints in Neural Network Models: A Case Study on Visual Space Coding. Frontiers Comput. Neurosci. 11: 13 (2017) - [j5]Roee Diamant
On the Relationship Between the Underwater Acoustic and Optical Channels. IEEE Trans. Wirel. Commun. 16(12): 8037-8051 (2017) - 2016
- [j4]Alberto Testolin
Learning Orthographic Structure With Sequential Generative Neural Networks. Cogn. Sci. 40(3): 579-606 (2016) - [j3]Alberto Testolin
Probabilistic Models and Generative Neural Networks: Towards an Unified Framework for Modeling Normal and Impaired Neurocognitive Functions. Frontiers Comput. Neurosci. 10: 73 (2016) - [c5]Michele Zorzi, Andrea Zanella, Alberto Testolin
COBANETS: A new paradigm for cognitive communications systems. ICNC 2016: 1-7 - 2015
- [j2]Michele Zorzi, Andrea Zanella
Cognition-Based Networks: A New Perspective on Network Optimization Using Learning and Distributed Intelligence. IEEE Access 3: 1512-1530 (2015) - [j1]Luca Pasa, Alberto Testolin
Neural Networks for Sequential Data: a Pre-training Approach based on Hidden Markov Models. Neurocomputing 169: 323-333 (2015) - 2014
- [c4]Luca Pasa, Alberto Testolin, Alessandro Sperduti:
A HMM-based pre-training approach for sequential data. ESANN 2014 - [c3]Alberto Testolin, Marco Zanforlin, Michele De Filippo De Grazia, Daniele Munaretto, Andrea Zanella, Marco Zorzi, Michele Zorzi:
A machine learning approach to QoE-based video admission control and resource allocation in wireless systems. Med-Hoc-Net 2014: 31-38 - [c2]Leonardo Badia
Cognition-based networks: Applying cognitive science to multimedia wireless networking. WoWMoM 2014: 1-6 - 2012
- [c1]Alberto Testolin, Alessandro Sperduti, Ivilin Peev Stoianov, Marco Zorzi:
Assessment of sequential Boltmann machines on a lexical processing task. ESANN 2012
Coauthor Index
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last updated on 2024-10-28 20:14 CET by the dblp team
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