Sfoglia per Relatore
A study of spiking neural networks for biologically plausible deep learning
2020/2021 DE MATOLA, MATTEO
Active Learning for ticket prediction
2020/2021 LANZA, ENRICO
Augmenting convolutional neural networks with kernels inspired by the early visual system
2021/2022 ROVOLETTO, MATTEO BRUNO
Classificazione gerarchica di testo in ambito e-commerce
2021/2022 VACCARO, FABIO
Classifying community text and community groups using machine learning
2021/2022 HUANG, ZESEN
Deep learning techniques for biological signal processing: Automatic detection of dolphin sounds
2021/2022 KORKMAZ, BURLA NUR
Deep Reinforcement Learning methods for StarCraft II Learning Environment
2020/2021 Dainese, Nicola
DeTT: Data Efficient Temporal Transformers for Surgical Phase Recognition
2022/2023 KULAZHENKOV, IVAN
Distributed Deep Reinforcement Learning for Drone Swarm Control
2020/2021 Venturini, Federico
Early-Stage Alzheimer's Disease Detection Using an Explainable Deep Learning Method and Functional Magnetic Resonance Imaging (fMRI)
2022/2023 EBRAHIMIAN BABOUKANI, REZA
Evaluation of basic mathematical abilities of neural networks
2022/2023 HOU, KUINAN
Explainability of machine learning models: A systematic review and a case study on BERT
2020/2021 ARCUDI, ALESSIO
Exploiting Large Language Models to Train Automatic Detectors of Sensitive Data
2022/2023 DE RENZIS, SIMONE
Forecasting epileptic seizures from electroencephalograms using deep neural networks
2022/2023 POZZA, MARCO
Generative adversarial network for predictive maintenance of a packaging machine
2021/2022 RASETTA, ADRIANO
Investigating the dynamics of spontaneous activity in energy-based neural networks
2021/2022 TAUSANI, LORENZO
Learning constraints from human stop-feedbacks in Reinforcement Learning
2021/2022 POLETTI, SILVIA
Machine Learning and Deep Learning approaches for XML document classification
2020/2021 GAZZOLA, GIOVANNI
Modelli di Machine Learning per la predizione di attacchi epilettici sulla base del segnale EEG
2021/2022 MORO, NICHOLAS
Multi Agent Reinforcement Learning for Drone Swarm Control with Non-homogeneous Agents
2020/2021 NOBILI, PIETRO MARIA
Tipologia | Anno | Titolo | Titolo inglese | Autore | File |
---|---|---|---|---|---|
Lauree magistrali | 2020 | A study of spiking neural networks for biologically plausible deep learning | A study of spiking neural networks for biologically plausible deep learning | DE MATOLA, MATTEO | |
Lauree magistrali | 2020 | Active Learning for ticket prediction | Active Learning for ticket prediction | LANZA, ENRICO | |
Lauree triennali | 2021 | Augmenting convolutional neural networks with kernels inspired by the early visual system | Augmenting convolutional neural networks with kernels inspired by the early visual system | ROVOLETTO, MATTEO BRUNO | |
Lauree magistrali | 2021 | Classificazione gerarchica di testo in ambito e-commerce | Hierarchical classification of text in e-commerce context | VACCARO, FABIO | |
Lauree magistrali | 2021 | Classifying community text and community groups using machine learning | Classifying community text and community groups using machine learning | HUANG, ZESEN | |
Lauree magistrali | 2021 | Deep learning techniques for biological signal processing: Automatic detection of dolphin sounds | Deep learning techniques for biological signal processing: Automatic detection of dolphin sounds | KORKMAZ, BURLA NUR | |
Lauree magistrali | 2020 | Deep Reinforcement Learning methods for StarCraft II Learning Environment | - | Dainese, Nicola | |
Lauree magistrali | 2022 | DeTT: Data Efficient Temporal Transformers for Surgical Phase Recognition | DeTT: Data Efficient Temporal Transformers for Surgical Phase Recognition | KULAZHENKOV, IVAN | |
Lauree magistrali | 2020 | Distributed Deep Reinforcement Learning for Drone Swarm Control | - | Venturini, Federico | |
Lauree magistrali | 2022 | Early-Stage Alzheimer's Disease Detection Using an Explainable Deep Learning Method and Functional Magnetic Resonance Imaging (fMRI) | Early-Stage Alzheimer's Disease Detection Using an Explainable Deep Learning Method and Functional Magnetic Resonance Imaging (fMRI) | EBRAHIMIAN BABOUKANI, REZA | |
Lauree magistrali | 2022 | Evaluation of basic mathematical abilities of neural networks | Evaluation of basic mathematical abilities of neural networks | HOU, KUINAN | |
Lauree magistrali | 2020 | Explainability of machine learning models: A systematic review and a case study on BERT | Explainability of machine learning models: A systematic review and a case study on BERT | ARCUDI, ALESSIO | |
Lauree magistrali | 2022 | Exploiting Large Language Models to Train Automatic Detectors of Sensitive Data | Exploiting Large Language Models to Train Automatic Detectors of Sensitive Data | DE RENZIS, SIMONE | |
Lauree magistrali | 2022 | Forecasting epileptic seizures from electroencephalograms using deep neural networks | Forecasting epileptic seizures from electroencephalograms using deep neural networks | POZZA, MARCO | |
Lauree magistrali | 2021 | Generative adversarial network for predictive maintenance of a packaging machine | Generative adversarial network for predictive maintenance of a packaging machine | RASETTA, ADRIANO | |
Lauree magistrali | 2021 | Investigating the dynamics of spontaneous activity in energy-based neural networks | Investigating the dynamics of spontaneous activity in energy-based neural networks | TAUSANI, LORENZO | |
Lauree magistrali | 2021 | Learning constraints from human stop-feedbacks in Reinforcement Learning | Learning constraints from human stop-feedbacks in Reinforcement Learning | POLETTI, SILVIA | |
Lauree magistrali | 2020 | Machine Learning and Deep Learning approaches for XML document classification | Machine Learning and Deep Learning approaches for XML document classification | GAZZOLA, GIOVANNI | |
Lauree magistrali | 2021 | Modelli di Machine Learning per la predizione di attacchi epilettici sulla base del segnale EEG | Machine Learning models for epileptic seizures prediction based on EEG signal | MORO, NICHOLAS | |
Lauree magistrali | 2020 | Multi Agent Reinforcement Learning for Drone Swarm Control with Non-homogeneous Agents | Multi Agent Reinforcement Learning for Drone Swarm Control with Non-homogeneous Agents | NOBILI, PIETRO MARIA |
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