Search Results for author: Vincenzo Lomonaco

Found 50 papers, 28 papers with code

Calibration of Continual Learning Models

1 code implementation11 Apr 2024 Lanpei Li, Elia Piccoli, Andrea Cossu, Davide Bacciu, Vincenzo Lomonaco

Continual Learning (CL) focuses on maximizing the predictive performance of a model across a non-stationary stream of data.

Continual Learning

Continual Policy Distillation of Reinforcement Learning-based Controllers for Soft Robotic In-Hand Manipulation

1 code implementation5 Apr 2024 Lanpei Li, Enrico Donato, Vincenzo Lomonaco, Egidio Falotico

The framework leverages Policy Distillation (PD) to transfer knowledge from expert policies to a continually evolving student policy network.

Reinforcement Learning (RL)

Adaptive Hyperparameter Optimization for Continual Learning Scenarios

no code implementations9 Mar 2024 Rudy Semola, Julio Hurtado, Vincenzo Lomonaco, Davide Bacciu

This paper aims to explore the role of hyperparameter selection in continual learning and the necessity of continually and automatically tuning them according to the complexity of the task at hand.

Continual Learning Hyperparameter Optimization

Design Principles for Lifelong Learning AI Accelerators

no code implementations5 Oct 2023 Dhireesha Kudithipudi, Anurag Daram, Abdullah M. Zyarah, Fatima Tuz Zohora, James B. Aimone, Angel Yanguas-Gil, Nicholas Soures, Emre Neftci, Matthew Mattina, Vincenzo Lomonaco, Clare D. Thiem, Benjamin Epstein

Lifelong learning - an agent's ability to learn throughout its lifetime - is a hallmark of biological learning systems and a central challenge for artificial intelligence (AI).

In-context Interference in Chat-based Large Language Models

no code implementations22 Sep 2023 Eric Nuertey Coleman, Julio Hurtado, Vincenzo Lomonaco

However, one limitation of this scenario is that users cannot modify the internal knowledge of the model, and the only way to add or modify internal knowledge is by explicitly mentioning it to the model during the current interaction.

In-Context Learning

A Comprehensive Empirical Evaluation on Online Continual Learning

2 code implementations20 Aug 2023 Albin Soutif--Cormerais, Antonio Carta, Andrea Cossu, Julio Hurtado, Hamed Hemati, Vincenzo Lomonaco, Joost Van de Weijer

Online continual learning aims to get closer to a live learning experience by learning directly on a stream of data with temporally shifting distribution and by storing a minimum amount of data from that stream.

Class Incremental Learning Image Classification

LuckyMera: a Modular AI Framework for Building Hybrid NetHack Agents

1 code implementation17 Jul 2023 Luigi Quarantiello, Simone Marzeddu, Antonio Guzzi, Vincenzo Lomonaco

In the last few decades we have witnessed a significant development in Artificial Intelligence (AI) thanks to the availability of a variety of testbeds, mostly based on simulated environments and video games.


Partial Hypernetworks for Continual Learning

1 code implementation19 Jun 2023 Hamed Hemati, Vincenzo Lomonaco, Davide Bacciu, Damian Borth

Inspired by latent replay methods in CL, we propose partial weight generation for the final layers of a model using hypernetworks while freezing the initial layers.

Continual Learning

Studying Generalization on Memory-Based Methods in Continual Learning

no code implementations16 Jun 2023 Felipe del Rio, Julio Hurtado, Cristian Buc, Alvaro Soto, Vincenzo Lomonaco

One of the objectives of Continual Learning is to learn new concepts continually over a stream of experiences and at the same time avoid catastrophic forgetting.

Continual Learning Out-of-Distribution Generalization

Projected Latent Distillation for Data-Agnostic Consolidation in Distributed Continual Learning

1 code implementation28 Mar 2023 Antonio Carta, Andrea Cossu, Vincenzo Lomonaco, Davide Bacciu, Joost Van de Weijer

We formalize this problem as a Distributed Continual Learning scenario, where SCD adapt to local tasks and a CL model consolidates the knowledge from the resulting stream of models without looking at the SCD's private data.

Continual Learning Knowledge Distillation

Avalanche: A PyTorch Library for Deep Continual Learning

1 code implementation2 Feb 2023 Antonio Carta, Lorenzo Pellegrini, Andrea Cossu, Hamed Hemati, Vincenzo Lomonaco

Continual learning is the problem of learning from a nonstationary stream of data, a fundamental issue for sustainable and efficient training of deep neural networks over time.

Class Incremental Learning

Continual Learning for Predictive Maintenance: Overview and Challenges

no code implementations29 Jan 2023 Julio Hurtado, Dario Salvati, Rudy Semola, Mattia Bosio, Vincenzo Lomonaco

In this work, we present a brief introduction to predictive maintenance, non-stationary environments, and continual learning, together with an extensive review of the current state of applying continual learning in real-world applications and specifically in predictive maintenance.

Continual Learning

Class-Incremental Learning with Repetition

1 code implementation26 Jan 2023 Hamed Hemati, Andrea Cossu, Antonio Carta, Julio Hurtado, Lorenzo Pellegrini, Davide Bacciu, Vincenzo Lomonaco, Damian Borth

We propose two stochastic stream generators that produce a wide range of CIR streams starting from a single dataset and a few interpretable control parameters.

Class Incremental Learning Incremental Learning

Architect, Regularize and Replay (ARR): a Flexible Hybrid Approach for Continual Learning

no code implementations6 Jan 2023 Vincenzo Lomonaco, Lorenzo Pellegrini, Gabriele Graffieti, Davide Maltoni

In recent years we have witnessed a renewed interest in machine learning methodologies, especially for deep representation learning, that could overcome basic i. i. d.

Class Incremental Learning Incremental Learning +1

Memory Population in Continual Learning via Outlier Elimination

1 code implementation4 Jul 2022 Julio Hurtado, Alain Raymond-Saez, Vladimir Araujo, Vincenzo Lomonaco, Alvaro Soto, Davide Bacciu

This paper introduces Memory Outlier Elimination (MOE), a method for identifying and eliminating outliers in the memory buffer by choosing samples from label-homogeneous subpopulations.

Continual Learning

Continual Learning for Human State Monitoring

1 code implementation29 Jun 2022 Federico Matteoni, Andrea Cossu, Claudio Gallicchio, Vincenzo Lomonaco, Davide Bacciu

Continual Learning (CL) on time series data represents a promising but under-studied avenue for real-world applications.

Continual Learning Time Series +1

Continual-Learning-as-a-Service (CLaaS): On-Demand Efficient Adaptation of Predictive Models

no code implementations14 Jun 2022 Rudy Semola, Vincenzo Lomonaco, Davide Bacciu

The two main future trends for companies that want to build machine learning-based applications and systems are real-time inference and continual updating.

Attribute BIG-bench Machine Learning +2

Continual Pre-Training Mitigates Forgetting in Language and Vision

1 code implementation19 May 2022 Andrea Cossu, Tinne Tuytelaars, Antonio Carta, Lucia Passaro, Vincenzo Lomonaco, Davide Bacciu

We formalize and investigate the characteristics of the continual pre-training scenario in both language and vision environments, where a model is continually pre-trained on a stream of incoming data and only later fine-tuned to different downstream tasks.

Continual Learning Continual Pretraining

Generative Negative Replay for Continual Learning

no code implementations12 Apr 2022 Gabriele Graffieti, Davide Maltoni, Lorenzo Pellegrini, Vincenzo Lomonaco

Learning continually is a key aspect of intelligence and a necessary ability to solve many real-life problems.

Continual Learning

Practical Recommendations for Replay-based Continual Learning Methods

no code implementations19 Mar 2022 Gabriele Merlin, Vincenzo Lomonaco, Andrea Cossu, Antonio Carta, Davide Bacciu

Continual Learning requires the model to learn from a stream of dynamic, non-stationary data without forgetting previous knowledge.

Continual Learning Data Augmentation

Avalanche RL: a Continual Reinforcement Learning Library

1 code implementation28 Feb 2022 Nicolò Lucchesi, Antonio Carta, Vincenzo Lomonaco, Davide Bacciu

Continual Reinforcement Learning (CRL) is a challenging setting where an agent learns to interact with an environment that is constantly changing over time (the stream of experiences).

Continual Learning OpenAI Gym +2

AI-as-a-Service Toolkit for Human-Centered Intelligence in Autonomous Driving

no code implementations3 Feb 2022 Valerio De Caro, Saira Bano, Achilles Machumilane, Alberto Gotta, Pietro Cassará, Antonio Carta, Rudy Semola, Christos Sardianos, Christos Chronis, Iraklis Varlamis, Konstantinos Tserpes, Vincenzo Lomonaco, Claudio Gallicchio, Davide Bacciu

This paper presents a proof-of-concept implementation of the AI-as-a-Service toolkit developed within the H2020 TEACHING project and designed to implement an autonomous driving personalization system according to the output of an automatic driver's stress recognition algorithm, both of them realizing a Cyber-Physical System of Systems.

Autonomous Driving reinforcement-learning +1

Ex-Model: Continual Learning from a Stream of Trained Models

1 code implementation13 Dec 2021 Antonio Carta, Andrea Cossu, Vincenzo Lomonaco, Davide Bacciu

Learning continually from non-stationary data streams is a challenging research topic of growing popularity in the last few years.

Continual Learning

Sustainable Artificial Intelligence through Continual Learning

no code implementations17 Nov 2021 Andrea Cossu, Marta Ziosi, Vincenzo Lomonaco

The increasing attention on Artificial Intelligence (AI) regulation has led to the definition of a set of ethical principles grouped into the Sustainable AI framework.

Continual Learning

International Workshop on Continual Semi-Supervised Learning: Introduction, Benchmarks and Baselines

no code implementations27 Oct 2021 Ajmal Shahbaz, Salman Khan, Mohammad Asiful Hossain, Vincenzo Lomonaco, Kevin Cannons, Zhan Xu, Fabio Cuzzolin

The aim of this paper is to formalize a new continual semi-supervised learning (CSSL) paradigm, proposed to the attention of the machine learning community via the IJCAI 2021 International Workshop on Continual Semi-Supervised Learning (CSSL-IJCAI), with the aim of raising field awareness about this problem and mobilizing its effort in this direction.

Activity Recognition Crowd Counting

Continual Learning with Echo State Networks

1 code implementation17 May 2021 Andrea Cossu, Davide Bacciu, Antonio Carta, Claudio Gallicchio, Vincenzo Lomonaco

Continual Learning (CL) refers to a learning setup where data is non stationary and the model has to learn without forgetting existing knowledge.

Continual Learning

Distilled Replay: Overcoming Forgetting through Synthetic Samples

2 code implementations29 Mar 2021 Andrea Rosasco, Antonio Carta, Andrea Cossu, Vincenzo Lomonaco, Davide Bacciu

Replay strategies are Continual Learning techniques which mitigate catastrophic forgetting by keeping a buffer of patterns from previous experiences, which are interleaved with new data during training.

Continual Learning

Continual Learning for Recurrent Neural Networks: an Empirical Evaluation

no code implementations12 Mar 2021 Andrea Cossu, Antonio Carta, Vincenzo Lomonaco, Davide Bacciu

We propose two new benchmarks for CL with sequential data based on existing datasets, whose characteristics resemble real-world applications.

Continual Learning

CVPR 2020 Continual Learning in Computer Vision Competition: Approaches, Results, Current Challenges and Future Directions

1 code implementation14 Sep 2020 Vincenzo Lomonaco, Lorenzo Pellegrini, Pau Rodriguez, Massimo Caccia, Qi She, Yu Chen, Quentin Jodelet, Ruiping Wang, Zheda Mai, David Vazquez, German I. Parisi, Nikhil Churamani, Marc Pickett, Issam Laradji, Davide Maltoni

In the last few years, we have witnessed a renewed and fast-growing interest in continual learning with deep neural networks with the shared objective of making current AI systems more adaptive, efficient and autonomous.

Benchmarking Continual Learning

Online Continual Learning on Sequences

no code implementations20 Mar 2020 German I. Parisi, Vincenzo Lomonaco

Online continual learning (OCL) refers to the ability of a system to learn over time from a continuous stream of data without having to revisit previously encountered training samples.

Continual Learning Incremental Learning

Latent Replay for Real-Time Continual Learning

3 code implementations2 Dec 2019 Lorenzo Pellegrini, Gabriele Graffieti, Vincenzo Lomonaco, Davide Maltoni

Continual learning techniques, where complex models are incrementally trained on small batches of new data, can make the learning problem tractable even for CPU-only embedded devices enabling remarkable levels of adaptiveness and autonomy.

Continual Learning valid

OpenLORIS-Object: A Robotic Vision Dataset and Benchmark for Lifelong Deep Learning

2 code implementations15 Nov 2019 Qi She, Fan Feng, Xinyue Hao, Qihan Yang, Chuanlin Lan, Vincenzo Lomonaco, Xuesong Shi, Zhengwei Wang, Yao Guo, Yimin Zhang, Fei Qiao, Rosa H. M. Chan

Yet, robotic vision poses unique challenges for applying visual algorithms developed from these standard computer vision datasets due to their implicit assumption over non-varying distributions for a fixed set of tasks.

Object Object Recognition

Efficient Continual Learning in Neural Networks with Embedding Regularization

1 code implementation9 Sep 2019 Jary Pomponi, Simone Scardapane, Vincenzo Lomonaco, Aurelio Uncini

Continual learning of deep neural networks is a key requirement for scaling them up to more complex applicative scenarios and for achieving real lifelong learning of these architectures.

Continual Learning

Rehearsal-Free Continual Learning over Small Non-I.I.D. Batches

5 code implementations8 Jul 2019 Vincenzo Lomonaco, Davide Maltoni, Lorenzo Pellegrini

Ideally, continual learning should be triggered by the availability of short videos of single objects and performed on-line on on-board hardware with fine-grained updates.

Class Incremental Learning Object Recognition

Continual Learning for Robotics: Definition, Framework, Learning Strategies, Opportunities and Challenges

no code implementations29 Jun 2019 Timothée Lesort, Vincenzo Lomonaco, Andrei Stoian, Davide Maltoni, David Filliat, Natalia Díaz-Rodríguez

An important challenge for machine learning is not necessarily finding solutions that work in the real world but rather finding stable algorithms that can learn in real world.

BIG-bench Machine Learning Continual Learning

Continual Reinforcement Learning in 3D Non-stationary Environments

1 code implementation24 May 2019 Vincenzo Lomonaco, Karan Desai, Eugenio Culurciello, Davide Maltoni

High-dimensional always-changing environments constitute a hard challenge for current reinforcement learning techniques.

reinforcement-learning Reinforcement Learning (RL)

Intelligent Drone Swarm for Search and Rescue Operations at Sea

no code implementations13 Nov 2018 Vincenzo Lomonaco, Angelo Trotta, Marta Ziosi, Juan de Dios Yáñez Ávila, Natalia Díaz-Rodríguez

In recent years, a rising numbers of people arrived in the European Union, traveling across the Mediterranean Sea or overland through Southeast Europe in what has been later named as the European migrant crisis.

Don't forget, there is more than forgetting: new metrics for Continual Learning

no code implementations31 Oct 2018 Natalia Díaz-Rodríguez, Vincenzo Lomonaco, David Filliat, Davide Maltoni

Continual learning consists of algorithms that learn from a stream of data/tasks continuously and adaptively thought time, enabling the incremental development of ever more complex knowledge and skills.

Attribute Computational Efficiency +2

Custom Dual Transportation Mode Detection by Smartphone Devices Exploiting Sensor Diversity

1 code implementation12 Oct 2018 Claudia Carpineti, Vincenzo Lomonaco, Luca Bedogni, Marco Di Felice, Luciano Bononi

Making applications aware of the mobility experienced by the user can open the door to a wide range of novel services in different use-cases, from smart parking to vehicular traffic monitoring.

Activity Recognition Benchmarking +1

Continuous Learning in Single-Incremental-Task Scenarios

1 code implementation22 Jun 2018 Davide Maltoni, Vincenzo Lomonaco

It was recently shown that architectural, regularization and rehearsal strategies can be used to train deep models sequentially on a number of disjoint tasks without forgetting previously acquired knowledge.

Class Incremental Learning Incremental Learning

CORe50: a New Dataset and Benchmark for Continuous Object Recognition

1 code implementation9 May 2017 Vincenzo Lomonaco, Davide Maltoni

Continuous/Lifelong learning of high-dimensional data streams is a challenging research problem.

Continuous Object Recognition Object

Semi-supervised Tuning from Temporal Coherence

1 code implementation10 Nov 2015 Davide Maltoni, Vincenzo Lomonaco

Recent works demonstrated the usefulness of temporal coherence to regularize supervised training or to learn invariant features with deep architectures.

General Classification

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