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08/20/2023
A Comprehensive Empirical Evaluation on Online Continual Learning
Online continual learning aims to get closer to a live learning experien...
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06/29/2023
Improving Online Continual Learning Performance and Stability with Temporal Ensembles
Neural networks are very effective when trained on large datasets for a ...
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03/28/2023
Projected Latent Distillation for Data-Agnostic Consolidation in Distributed Continual Learning
Distributed learning on the edge often comprises self-centered devices (...
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02/02/2023
Avalanche: A PyTorch Library for Deep Continual Learning
Continual learning is the problem of learning from a nonstationary strea...
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01/26/2023
Class-Incremental Learning with Repetition
Real-world data streams naturally include the repetition of previous con...
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12/13/2022
3rd Continual Learning Workshop Challenge on Egocentric Category and Instance Level Object Understanding
Continual Learning, also known as Lifelong or Incremental Learning, has ...
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06/23/2022
Sample Condensation in Online Continual Learning
Online Continual learning is a challenging learning scenario where the m...
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05/19/2022
Continual Pre-Training Mitigates Forgetting in Language and Vision
Pre-trained models are nowadays a fundamental component of machine learn...
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03/19/2022
Practical Recommendations for Replay-based Continual Learning Methods
Continual Learning requires the model to learn from a stream of dynamic,...
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02/28/2022
Avalanche RL: a Continual Reinforcement Learning Library
Continual Reinforcement Learning (CRL) is a challenging setting where an...
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02/03/2022
AI-as-a-Service Toolkit for Human-Centered Intelligence in Autonomous Driving
This paper presents a proof-of-concept implementation of the AI-as-a-Ser...
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12/13/2021
Ex-Model: Continual Learning from a Stream of Trained Models
Learning continually from non-stationary data streams is a challenging r...
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12/06/2021
Is Class-Incremental Enough for Continual Learning?
The ability of a model to learn continually can be empirically assessed ...
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07/14/2021
TEACHING – Trustworthy autonomous cyber-physical applications through human-centred intelligence
This paper discusses the perspective of the H2020 TEACHING project on th...
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05/17/2021
Continual Learning with Echo State Networks
Continual Learning (CL) refers to a learning setup where data is non sta...
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03/29/2021
Distilled Replay: Overcoming Forgetting through Synthetic Samples
Replay strategies are Continual Learning techniques which mitigate catas...
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03/22/2021
Catastrophic Forgetting in Deep Graph Networks: an Introductory Benchmark for Graph Classification
In this work, we study the phenomenon of catastrophic forgetting in the ...
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03/12/2021
Continual Learning for Recurrent Neural Networks: a Review and Empirical Evaluation
Learning continuously during all model lifetime is fundamental to deploy...
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11/05/2020
Short-Term Memory Optimization in Recurrent Neural Networks by Autoencoder-based Initialization
Training RNNs to learn long-term dependencies is difficult due to vanish...
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06/29/2020
Incremental Training of a Recurrent Neural Network Exploiting a Multi-Scale Dynamic Memory
The effectiveness of recurrent neural networks can be largely influenced...
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04/08/2020
Continual Learning with Gated Incremental Memories for sequential data processing
The ability to learn in dynamic, nonstationary environments without forg...
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01/31/2020
Encoding-based Memory Modules for Recurrent Neural Networks
Learning to solve sequential tasks with recurrent models requires the ab...
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01/15/2020
Learning Style-Aware Symbolic Music Representations by Adversarial Autoencoders
We address the challenging open problem of learning an effective latent ...
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01/15/2020
Learning a Latent Space of Style-Aware Symbolic Music Representations by Adversarial Autoencoders
We address the challenging open problem of learning an effective latent ...
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11/08/2018