Skip to content
View AndreaCossu's full-sized avatar

Highlights

  • Pro

Block or report AndreaCossu

Block user

Prevent this user from interacting with your repositories and sending you notifications. Learn more about blocking users.

You must be logged in to block users.

Please don't include any personal information such as legal names or email addresses. Maximum 100 characters, markdown supported. This note will be visible to only you.
Report abuse

Contact GitHub support about this user’s behavior. Learn more about reporting abuse.

Report abuse
AndreaCossu/README.md

Hi there 👋

I am an assistant professor (RTD-A in Italy) at University of Pisa.
My research focuses on Continual Learning, with applications to Recurrent Neural Networks models and sequential data processing.

Pinned Loading

  1. ContinualAI/avalanche ContinualAI/avalanche Public

    Avalanche: an End-to-End Library for Continual Learning based on PyTorch.

    Python 1.8k 291

  2. ContinualLearning-SequentialProcessing ContinualLearning-SequentialProcessing Public

    Continual Learning with Gated Incremental Memories for Sequential Data Processing. IJCNN 2020. Continual Learning with Recurrent Neural Networks (RNNs) inspired by Progressive network architecture.

    Python 15 4

  3. Pervasive-AI-Lab/ContinualLearning-EchoStateNetworks Pervasive-AI-Lab/ContinualLearning-EchoStateNetworks Public

    Continual Learning with Echo State Networks experiments

    Python 9 3

  4. Relation-Network-PyTorch Relation-Network-PyTorch Public

    Implementation of Relation Network and Recurrent Relational Network using PyTorch v1.3. Original papers: (RN) https://arxiv.org/abs/1706.01427 (RRN): https://arxiv.org/abs/1711.08028

    Python 19 7

  5. ContinualAI/continual-learning-papers ContinualAI/continual-learning-papers Public

    Continual Learning papers list, curated by ContinualAI

    HTML 580 53

  6. ContinualAI/continual-learning-baselines ContinualAI/continual-learning-baselines Public

    Continual learning baselines and strategies from popular papers, using Avalanche. We include EWC, SI, GEM, AGEM, LwF, iCarl, GDumb, and other strategies.

    Python 261 38