In the last few years we have witnessed a renewed and steadily growing interest in the AI community towards algorithms that can learn continuously from high-dimensional data. At ContinualAI we work to create a distributed and inclusive research lab on Continual Learning, where anyone can contribute and learn more about this fascinating topic, while producing cutting edge research results.
One of the main goal of ContinualAI is indeed to help ContinualAI members disseminate and improve their work: every paper published by the ContinualAI members will be listed in this page, shared and openly discussed on the slack platform.
Research Papers
In this section we report all the papers from the ContinualAI members in chronological order. Join us on slack if you want to discuss with the authors about them!
Lomonaco, V., Maltoni, D., Pellegrini, L. (2019) Fine-Grained Continual Learning. arXiv:1907.03799.
Maltoni, D., Lomonaco, V. (2019) Continuous Learning in Single-Incremental-Task Scenarios. Neural Networks, 116:56-73.
Parisi, G.I., Kemker, R., Part, J.L., Kanan, C., Wermter, S. (2019) Continual Lifelong Learning with Neural Networks: A Review. Neural Networks 113:54-71 [arXiv:1802.07569]
Parisi, G.I., Tani, J., Weber, C., Wermter, S. (2018) Lifelong Learning of Spatiotemporal Representations with Dual-Memory Recurrent Self-Organization. Frontiers in Neurorobotics, 12:78 [arXiv:1805.10966]
Díaz-Rodríguez, N., Lomonaco, V., Filliat, D., Maltoni, D. (2018) Don’t forget, there is more than forgetting: new metrics for Continual Learning. Continual Learning Workshop at NeurIPS 2018, Montreal, Canada.
Parisi, G.I., Ji, X., Wermter, S. (2018) On the role of neurogenesis in overcoming catastrophic forgetting. Continual Learning Workshop at NeurIPS 2018., Montreal, Canada [arXiv:1811.02113]
Lomonaco, V., Maltoni, D. (2017) CORe50: a new Dataset and Benchmark for Continuous Object Recognition. Conference on Robot Learning (CoRL), pp. 17-26
Parisi, G.I., Tani, J., Weber, C., Wermter, S. (2017) Lifelong Learning of Human Actions with Deep Neural Network Self-Organization. Neural Networks 96:137-149.