[ContinualAI Seminars]: "Ex-Model: Continual Learning from a Stream of Trained Models"
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Keiland Cooper
Apr 27, 2022, 11:49:29 AM4/27/22
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Hi All,
This Thursday 28-04-2022 for the ContinualAI Seminar, Antonio Carta (University of Pisa) will present the paper:
Title: “Ex-Model: Continual Learning from a Stream of Trained Models”
Abstract: Learning continually from non-stationary data streams is a challenging research topic of growing popularity in the last few years. Being able to learn, adapt, and generalize continually in an efficient, effective, and scalable way is fundamental for a sustainable development of Artificial Intelligent systems. However, an agent-centric view of continual learning requires learning directly from raw data, which limits the interaction between independent agents, the efficiency, and the privacy of current approaches. Instead, we argue that continual learning systems should exploit the availability of compressed information in the form of trained models. In this paper, we introduce and formalize a new paradigm named 'Ex-Model Continual Learning' (ExML), where an agent learns from a sequence of previously trained models instead of raw data. We further contribute with three ex-model continual learning algorithms and an empirical setting comprising three datasets (MNIST, CIFAR-10 and CORe50), and eight scenarios, where the proposed algorithms are extensively tested. Finally, we highlight the peculiarities of the ex-model paradigm and we point out interesting future research directions.
See you there at:
UTC - 15:30 PM
CEST - 17:30 PM
PST - 8:30 AM
---------------
- YouTube link: [click here]
- Microsoft Teams: [ click here]
- YouTube recordings of the previous sessions: https://www.youtube.com/c/ContinualAI
---------------
Feel free to share this email to anyone interested and invite them to subscribe this mailing-list here: https://groups.google.com/g/continualai
Please also contact me if you want to speak at one of the next sessions!
Looking forward to seeing you all there!
All the best,
Keiland Cooper
University of California
ContinualAI Co-founder
http://kwcooper.xyz
This Thursday 28-04-2022 for the ContinualAI Seminar, Antonio Carta (University of Pisa) will present the paper:
Title: “Ex-Model: Continual Learning from a Stream of Trained Models”
Abstract: Learning continually from non-stationary data streams is a challenging research topic of growing popularity in the last few years. Being able to learn, adapt, and generalize continually in an efficient, effective, and scalable way is fundamental for a sustainable development of Artificial Intelligent systems. However, an agent-centric view of continual learning requires learning directly from raw data, which limits the interaction between independent agents, the efficiency, and the privacy of current approaches. Instead, we argue that continual learning systems should exploit the availability of compressed information in the form of trained models. In this paper, we introduce and formalize a new paradigm named 'Ex-Model Continual Learning' (ExML), where an agent learns from a sequence of previously trained models instead of raw data. We further contribute with three ex-model continual learning algorithms and an empirical setting comprising three datasets (MNIST, CIFAR-10 and CORe50), and eight scenarios, where the proposed algorithms are extensively tested. Finally, we highlight the peculiarities of the ex-model paradigm and we point out interesting future research directions.
See you there at:
UTC - 15:30 PM
CEST - 17:30 PM
PST - 8:30 AM
---------------
- YouTube link: [click here]
- Microsoft Teams: [ click here]
- YouTube recordings of the previous sessions: https://www.youtube.com/c/ContinualAI
---------------
Feel free to share this email to anyone interested and invite them to subscribe this mailing-list here: https://groups.google.com/g/continualai
Please also contact me if you want to speak at one of the next sessions!
Looking forward to seeing you all there!
All the best,
Keiland Cooper
University of California
ContinualAI Co-founder
http://kwcooper.xyz
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