Paper session 1 (11.30 - 12.30, 19 Oct 2023 UTC)

Proposed talks

• In-context interference in Chat-based Large Language Models
  Eric Nuertey Coleman
• Addressing the challenges of learning in dynamic environments
  Federico Giannini
• Alleviating Catastrophic Forgetting through Direct Feedback Alignment in Neural Networks
  Sara Folchini

Pre-registration papers

• Adaptive Hyperparameter Optimization for Continual Learning Scenarios
  Rudy Semola
• AdaCL: Adaptive Continual Learning
  Elif Ceren Gok Yildirim

Paper session 2 (15.00 - 16.00, 19 Oct 2023 UTC)

Proposed talks

• Computationally Efficient Continual Learning for Real-World Applications
  Christopher Kanan
• Lifelong Learning for Evolving Graphs
  Lukas Galke
• Continual Learning from Demonstration
  Sayantan Auddy, Jakob Hollenstein, Matteo Saveriano, Antonio Rodriguez-sanchez, Justus Piater

Pre-registration papers

• Implicit Neural Representation as vectorizer for classification task applied to diverse data structures
  Thibault Malherbe

Paper session 3 (18.00 - 19.00, 19 Oct 2023 UTC)

Proposed talks

• Graphical Neural Activity Threads as an abstraction for spiking neural computation
  Bradley Theilman
• Dendrites as a biologically inspired approach to overcome catastrophic forgetting
  Jeremy Forest

Pre-registration papers

• CD-IMM: The Benefits of Domain-based Mixture Models in Bayesian Continual Learning
  Antonio Carta and Daniele Castellana

Paper session 4 (23.30 - 00.30, 19/20 Oct 2023 UTC)

Proposed talks

• An Analysis of Forgetting in Regularization-Based Continual Learning
  Haoran Li
• Memory Management Strategies in Replay-Based Continual Learning beyond Reservoir Sampling
  Andrii Krutsylo
• Developing Strategies for Continual Object Detection with the MMDetection Toolbox
  Angelo Garangau Menezes

Paper session 5 (02.15 - 03.15, 20 Oct 2023 UTC)

Proposed talks

• Strategies for using pre-trained models as a practical solution for continual learning
  Mark McDonnell

Paper session 6 (07.45 - 08.45, 20 Oct 2023 UTC)

• Analyzing Continual Learning from a Perspective of Sequential Projections
  Itay Evron, Gon Buzaglo, Edward Moroshko, Nathan Srebro, Daniel Soudry

Pre-registration papers

• Examining Changes in Internal Representations of Continual Learning Models through Tensor Decomposition
  Nishant Suresh Aswani
• Two Complementary Perspectives to Continual Learning: Ask Not Only What to Optimize, But Also How
  Timm Hess