A Theory of Initialisation's Impact on Specialisation
Devon Jarvis, Sebastian Lee, Clémentine Carla Juliette Dominé, Andrew M. Saxe, Stefano Sarao Mannelli
2025年份
2顶会引用
摘要
Prior work has demonstrated a consistent tendency in neural networks engaged in continual learning tasks, wherein intermediate task similarity results in the highest levels of catastrophic interference. This phenomenon is attributed to the network's tendency to reuse learned features across tasks.
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引用它的顶会 Paper2
- A Theory of How Pretraining Shapes Inductive Bias in Fine-TuningNicolas Anguita, Francesco Locatello, Andrew Saxe, Marco Mondelli 等ICML 2026 · 被引用 3 次
- Sharp description of local minima in the loss landscape of high-dimensional two-layer ReLU neural networksJie Huang, Bruno Loureiro, Stefano Sarao MannelliICML 2026 · 被引用 1 次
它引用的顶会 Paper6
- Anatomy of Catastrophic Forgetting: Hidden Representations and Task SemanticsVinay Venkatesh Ramasesh, Ethan Dyer, Maithra RaghuICLR 2021 · 被引用 207 次
- Self-Consistent Dynamical Field Theory of Kernel Evolution in Wide Neural NetworksBlake Bordelon, Cengiz PehlevanNeurIPS 2022 · 被引用 140 次
- Neural Networks as Kernel Learners: The Silent Alignment EffectAlexander B. Atanasov, Blake Bordelon, Cengiz PehlevanICLR 2022 · 被引用 110 次
- Exact learning dynamics of deep linear networks with prior knowledgeLukas Braun, Clémentine C. J. Dominé, James Fitzgerald, Andrew M. SaxeNeurIPS 2022 · 被引用 75 次
- Get rich quick: exact solutions reveal how unbalanced initializations promote rapid feature learningDaniel Kunin, Allan Raventós, Clémentine C. J. Dominé, Feng Chen 等NeurIPS 2024 · 被引用 48 次
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