PrimeKG-CL supplies the first continual graph learning benchmark using authentic temporal snapshots from nine biomedical databases, showing strong interactions between embedding decoders and learning strategies plus limits of standard metrics on retention versus forgetting.
Overcoming catastrophic forgetting in neural networks.Proceedings of the National Academy of Sciences, 114(13):3521–3526
7 Pith papers cite this work. Polarity classification is still indexing.
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representative citing papers
RaPO reduces catastrophic forgetting in visual continual learning by shaping rewards around policy drift and stabilizing advantages with cross-task exponential moving averages during reinforcement fine-tuning of multimodal models.
Expert upcycling duplicates experts in an existing MoE checkpoint and continues pre-training to match fixed-size baseline performance with 32% less compute.
CMKL delivers a 60% gain in average precision on continual entity classification in a 129K-entity biomedical KG benchmark by fusing multimodal features and protecting against modality-specific forgetting, while relationship prediction stays comparable to baselines.
BRPC is an online Bayesian calibration framework that decouples parameter tracking from discrepancy modeling for gradual nonstationarity and adds restart mechanisms to handle abrupt regime shifts.
C-Nav is a continual visual navigation framework with dual-path anti-forgetting via feature distillation and replay plus adaptive sampling that outperforms baselines on a new continual object navigation benchmark while using less memory.
SCM enables LLMs to achieve perfect recall in ten-turn conversations by using sleep-like consolidation and adaptive forgetting to reduce memory noise by over 90%.
citing papers explorer
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PrimeKG-CL: A Continual Graph Learning Benchmark on Evolving Biomedical Knowledge Graphs
PrimeKG-CL supplies the first continual graph learning benchmark using authentic temporal snapshots from nine biomedical databases, showing strong interactions between embedding decoders and learning strategies plus limits of standard metrics on retention versus forgetting.
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Overcoming Catastrophic Forgetting in Visual Continual Learning with Reinforcement Fine-Tuning
RaPO reduces catastrophic forgetting in visual continual learning by shaping rewards around policy drift and stabilizing advantages with cross-task exponential moving averages during reinforcement fine-tuning of multimodal models.
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Expert Upcycling: Shifting the Compute-Efficient Frontier of Mixture-of-Experts
Expert upcycling duplicates experts in an existing MoE checkpoint and continues pre-training to match fixed-size baseline performance with 32% less compute.
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CMKL: Modality-Aware Continual Learning for Evolving Biomedical Knowledge Graphs
CMKL delivers a 60% gain in average precision on continual entity classification in a 129K-entity biomedical KG benchmark by fusing multimodal features and protecting against modality-specific forgetting, while relationship prediction stays comparable to baselines.
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Online Bayesian Calibration under Gradual and Abrupt System Changes
BRPC is an online Bayesian calibration framework that decouples parameter tracking from discrepancy modeling for gradual nonstationarity and adds restart mechanisms to handle abrupt regime shifts.
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C-NAV: Towards Self-Evolving Continual Object Navigation in Open World
C-Nav is a continual visual navigation framework with dual-path anti-forgetting via feature distillation and replay plus adaptive sampling that outperforms baselines on a new continual object navigation benchmark while using less memory.
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SCM: Sleep-Consolidated Memory with Algorithmic Forgetting for Large Language Models
SCM enables LLMs to achieve perfect recall in ten-turn conversations by using sleep-like consolidation and adaptive forgetting to reduce memory noise by over 90%.