MIST fixes unreliable splits in streaming decision trees for class-incremental learning by replacing Hoeffding-style bounds with a K-independent McDiarmid radius on Gini, plus Bayesian parent-to-child inheritance and per-leaf quantile sketches.
Learning without forgetting.IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(12):2935–2947
3 Pith papers cite this work, alongside 3,056 external citations. Polarity classification is still indexing.
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MeMo encodes new knowledge into a separate memory model that integrates with frozen LLMs, showing strong performance on QA benchmarks while avoiding catastrophic forgetting and working without access to model weights.
Fine-tuning a pop-pretrained Music Transformer on jazz recovers pop chord accuracy to baseline when mixing in about 2.5K pop samples alongside the jazz data.
citing papers explorer
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MIST: Reliable Streaming Decision Trees for Online Class-Incremental Learning via McDiarmid Bound
MIST fixes unreliable splits in streaming decision trees for class-incremental learning by replacing Hoeffding-style bounds with a K-independent McDiarmid radius on Gini, plus Bayesian parent-to-child inheritance and per-leaf quantile sketches.
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MeMo: Memory as a Model
MeMo encodes new knowledge into a separate memory model that integrates with frozen LLMs, showing strong performance on QA benchmarks while avoiding catastrophic forgetting and working without access to model weights.
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Empirical Study of Pop and Jazz Mix Ratios for Genre-Adaptive Chord Generation
Fine-tuning a pop-pretrained Music Transformer on jazz recovers pop chord accuracy to baseline when mixing in about 2.5K pop samples alongside the jazz data.