FML-Bench shows a simple greedy hill-climber nearly matches tree search on dense-opportunity tasks while an adaptive agent that broadens search on stagnation outperforms six baselines across 18 tasks.
Barlow twins: Self- supervised learning via redundancy reduction
5 Pith papers cite this work. Polarity classification is still indexing.
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AdaTKG equips temporal knowledge graph entities with per-entity memories updated via a single shared learnable exponential moving average, allowing online adaptation and better reasoning on evolving facts.
A bilevel method learns composite pretraining loss weights online via gradient alignment with a downstream objective, matching tuned baselines at roughly 30% extra cost over one training run.
CoUn emulates retrained-model behavior on forget data by using contrastive learning on retain data to adjust semantic representations while preserving retain clusters via supervised learning, outperforming prior MU methods in experiments.
Pan-FM learns balanced representations across seven organs by adaptively masking dominant organs during pre-training, yielding stronger disease prediction and missing-organ robustness than single-organ or naive multimodal baselines on UK Biobank.
citing papers explorer
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FML-bench: A Controlled Study of AI Research Agent Strategies from the Perspective of Search Dynamics
FML-Bench shows a simple greedy hill-climber nearly matches tree search on dense-opportunity tasks while an adaptive agent that broadens search on stagnation outperforms six baselines across 18 tasks.
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AdaTKG: Adaptive Memory for Temporal Knowledge Graph Reasoning
AdaTKG equips temporal knowledge graph entities with per-entity memories updated via a single shared learnable exponential moving average, allowing online adaptation and better reasoning on evolving facts.
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When Losses Align: Gradient-Based Composite Loss Weighting for Efficient Pretraining
A bilevel method learns composite pretraining loss weights online via gradient alignment with a downstream objective, matching tuned baselines at roughly 30% extra cost over one training run.
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CoUn: Empowering Machine Unlearning via Contrastive Learning
CoUn emulates retrained-model behavior on forget data by using contrastive learning on retain data to adjust semantic representations while preserving retain clusters via supervised learning, outperforming prior MU methods in experiments.
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Pan-FM: A Pan-Organ Foundation Model with Saliency-Guided Masking for Missing Robustness
Pan-FM learns balanced representations across seven organs by adaptively masking dominant organs during pre-training, yielding stronger disease prediction and missing-organ robustness than single-organ or naive multimodal baselines on UK Biobank.