Latent prediction SSL recovers latent trees from PCFG data with sample complexity constant in hierarchy depth L (up to logs), unlike exponential for token-level or supervised methods.
Jacobian descent for multi-objective optimization.arXiv preprint arXiv:2406.16232
7 Pith papers cite this work, alongside 1 external citations. Polarity classification is still indexing.
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A new multi-objective optimization framework using Liouville-space adjoint formulation and simulated annealing achieves nearly 60% success rate in designing photon blockade with g2(0) < 0.1 and bounded brightness.
RAUL is a multi-objective unlearning framework using bounded KL alignment to a reference distribution and Jacobian descent that reports closer performance to full retraining than single-objective baselines.
A unifying framework for gradient aggregation in multi-objective optimization establishes convergence to Pareto stationarity under a sufficient alignment condition and introduces capped MGDA for robustness in adversarial federated learning.
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.
APT augments multi-task learning by adapting advanced optimizers via momentum balancing and light direction preservation, delivering performance gains on four standard MTL datasets.
RI-CC2 simulations of pyrazine internal conversion match the experimental 22 fs decay time, identify Q9a and Q8a modes as drivers, and show the dark A1u state participates actively.
citing papers explorer
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Learn from your own latents and not from tokens: A sample-complexity theory
Latent prediction SSL recovers latent trees from PCFG data with sample complexity constant in hierarchy depth L (up to logs), unlike exponential for token-level or supervised methods.
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Multi-objective design of photon blockade for bright single-photon sources
A new multi-objective optimization framework using Liouville-space adjoint formulation and simulated annealing achieves nearly 60% success rate in designing photon blockade with g2(0) < 0.1 and bounded brightness.
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Multi-Objective Reference-Aligned Machine Unlearning
RAUL is a multi-objective unlearning framework using bounded KL alignment to a reference distribution and Jacobian descent that reports closer performance to full retraining than single-objective baselines.
-
A Unified Framework for Gradient Aggregation in Multi-Objective Optimization
A unifying framework for gradient aggregation in multi-objective optimization establishes convergence to Pareto stationarity under a sufficient alignment condition and introduces capped MGDA for robustness in adversarial federated learning.
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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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Delve into the Applicability of Advanced Optimizers for Multi-Task Learning
APT augments multi-task learning by adapting advanced optimizers via momentum balancing and light direction preservation, delivering performance gains on four standard MTL datasets.
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Accessing the performance of CC2 for excited state dynamics: a benchmark study with pyrazine
RI-CC2 simulations of pyrazine internal conversion match the experimental 22 fs decay time, identify Q9a and Q8a modes as drivers, and show the dark A1u state participates actively.