Preconditioned delta-rule models with a diagonal curvature approximation improve upon standard DeltaNet, GDN, and KDA by better approximating the test-time regression objective.
Optimizing neural networks with kronecker-factored approximate curvature
10 Pith papers cite this work. Polarity classification is still indexing.
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Data-similarity and data-influence produce significantly overlapping rankings of training documents for LLM outputs, with asymmetry allowing a favorable cost-accuracy trade-off.
New canary crafting via greedy influence-based init and bilevel optimization for diversity in embedding space yields stronger one-run privacy leakage estimates at lower cost.
Asteria is a runtime system that enables second-order optimization for LLMs by dynamically distributing optimizer state across GPU, CPU, and NVMe while using asynchronous inverse-root computations and bounded-staleness synchronization.
Deep Boltzmann Quantum States with natural-gradient optimization and annealing-like training match exact or best-known solutions for large infinite-range Ising spin glasses and solve job shop scheduling instances.
JACTUS unifies low-rank compression and task adaptation via a task-aware union of subspaces and global rank allocation by marginal gain, outperforming 100% PEFT methods like DoRA on ViT-Base (89.2% avg) and Llama2-7B (80.9% avg) at 80% retained parameters.
Natural Riemannian gradient descent enables optimization of functional tensor networks for general losses and shows improved convergence on classification tasks.
Loss-aware natural gradient variants are introduced by embedding the loss hypersurface in a statistical manifold or using quantum state overlaps, yielding conformal updates that adjust effective step size.
Refines subspace preconditioning for randomized linear solvers via QR-like factorization, enabling implicit use and proving expected linear convergence while reducing to a smaller system with good singular values.
Introduces natural-gradient versions of Heavy-Ball and Nesterov momentum methods for function approximation on differentiable nonlinear manifolds.
citing papers explorer
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Preconditioned DeltaNet: Curvature-aware Sequence Modeling for Linear Recurrences
Preconditioned delta-rule models with a diagonal curvature approximation improve upon standard DeltaNet, GDN, and KDA by better approximating the test-time regression objective.
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Quantifying the Agreement Between Data-Influence and Data-Similarity to Understand LLM Behavior
Data-similarity and data-influence produce significantly overlapping rankings of training documents for LLM outputs, with asymmetry allowing a favorable cost-accuracy trade-off.
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Detectability in Diversity: Improved Canary Crafting for Privacy Auditing in One Run
New canary crafting via greedy influence-based init and bilevel optimization for diversity in embedding space yields stronger one-run privacy leakage estimates at lower cost.
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Runtime-Orchestrated Second-Order Optimization for Scalable LLM Training
Asteria is a runtime system that enables second-order optimization for LLMs by dynamically distributing optimizer state across GPU, CPU, and NVMe while using asynchronous inverse-root computations and bounded-staleness synchronization.
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Solving Classical and Quantum Spin Glasses with Deep Boltzmann Quantum States
Deep Boltzmann Quantum States with natural-gradient optimization and annealing-like training match exact or best-known solutions for large infinite-range Ising spin glasses and solve job shop scheduling instances.
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Compress Then Adapt? No, Do It Together via Task-aware Union of Subspaces
JACTUS unifies low-rank compression and task adaptation via a task-aware union of subspaces and global rank allocation by marginal gain, outperforming 100% PEFT methods like DoRA on ViT-Base (89.2% avg) and Llama2-7B (80.9% avg) at 80% retained parameters.
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Natural Riemannian gradient for learning functional tensor networks
Natural Riemannian gradient descent enables optimization of functional tensor networks for general losses and shows improved convergence on classification tasks.
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Loss-aware state space geometry for quantum variational algorithms
Loss-aware natural gradient variants are introduced by embedding the loss hypersurface in a statistical manifold or using quantum state overlaps, yielding conformal updates that adjust effective step size.
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On subspace-constrained preconditioning for randomized iterative methods
Refines subspace preconditioning for randomized linear solvers via QR-like factorization, enabling implicit use and proving expected linear convergence while reducing to a smaller system with good singular values.
-
Natural gradient descent with momentum
Introduces natural-gradient versions of Heavy-Ball and Nesterov momentum methods for function approximation on differentiable nonlinear manifolds.