Pages that link to "Item:Q4972551"
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The following pages link to A robust multi-batch L-BFGS method for machine learning (Q4972551):
Displaying 13 items.
- Diagonally scaled memoryless quasi-Newton methods with application to compressed sensing (Q2083385) (← links)
- Limited-memory BFGS with displacement aggregation (Q2149548) (← links)
- Nonmonotone diagonally scaled limited-memory BFGS methods with application to compressive sensing based on a penalty model (Q2165892) (← links)
- A robust multi-batch L-BFGS method for machine learning (Q4972551) (← links)
- Quasi-Newton methods for machine learning: forget the past, just sample (Q5058389) (← links)
- Trust-region algorithms for training responses: machine learning methods using indefinite Hessian approximations (Q5113710) (← links)
- LSOS: Line-search second-order stochastic optimization methods for nonconvex finite sums (Q5879118) (← links)
- A machine-learning-accelerated distributed LBFGS method for field development optimization: algorithm, validation, and applications (Q6074257) (← links)
- An overview of stochastic quasi-Newton methods for large-scale machine learning (Q6097379) (← links)
- Globally Convergent Multilevel Training of Deep Residual Networks (Q6108152) (← links)
- Adaptive sampling quasi-Newton methods for zeroth-order stochastic optimization (Q6175706) (← links)
- A non-monotone trust-region method with noisy oracles and additional sampling (Q6606856) (← links)
- Tuning parameters of deep neural network training algorithms pays off: a computational study (Q6635854) (← links)
