In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
Example: Mixed precision training (MPT) is becoming a practical technique to improve the speed and energy efficiency of training deep neural networks by leveraging the fast hardware support for IEEE half-precision floating point that is available in existing GPUs. MPT is typically used in combination with a technique called loss scaling, that works by scaling up the loss value up before the start of backpropagation in order to minimize the impact of numerical underflow on training. Unfortunately, existing methods make this loss scale value a hyperparameter that needs to be tuned per-model, and a single scale cannot be adapted to different layers at different training stages. We introduce a loss scaling-based training method called adaptive loss scaling that makes MPT easier and more practical to use, by removing the need to tune a model-specific loss scale hyperparameter. We achieve this by introducing layer-wise loss scale values which are automatically computed during training to deal with underflow more effectively than existing methods. We present experimental results on a variety of networks and tasks that show our approach can shorten the time to convergence and improve accuracy, compared with using the existing state-of-the-art MPT and single-precision floating point.
Example solution: We devise adaptive loss scaling to improve mixed precision training that surpass the state-of-the-art results.
Example explanation: The abstract focusses on designing an adaptive loss scaling method, hence the generated output is correct.

Problem: We consider the problem of generating configurations that satisfy physical constraints for optimal material nano-pattern design, where multiple (and often conflicting) properties need to be simultaneously satisfied. Consider, for example, the trade-off between thermal resistance, electrical conductivity, and mechanical stability needed to design a nano-porous template with optimal thermoelectric efficiency. To that end, we leverage the posterior regularization framework andshow that this constraint satisfaction problem can be formulated as sampling froma Gibbs distribution. The main challenges come from the black-box nature ofthose physical constraints, since they are obtained via solving highly non-linearPDEs. To overcome those difficulties, we introduce Surrogate-based Constrained Langevin dynamics for black-box sampling. We explore two surrogate approaches. The first approach exploits zero-order approximation of gradients in the Langevin Sampling and we refer to it as Zero-Order Langevin. In practice, this approach can be prohibitive since we still need to often query the expensive PDE solvers. The second approach approximates the gradients in the Langevin dynamics with deep neural networks, allowing us an efficient sampling strategy using the surrogate model. We prove the convergence of those two approaches when the target distribution is log-concave and smooth. We show the effectiveness of both approaches in designing optimal nano-porous material configurations, where the goal is to produce nano-pattern templates with low thermal conductivity and reasonable mechanical stability.
Solution: We propose surrogate based Constrained Langevin sampling with application in nano-porous material configuration design.