Teacher: 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.
Teacher: Now, understand the problem? If you are still confused, see the following 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.
Solution: We devise adaptive loss scaling to improve mixed precision training that surpass the state-of-the-art results.
Reason: The abstract focusses on designing an adaptive loss scaling method, hence the generated output is correct.

Now, solve this instance: In many robotic applications, it is crucial to maintain a belief about the state of
a system, like the location of a robot or the pose of an object.
These state estimates serve as input for planning and decision making and
provide feedback during task execution.
Recursive Bayesian Filtering algorithms address the state estimation problem,
but they require a model of the process dynamics and the sensory observations as well as
noise estimates that quantify the accuracy of these models.
Recently, multiple works have demonstrated that the process and sensor models can be
learned by end-to-end training through differentiable versions of Recursive Filtering methods.
However, even if the predictive models are known, finding suitable noise models
remains challenging. Therefore, many practical applications rely on very simplistic noise
models.
Our hypothesis is that end-to-end training through differentiable Bayesian
Filters enables us to learn more complex heteroscedastic noise models for
the system dynamics. We evaluate learning such models with different types of
filtering algorithms and on two different robotic tasks. Our experiments show that especially
for sampling-based filters like the Particle Filter, learning heteroscedastic noise
models can drastically improve the tracking performance in comparison to using
constant noise models.
Student:
We evaluate learning heteroscedastic noise models within different Differentiable Bayes Filters