instruction:
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.
question:
In classic papers, Zellner (1988, 2002) demonstrated that Bayesian inference could be derived as the solution to an information theoretic functional.   Below we derive a generalized form of this functional as a variational lower bound of a predictive information bottleneck objective.   This generalized functional encompasses most modern inference procedures and suggests novel ones.
answer:
Rederive a wide class of inference procedures from an global information bottleneck objective.


question:
Deep ensembles have been empirically shown to be a promising approach for improving accuracy, uncertainty  and out-of-distribution robustness of deep learning models. While deep ensembles were theoretically motivated by the bootstrap, non-bootstrap ensembles trained with just random initialization also perform well in practice, which suggests that there could be other explanations for why deep ensembles work well. Bayesian neural networks, which learn distributions over the parameters of the network, are theoretically well-motivated by Bayesian principles, but do not perform as well as deep ensembles in practice, particularly under dataset shift. One possible explanation for this gap between theory and practice is that popular scalable approximate Bayesian methods tend to focus on a single mode, whereas deep ensembles tend to explore diverse modes in function space. We investigate this hypothesis by building on recent work on understanding the loss landscape of neural networks and adding our own exploration to measure the similarity of functions in the space of predictions. Our results show that random initializations explore entirely different modes, while functions along an optimization trajectory or sampled from the subspace thereof cluster within a single mode predictions-wise, while often deviating significantly in the weight space. We demonstrate that while low-loss connectors between modes exist, they are not connected in the space of predictions. Developing the concept of the diversity--accuracy plane, we show that the decorrelation power of random initializations is unmatched by popular subspace sampling methods.
answer:
We study deep ensembles through the lens of loss landscape and the space of predictions, demonstrating that the decorrelation power of random initializations is unmatched by subspace sampling that only explores a single mode.


question:
Generating and scheduling activities is particularly challenging
 when considering both consumptive resources and
 complex resource interactions such as time-dependent resource
 usage.We present three methods of determining valid
 temporal placement intervals for an activity in a temporally
 grounded plan in the presence of such constraints. We introduce
 the Max Duration and Probe algorithms which are
 sound, but incomplete, and the Linear algorithm which is
 sound and complete for linear rate resource consumption.
 We apply these techniques to the problem of scheduling
 awakes for a planetary rover where the awake durations
 are affected by existing activities. We demonstrate how the
 Probe algorithm performs competitively with the Linear algorithm
 given an advantageous problem space and well-defined
 heuristics. We show that the Probe and Linear algorithms
 outperform the Max Duration algorithm empirically.
 We then empirically present the runtime differences between
 the three algorithms. The Probe algorithm is currently base-lined
 for use in the onboard scheduler for NASA’s next planetary
 rover, the Mars 2020 rover.
answer:

This paper describes and analyzes three methods to schedule non-fixed duration activities in the presence of consumptive resources.