Recent work in Bayesian Experiment Design (BED) has shown the value of using Deep Learning (DL) to obtain highly efficient adaptive experiment designs. In this paper, we argue that a central bottleneck of DL training for BED is belief explosion. Specifically, as an agent progresses deeper into an experiment, the effective number of realisable beliefs grows enormously, placing significant sampling burdens on offline training schemes in an effort to gather experience from all regions of belief space. We argue that choosing an appropriate inductive bias for actor/critic networks is a critical component in mitigating the effects of belief explosion and has so far been overlooked in the BED literature. We show how Graph Neural Networks are particularly well-suited for BEDDL training due to their domain permutation equivariance properties, resulting in multiple orders of magnitude improvement to sample efficiency compared to naive parameterizations.

Proposition(Boundary. Let g( ): =2 ( ) 1.

Neural Information Processing Systems http://nips.cc/

Our experiments with multiple real-world dynamic graph datasets demonstrate thatSI-VGRNN andVGRNN consistently outperform the existing baseline and state-of-the-art methods by a significant marginindynamiclinkprediction.

Neural Information Processing Systems http://nips.cc/

Then,wecalculatethe mean stiffness of the value network across all state pairs and report its average computed over all trainingepochs. Eachagentis trained on 200 training levels for 25M environment steps. The mean and standard deviation are computedover10differentruns. Morespecifically,wecollect100 training episodes throughout the training and evaluate the value network prediction for the initial stateofeachtrajectory. Each agent is trained on 200 training levels for 25M environment steps.

As humans rely on audio localization cues for full scene awareness,spatial audio is a crucial componentof360 video.

Machine learning models based on temporal point processes arethe state ofthe artinawide variety ofapplications involving discrete events incontinuous time.

Deep learning has seen remarkable progress in a variety of areas, with advanced development in over-parameterized neural networks.

Studying molecular interactions across different data types helps reveal signal transduction mechanisms across different classes of molecules.