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Found 7 papers, 5 papers with code
Thermal Bridges on Building Rooftops
Thermal Bridges on Building Rooftops (TBBR) is a multi-channel remote sensing dataset.
Ultra-High-Resolution Detector Simulation with Intra-Event Aware GAN and Self-Supervised Relational Reasoning
Simulating high-resolution detector responses is a storage-costly and computationally intensive process that has long been challenging in particle physics.
Deep learning approaches to building rooftop thermal bridge detection from aerial images
Thermal bridges are weak points of building envelopes that can lead to energy losses, collection of moisture, and formation of mould in the building fabric.
Ranked #1 on
Object Detection
on TBBR
Learning Tree Structures from Leaves For Particle Decay Reconstruction
In the case of high-energy particle physics, a particle decay forms a hierarchical tree structure of which only the final products can be observed experimentally, and the large combinatorial space of possible trees makes an analytic solution intractable.
AI-based thermal bridge detection of building rooftops on district scale using aerial images
With a neural network approach, we demonstrate a method of automatically detecting thermal bridges on building rooftops from panorama drone images of whole city districts.
Ranked #5 on
Instance Segmentation
on TBBR
Accelerated Computation of a High Dimensional Kolmogorov-Smirnov Distance
We perform power analysis of ddKS and its approximations on a corpus of datasets and compare to other common high dimensional two sample tests and distances: Hotelling's T^2 test and Kullback-Leibler divergence.
Accelerating Neural Network Training with Distributed Asynchronous and Selective Optimization (DASO)
With increasing data and model complexities, the time required to train neural networks has become prohibitively large.
