We develop a deep learning framework (DeepAccNet) that estimates per-residue accuracy and residue-residue distance signed error in protein models and uses these predictions to guide Rosetta protein structure refinement. The network uses 3D convolutions t
ExplanationFiltering-basedPrior-basedLearning-based 2024 Venuse TitleVenueTypeCode SGNet: Structure Guided Network via Gradient-Frequency Awareness for Depth Map Super-ResolutionpdfAAAILGithub 2023 Venues TitleVenueTypeCode Guided Depth Super-Resolution by Deep Anisotropic DiffusionpdfCVPRLGithub ...
2. Theory-guided deep-learning load forecasting (TgDLF) In this study, the TgDLF is used to predict the load ratio based on the EnLSTM algorithm [35], and the desired grid load can be obtained based on the load ratio and historical load. The inputs of the TgDLF include historical loa...
Initially, syzkaller was developed with Linux kernel fuzzing in mind, but now it's being extended to support other OS kernels as well. Most of the documentation at this moment is related to the Linux kernel. For other OS kernels check: Akaros, Darwin/XNU, FreeBSD, Fuchsia, NetBSD, OpenBSD...
Below, we discuss one- and few-shot learning models, saliency detectors, second-order statistical models, and anomaly detectors. 2.1 Learning from few samplesFor deep learning algorithms, the ability of “learning quickly from only a few examples is definitely the desired characteristic to emulate ...
摘要: A kernel-guided injection deep network for blind pansharpening is proposed.An interpretable fusion sub-network that adjusts the injected details is designed.The proposed blind network can be applied in real complex degradation situations.
Metzger, N., Daudt, R.C., Schindler, K.: Guided depth super-resolution by deep anisotropic diffusion. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 18237–18246 (2023) Kim, B., Ponce, J., Ham, B.: Deformable kernel networks for joint image...
Zero-shotDepth guidanceIn the past few years, we have witnessed the great progress of image super-resolution (SR) thanks to the power of deep learning. However, a major limitation of the current image SR approaches is that they assume a pre-determined degradation model or kernel, e.g. ...
This repository contains scripts for deep learning for guided medical interventions. For some projects, the complete workflow is implemented, from formatting and annotations to deployment of models in real time. Most projects use ultrasound imaging. ...
Combining Gaussian processes with the expressive power of deep neural networks is commonly done nowadays through deep kernel learning (DKL). Unfortunately, due to the kernel optimization process, this often results in losing their Bayesian benefits. In this study, we present a novel approach for ...