Papers Probabilistic Deep Learning
“Probabilistic Deep Learning” 태그가 달린 논문 79편 · 필터 해제
Stochastic-Shield: A Probabilistic Approach Towards Training-Free Adversarial Defense in Quantized CNNs
Quantized neural networks (NN) are the common standard to efficiently deploy deep learning models on tiny hardware platforms. However, we notice that quantized NNs are as vulnerable to adversarial attacks as the full-pre…
Adversarial DefenseDeep Learninginput filteringProbabilistic Deep LearningGraph-based Thermal-Inertial SLAM with Probabilistic Neural Networks
Simultaneous Localization and Mapping (SLAM) system typically employ vision-based sensors to observe the surrounding environment. However, the performance of such systems highly depends on the ambient illumination condit…
feature selectionProbabilistic Deep LearningSimultaneous Localization and MappingA probabilistic deep learning approach to automate the interpretation of multi-phase diffraction spectra
Autonomous synthesis and characterization of inorganic materials requires the automatic and accurate analysis of X-ray diffraction spectra. For this task, we designed a probabilistic deep learning algorithm to identify c…
Probabilistic Deep LearningA VAE-Bayesian Deep Learning Scheme for Solar Generation Forecasting based on Dimensionality Reduction
The advancement of distributed generation technologies in modern power systems has led to a widespread integration of renewable power generation at customer side. However, the intermittent nature of renewable energy pose…
Computational EfficiencyDimensionality ReductionProbabilistic Deep LearningGlobal canopy height regression and uncertainty estimation from GEDI LIDAR waveforms with deep ensembles
NASA's Global Ecosystem Dynamics Investigation (GEDI) is a key climate mission whose goal is to advance our understanding of the role of forests in the global carbon cycle. While GEDI is the first space-based LIDAR expli…
Probabilistic Deep LearningregressionProstate Tissue Grading with Deep Quantum Measurement Ordinal Regression
Prostate cancer (PCa) is one of the most common and aggressive cancers worldwide. The Gleason score (GS) system is the standard way of classifying prostate cancer and the most reliable method to determine the severity an…
Binary ClassificationClassificationGeneral ClassificationOrdinal Classification+3Deep Bag-of-Sub-Emotions for Depression Detection in Social Media
This paper presents the Deep Bag-of-Sub-Emotions (DeepBoSE), a novel deep learning model for depression detection in social media. The model is formulated such that it internally computes a differentiable Bag-of-Features…
Deep LearningDepression DetectionProbabilistic Deep LearningTransfer LearningResidue Density Segmentation for Monitoring and Optimizing Tillage Practices
"No-till" and cover cropping are often identified as the leading simple, best management practices for carbon sequestration in agriculture. However, the root of the problem is more complex, with the potential benefits of…
ManagementProbabilistic Deep LearningEstimating and Evaluating Regression Predictive Uncertainty in Deep Object Detectors
Predictive uncertainty estimation is an essential next step for the reliable deployment of deep object detectors in safety-critical tasks. In this work, we focus on estimating predictive distributions for bounding box re…
Objectobject-detectionObject DetectionProbabilistic Deep Learning+2Towards Adversarial Robustness of Bayesian Neural Network through Hierarchical Variational Inference
Recent works have applied Bayesian Neural Network (BNN) to adversarial training, and shown the improvement of adversarial robustness via the BNN's strength of stochastic gradient defense. However, we have found that in g…
Adversarial DefenseAdversarial RobustnessBayesian InferenceProbabilistic Deep Learning+1Energy Forecasting in Smart Grid Systems: A Review of the State-of-the-art Techniques
Energy forecasting has a vital role to play in smart grid (SG) systems involving various applications such as demand-side management, load shedding, and optimum dispatch. Managing efficient forecasting while ensuring the…
ManagementProbabilistic Deep LearningA Quantum-Inspired Probabilistic Model for the Inverse Design of Meta-Structures
In quantum mechanics, a norm squared wave function can be interpreted as the probability density that describes the likelihood of a particle to be measured in a given position or momentum. This statistical property is at…
PositionProbabilistic Deep LearningImproving seasonal forecast using probabilistic deep learning
The path toward realizing the potential of seasonal forecasting and its socioeconomic benefits depends heavily on improving general circulation model based dynamical forecasting systems. To improve dynamical seasonal for…
BenchmarkingDeep LearningProbabilistic Deep LearningLearning Monocular Dense Depth from Events
Event cameras are novel sensors that output brightness changes in the form of a stream of asynchronous events instead of intensity frames. Compared to conventional image sensors, they offer significant advantages: high t…
Depth EstimationDepth PredictionProbabilistic Deep LearningOlympus: a benchmarking framework for noisy optimization and experiment planning
Research challenges encountered across science, engineering, and economics can frequently be formulated as optimization tasks. In chemistry and materials science, recent growth in laboratory digitization and automation h…
BenchmarkingProbabilistic Deep Learningscientific discoveryProbabilistic 3D surface reconstruction from sparse MRI information
Surface reconstruction from magnetic resonance (MR) imaging data is indispensable in medical image analysis and clinical research. A reliable and effective reconstruction tool should: be fast in prediction of accurate we…
3D ReconstructionMedical Image AnalysisPredictionProbabilistic Deep Learning+1Advancing from Predictive Maintenance to Intelligent Maintenance with AI and IIoT
As Artificial Intelligent (AI) technology advances and increasingly large amounts of data become readily available via various Industrial Internet of Things (IIoT) projects, we evaluate the state of the art of predictive…
BIG-bench Machine LearningDecision MakingProbabilistic Deep LearningProbabilistic Deep Learning for Instance Segmentation
Probabilistic convolutional neural networks, which predict distributions of predictions instead of point estimates, led to recent advances in many areas of computer vision, from image reconstruction to semantic segmentat…
Active LearningDeep LearningImage ReconstructionInstance Segmentation+3Movement Tracking by Optical Flow Assisted Inertial Navigation
Robust and accurate six degree-of-freedom tracking on portable devices remains a challenging problem, especially on small hand-held devices such as smartphones. For improved robustness and accuracy, complementary movemen…
Optical Flow EstimationProbabilistic Deep LearningBreastScreening: On the Use of Multi-Modality in Medical Imaging Diagnosis
This paper describes the field research, design and comparative deployment of a multimodal medical imaging user interface for breast screening. The main contributions described here are threefold: 1) The design of an adv…
3D Medical Imaging SegmentationAutomatic Machine Learning Model SelectionBreast Cancer DetectionBreast Mass Segmentation In Whole Mammograms+6