Papers Probabilistic Deep Learning
“Probabilistic Deep Learning” 태그가 달린 논문 79편 · 필터 해제
Multivariate Probabilistic Time Series Forecasting via Conditioned Normalizing Flows
Time series forecasting is often fundamental to scientific and engineering problems and enables decision making. With ever increasing data set sizes, a trivial solution to scale up predictions is to assume independence b…
Decision MakingMultivariate Time Series ForecastingProbabilistic Deep LearningProbabilistic Time Series Forecasting+3Uncertainty Estimation for End-To-End Learned Dense Stereo Matching via Probabilistic Deep Learning
Motivated by the need to identify erroneous disparity assignments, various approaches for uncertainty and confidence estimation of dense stereo matching have been presented in recent years. As in many other fields, espec…
Probabilistic Deep LearningStereo MatchingUncertainty based Class Activation Maps for Visual Question Answering
Understanding and explaining deep learning models is an imperative task. Towards this, we propose a method that obtains gradient-based certainty estimates that also provide visual attention maps. Particularly, we solve f…
Deep LearningProbabilistic Deep LearningQuestion AnsweringVisual Question Answering+1DeepSynth: Automata Synthesis for Automatic Task Segmentation in Deep Reinforcement Learning
This paper proposes DeepSynth, a method for effective training of deep Reinforcement Learning (RL) agents when the reward is sparse and non-Markovian, but at the same time progress towards the reward requires achieving a…
Deep Reinforcement LearningHierarchical Reinforcement LearningMontezuma's RevengeProbabilistic Deep Learning+4Probabilistic Radiomics: Ambiguous Diagnosis with Controllable Shape Analysis
Radiomics analysis has achieved great success in recent years. However, conventional Radiomics analysis suffers from insufficiently expressive hand-crafted features. Recently, emerging deep learning techniques, e.g., con…
Probabilistic Deep LearningU-CAM: Visual Explanation using Uncertainty based Class Activation Maps
Understanding and explaining deep learning models is an imperative task. Towards this, we propose a method that obtains gradient-based certainty estimates that also provide visual attention maps. Particularly, we solve f…
Deep LearningProbabilistic Deep LearningQuestion AnsweringVisual Question Answering+1Confident Head Circumference Measurement from Ultrasound with Real-time Feedback for Sonographers
Manual estimation of fetal Head Circumference (HC) from Ultrasound (US) is a key biometric for monitoring the healthy development of fetuses. Unfortunately, such measurements are subject to large inter-observer variabili…
Probabilistic Deep LearningOvercoming Limitations of Mixture Density Networks: A Sampling and Fitting Framework for Multimodal Future Prediction
Future prediction is a fundamental principle of intelligence that helps plan actions and avoid possible dangers. As the future is uncertain to a large extent, modeling the uncertainty and multimodality of the future stat…
Future predictionPredictionProbabilistic Deep LearningCan You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift
Modern machine learning methods including deep learning have achieved great success in predictive accuracy for supervised learning tasks, but may still fall short in giving useful estimates of their predictive {\em uncer…
Probabilistic Deep LearningA 3D Probabilistic Deep Learning System for Detection and Diagnosis of Lung Cancer Using Low-Dose CT Scans
We introduce a new computer aided detection and diagnosis system for lung cancer screening with low-dose CT scans that produces meaningful probability assessments. Our system is based entirely on 3D convolutional neural …
Decision MakingDiagnosticGeneral ClassificationLung Nodule Detection+1Hybrid Models with Deep and Invertible Features
We propose a neural hybrid model consisting of a linear model defined on a set of features computed by a deep, invertible transformation (i.e. a normalizing flow). An attractive property of our model is that both p(featu…
Probabilistic Deep LearningConditional deep surrogate models for stochastic, high-dimensional, and multi-fidelity systems
We present a probabilistic deep learning methodology that enables the construction of predictive data-driven surrogates for stochastic systems. Leveraging recent advances in variational inference with implicit distributi…
Probabilistic Deep LearningVariational InferenceDeepTract: A Probabilistic Deep Learning Framework for White Matter Fiber Tractography
We present DeepTract, a deep-learning framework for estimating white matter fibers orientation and streamline tractography. We adopt a data-driven approach for fiber reconstruction from diffusion weighted images (DWI), w…
Probabilistic Deep LearningWhite Matter Fiber TractographyHybrid Active Inference
We describe a framework of hybrid cognition by formulating a hybrid cognitive agent that performs hierarchical active inference across a human and a machine part. We suggest that, in addition to enhancing human cognitive…
BIG-bench Machine LearningProbabilistic Deep LearningRepresentation LearningUncertainty Quantification in CNN-Based Surface Prediction Using Shape Priors
Surface reconstruction is a vital tool in a wide range of areas of medical image analysis and clinical research. Despite the fact that many methods have proposed solutions to the reconstruction problem, most, due to thei…
Medical Image AnalysisProbabilistic Deep LearningSurface ReconstructionUncertainty QuantificationProbabilistic Deep Learning using Random Sum-Product Networks
The need for consistent treatment of uncertainty has recently triggered increased interest in probabilistic deep learning methods. However, most current approaches have severe limitations when it comes to inference, sinc…
Deep LearningGPUProbabilistic Deep LearningDeep Directional Statistics: Pose Estimation with Uncertainty Quantification
Modern deep learning systems successfully solve many perception tasks such as object pose estimation when the input image is of high quality. However, in challenging imaging conditions such as on low-resolution images or…
Deep LearningPose EstimationProbabilistic Deep LearningUncertainty QuantificationHyperedge2vec: Distributed Representations for Hyperedges
Data structured in form of overlapping or non-overlapping sets is found in a variety of domains, sometimes explicitly but often subtly. For example, teams, which are of prime importance in social science studies are \enq…
Probabilistic Deep LearningSentenceLearning Large-Scale Topological Maps Using Sum-Product Networks
In order to perform complex actions in human environments, an autonomous robot needs the ability to understand the environment, that is, to gather and maintain spatial knowledge. Topological map is commonly used for repr…
AttributeProbabilistic Deep Learning