Probabilistic Deep Learning

Benchmarks archive 2025-07-28

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Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

6 datasets whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

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Most implemented papers archive 2025-07-28

30 shown of 34 papers with code (79 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• BreastScreening: On the Use of Multi-Modality in Medical Imaging Diagnosis
  7 Apr 2020 8 repositories listed
  This paper describes the field research, design and comparative deployment of a multimodal medical imaging user interface for breast screening.
• Estimating and Evaluating Regression Predictive Uncertainty in Deep Object Detectors
  13 Jan 2021 3 repositories listed Syntology ran 1 of 1 samples · 0 unverified
  We show that in the context of object detection, training variance networks with negative log likelihood (NLL) can lead to high entropy predictive distributions regardless of the correctness of the output mean.
• Kernel Density Matrices for Probabilistic Deep Learning
  26 May 2023 2 repositories listed
  In doing so, we provide a versatile representation of marginal and joint probability distributions that allows us to develop a differentiable, compositional, and reversible inference procedure that covers a wide range…
• A Simple Approach to Improve Single-Model Deep Uncertainty via Distance-Awareness
  1 May 2022 2 repositories listed Syntology ran 1 of 1 samples · 0 unverified · 1 pointer-only (licence)
  The most popular approaches to estimate predictive uncertainty in deep learning are methods that combine predictions from multiple neural networks, such as Bayesian neural networks (BNNs) and deep ensembles.
• Short-Term Density Forecasting of Low-Voltage Load using Bernstein-Polynomial Normalizing Flows
  29 Apr 2022 2 repositories listed
  The transition to a fully renewable energy grid requires better forecasting of demand at the low-voltage level to increase efficiency and ensure reliable control.
• Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift
  6 Jun 2019 2 repositories listed
  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…
• Conditional deep surrogate models for stochastic, high-dimensional, and multi-fidelity systems
  15 Jan 2019 2 repositories listed
  We present a probabilistic deep learning methodology that enables the construction of predictive data-driven surrogates for stochastic systems.
• Microcanonical Langevin Ensembles: Advancing the Sampling of Bayesian Neural Networks
  10 Feb 2025 1 repository listed
  To tackle these challenges, we introduce an ensembling approach that leverages strategies from optimization and a recently proposed sampler called Microcanonical Langevin Monte Carlo (MCLMC) for efficient, robust and…
• Probabilistic Deep Learning and Transfer Learning for Robust Cryptocurrency Price Prediction
  19 Jun 2024 1 repository listed
  This research is driven by the central goal of introducing a specialized deep learning model tailored to predict digital currency prices, with a specific emphasis on BTC.
• Integrating Physics of the Problem into Data-Driven Methods to Enhance Elastic Full-Waveform Inversion with Uncertainty Quantification
  4 Jun 2024 1 repository listed
  In this paper, by using the knowledge of elastic scattering (physics of the problem) and integrating it with machine learning techniques, we propose methods for the solution of time-harmonic FWI to enhance accuracy…
• Stochastic Latent Transformer: Efficient Modelling of Stochastically Forced Zonal Jets
  25 Oct 2023 1 repository listed
  We present a novel probabilistic deep learning approach, the 'Stochastic Latent Transformer' (SLT), designed for the efficient reduced-order modelling of stochastic partial differential equations.
• Variational Imbalanced Regression: Fair Uncertainty Quantification via Probabilistic Smoothing
  11 Jun 2023 1 repository listed Syntology ran 6 of 7 samples · 1 unverified · 7 pointer-only (licence)
  Existing regression models tend to fall short in both accuracy and uncertainty estimation when the label distribution is imbalanced.
• Uncertainty Voting Ensemble for Imbalanced Deep Regression
  24 May 2023 1 repository listed Syntology ran 0 of 4 samples · 4 unverified · 4 pointer-only (licence)
  For regression, recent work leverages the continuity of the distribution, while for classification, the trend has been to use ensemble methods, allowing some members to specialize in predictions for sparser regions.
• Benchmarking Probabilistic Deep Learning Methods for License Plate Recognition
  2 Feb 2023 1 repository listed
  Such an uncertainty measure allows to detect false predictions, indicating an analyst when not to trust the result of the automated license plate recognition.
• A Self-Supervised Approach to Reconstruction in Sparse X-Ray Computed Tomography
  30 Oct 2022 1 repository listed
  However, obtaining high-quality object reconstructions for the training dataset requires high x-ray dose measurements that can destroy or alter the specimen before imaging is complete.
• Transductive Decoupled Variational Inference for Few-Shot Classification
  22 Aug 2022 1 repository listed Syntology ran 2 of 8 samples · 6 unverified
  The versatility to learn from a handful of samples is the hallmark of human intelligence.
• FiLM-Ensemble: Probabilistic Deep Learning via Feature-wise Linear Modulation
  31 May 2022 1 repository listed Syntology ran 1 of 5 samples · 4 unverified
  We show that the idea can be extended to uncertainty quantification: by modulating the network activations of a single deep network with FiLM, one obtains a model ensemble with high diversity, and consequently…
• A Novel Deep Learning Model for Hotel Demand and Revenue Prediction amid COVID-19
  8 Mar 2022 1 repository listed
  To this end, it is essential to develop an interpretable forecast model that supports managerial and organizational decision-making.
• Detecting Model Misspecification in Amortized Bayesian Inference with Neural Networks
  16 Dec 2021 1 repository listed
  Neural density estimators have proven remarkably powerful in performing efficient simulation-based Bayesian inference in various research domains.
• Probabilistic Deep Learning to Quantify Uncertainty in Air Quality Forecasting
  5 Dec 2021 1 repository listed
  Through extensive experiments, we describe training probabilistic models and evaluate their predictive uncertainties based on empirical performance, reliability of confidence estimate, and practical applicability.
• Probabilistic Metamodels for an Efficient Characterization of Complex Driving Scenarios
  6 Oct 2021 1 repository listed
  However, despite the safety criticality of AV testing, metamodels are usually seen as a part of an overall approach, and their predictions are not questioned.
• Probabilistic Deep Learning for Electric-Vehicle Energy-Use Prediction
  23 Aug 2021 1 repository listed
  For example, long-distance route planning for such vehicles relies on the prediction of both the expected travel time as well as energy use.
• Graph-based Thermal-Inertial SLAM with Probabilistic Neural Networks
  15 Apr 2021 1 repository listed
  Simultaneous Localization and Mapping (SLAM) system typically employ vision-based sensors to observe the surrounding environment.
• Global canopy height regression and uncertainty estimation from GEDI LIDAR waveforms with deep ensembles
  5 Mar 2021 1 repository listed
  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.
• Towards Adversarial Robustness of Bayesian Neural Network through Hierarchical Variational Inference
  1 Jan 2021 1 repository listed
  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.
• A Quantum-Inspired Probabilistic Model for the Inverse Design of Meta-Structures
  11 Nov 2020 1 repository listed Syntology ran 0 of 1 samples · 1 unverified · 1 pointer-only (licence)
  Here, inspired by quantum theory, we propose a probabilistic deep learning paradigm for the inverse design of functional meta-structures.
• Learning Monocular Dense Depth from Events
  16 Oct 2020 1 repository listed
  Event cameras are novel sensors that output brightness changes in the form of a stream of asynchronous events instead of intensity frames.
• Olympus: a benchmarking framework for noisy optimization and experiment planning
  8 Oct 2020 1 repository listed
  Experiment planning strategies based on off-the-shelf optimization algorithms can be employed in fully autonomous research platforms to achieve desired experimentation goals with the minimum number of trials.
• Multivariate Probabilistic Time Series Forecasting via Conditioned Normalizing Flows
  14 Feb 2020 1 repository listed
  In this work we model the multivariate temporal dynamics of time series via an autoregressive deep learning model, where the data distribution is represented by a conditioned normalizing flow.
• DeepSynth: Automata Synthesis for Automatic Task Segmentation in Deep Reinforcement Learning
  22 Nov 2019 1 repository listed
  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…

Syntology lines on 7 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.