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Found 10 papers, 5 papers with code
Distilled Reverse Attention Network for Open-world Compositional Zero-Shot Learning
Open-World Compositional Zero-Shot Learning (OW-CZSL) aims to recognize new compositions of seen attributes and objects.
Plug-and-Play Model-Agnostic Counterfactual Policy Synthesis for Deep Reinforcement Learning based Recommendation
Recent advances in recommender systems have proved the potential of Reinforcement Learning (RL) to handle the dynamic evolution processes between users and recommender systems.
A Deep Architecture for Log-Linear Models
We present a novel perspective on deep learning architectures using a partial order structure, which is naturally incorporated into the information geometric formulation of the log-linear model.
Learning as We Go: An Examination of the Statistical Accuracy of COVID19 Daily Death Count Predictions
To assess the accuracy of the IHME models, we examine both forecast accuracy as well as the predictive performance of the 95% prediction intervals provided by the IHME models.
Other Statistics Populations and Evolution
Structured Variational Inference in Continuous Cox Process Models
We propose a scalable framework for inference in an inhomogeneous Poisson process modeled by a continuous sigmoidal Cox process that assumes the corresponding intensity function is given by a Gaussian process (GP) prior transformed with a scaled logistic sigmoid function.
Bayesian Nonparametric Adaptive Spectral Density Estimation for Financial Time Series
Discrimination between non-stationarity and long-range dependency is a difficult and long-standing issue in modelling financial time series.
Efficiency and robustness in Monte Carlo sampling of 3-D geophysical inversions with Obsidian v0.1.2: Setting up for success
We explore the influences of different choices made by the practitioner on the efficiency and accuracy of Bayesian geophysical inversion methods that rely on Markov chain Monte Carlo sampling to assess uncertainty, using a multi-sensor inversion of the three-dimensional structure and composition of a region in the Cooper Basin of South Australia as a case study.
Langevin-gradient parallel tempering for Bayesian neural learning
This not only provides point estimates of optimal set of weights but also the ability to quantify uncertainty in decision making using the posterior distribution.
Multi-core parallel tempering Bayeslands for basin and landscape evolution
The results show that PT in Bayeslands not only reduces the computation time over a multi-core architecture, but also provides a means to improve the sampling process in a multi-modal landscape.
Geophysics Distributed, Parallel, and Cluster Computing
Bayeslands: A Bayesian inference approach for parameter uncertainty quantification in Badlands
The inference of unknown parameters is challenging due to the scarcity of data, sensitivity of the parameter setting and complexity of the Badlands model.
