{"total":13,"rows":[{"id":"2107.03342","title":"A Survey of Uncertainty in Deep Neural Networks","created":"2021-07-07","cited_by_count":680,"area":null},{"id":"2103.14250","title":"Evaluation of deep learning models for multi-step ahead time series prediction","created":"2021-03-26","cited_by_count":25,"area":null},{"id":"2108.03235","title":"SMOTified-GAN for class imbalanced pattern classification problems","created":"2021-08-06","cited_by_count":20,"area":null},{"id":"2104.10662","title":"COVID-19 sentiment analysis via deep learning during the rise of novel cases","created":"2021-04-05","cited_by_count":18,"area":null},{"id":"2101.11881","title":"Deep learning via LSTM models for COVID-19 infection forecasting in India","created":"2021-01-28","cited_by_count":12,"area":null},{"id":"2409.20547","title":"Annealing Flow Generative Models Towards Sampling High-Dimensional and Multi-Modal Distributions","created":"2024-09-30","cited_by_count":11,"area":null},{"id":"2104.05915","title":"Revisiting Bayesian Autoencoders with MCMC","created":"2021-04-13","cited_by_count":10,"area":null},{"id":"2104.08438","title":"Bayesian graph convolutional neural networks via tempered MCMC","created":"2021-04-17","cited_by_count":9,"area":null},{"id":"2304.02595","title":"Bayesian neural networks via MCMC: a Python-based tutorial","created":"2023-04-02","cited_by_count":8,"area":null},{"id":"2412.09333","title":"MaskTerial: A Foundation Model for Automated 2D Material Flake Detection","created":"2024-12-12","cited_by_count":4,"area":null},{"id":"2202.05621","title":"Nonlinear MCMC for Bayesian Machine Learning","created":"2022-02-11","cited_by_count":2,"area":null},{"id":"2102.10551","title":"Delhi air quality prediction using LSTM deep learning models with a focus on COVID-19 lockdown","created":"2021-02-21","cited_by_count":1,"area":null},{"id":"2104.07454","title":"Memory Capacity of Recurrent Neural Networks with Matrix Representation","created":"2021-04-11","cited_by_count":0,"area":null}],"next_cursor":null}