No abstract available.
Cited By
- Ma W, Li J, Li B and Lu Y (2024). Uncertainty-Aware Deep Video Compression With Ensembles, IEEE Transactions on Multimedia, 26, (7863-7872), Online publication date: 1-Jan-2024.
- Tan L, Wu S, Zhou W and Huang X (2023). Weighted neural tangent kernel: a generalized and improved network-induced kernel, Machine Language, 112:8, (2871-2901), Online publication date: 1-Aug-2023.
- Mena J, Pujol O and Vitrià J (2021). A Survey on Uncertainty Estimation in Deep Learning Classification Systems from a Bayesian Perspective, ACM Computing Surveys, 54:9, (1-35), Online publication date: 31-Dec-2022.
- Wu Q, Fu Y, Zhang Y and Amin M (2022). Structured Bayesian compressive sensing exploiting dirichlet process priors, Signal Processing, 201:C, Online publication date: 1-Dec-2022.
- Li J, Liu Y and Wang W (2022). Convolutional spectral kernel learning with generalization guarantees, Artificial Intelligence, 313:C, Online publication date: 1-Dec-2022.
- Villacampa-Calvo C, Hernández-Muñoz G and Hernández-Lobato D (2022). Alpha-divergence minimization for deep Gaussian processes, International Journal of Approximate Reasoning, 150:C, (139-171), Online publication date: 1-Nov-2022.
- Yang X, Wang J, Zhao X, Li S and Tao Z Calibrate Automated Graph Neural Network via Hyperparameter Uncertainty Proceedings of the 31st ACM International Conference on Information & Knowledge Management, (4640-4644)
- Zhang X, Chan F, Yan C and Bose I (2022). Towards risk-aware artificial intelligence and machine learning systems, Decision Support Systems, 159:C, Online publication date: 1-Aug-2022.
- Zhang B and Shin Y (2022). A probabilistic neural network for uncertainty prediction with applications to manufacturing process monitoring▪, Applied Soft Computing, 124:C, Online publication date: 1-Jul-2022.
- Hilal W, Gadsden S and Yawney J (2022). Financial Fraud, Expert Systems with Applications: An International Journal, 193:C, Online publication date: 1-May-2022.
- Robson B, Boray S and Weisman J (2022). Mining real-world high dimensional structured data in medicine and its use in decision support. Some different perspectives on unknowns, interdependency, and distinguishability, Computers in Biology and Medicine, 141:C, Online publication date: 1-Feb-2022.
- Fernández J, Chiachío M, Chiachío J, Muñoz R and Herrera F (2022). Uncertainty quantification in Neural Networks by Approximate Bayesian Computation, Engineering Applications of Artificial Intelligence, 107:C, Online publication date: 1-Jan-2022.
- Cobb A, Jalaian B, Bastian N and Russell S Robust decision-making in the internet of battlefield things using bayesian neural networks Proceedings of the Winter Simulation Conference, (1-12)
- Liu C, Sun X, Wang J, Tang H, Li T, Qin T, Chen W and Liu T Learning causal semantic representation for out-of-distribution prediction Proceedings of the 35th International Conference on Neural Information Processing Systems, (6155-6170)
- Pleiss G and Cunningham J The limitations of large width in neural networks Proceedings of the 35th International Conference on Neural Information Processing Systems, (3349-3363)
- Sevilla-Salcedo C, Gómez-Verdejo V and Olmos P (2021). Sparse semi-supervised heterogeneous interbattery bayesian analysis, Pattern Recognition, 120:C, Online publication date: 1-Dec-2021.
- Mae Y, Kumagai W and Kanamori T (2022). Uncertainty propagation for dropout-based Bayesian neural networks, Neural Networks, 144:C, (394-406), Online publication date: 1-Dec-2021.
- Roman I, Santana R, Mendiburu A and Lozano J (2021). Evolving Gaussian process kernels from elementary mathematical expressions for time series extrapolation, Neurocomputing, 462:C, (426-439), Online publication date: 28-Oct-2021.
- Krivec T, Kocijan J, Perne M, Grašic B, Božnar M and Mlakar P (2021). Data-driven method for the improving forecasts of local weather dynamics, Engineering Applications of Artificial Intelligence, 105:C, Online publication date: 1-Oct-2021.
- Pomponi J, Scardapane S and Uncini A (2021). Bayesian Neural Networks with Maximum Mean Discrepancy regularization, Neurocomputing, 453:C, (428-437), Online publication date: 17-Sep-2021.
- Wu D, Gao L, Chinazzi M, Xiong X, Vespignani A, Ma Y and Yu R Quantifying Uncertainty in Deep Spatiotemporal Forecasting Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, (1841-1851)
- Guo L, Jin J, Zhang H, Zheng Z, Yang Z, Xing Z, Pan F, Niu L, Wu F, Xu H, Yu C, Jiang Y and Zhu X We Know What You Want: An Advertising Strategy Recommender System for Online Advertising Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, (2919-2927)
- Kulkarni S, Bhat S and Moritz C (2021). Architecting for Artificial Intelligence with Emerging Nanotechnology, ACM Journal on Emerging Technologies in Computing Systems, 17:3, (1-33), Online publication date: 31-Jul-2021.
- Selitskiy S, Christou N and Selitskaya N Isolating Uncertainty of the Face Expression Recognition with the Meta-Learning Supervisor Neural Network 2021 5th International Conference on Artificial Intelligence and Virtual Reality (AIVR), (104-112)
- Bhatt U, Antorán J, Zhang Y, Liao Q, Sattigeri P, Fogliato R, Melançon G, Krishnan R, Stanley J, Tickoo O, Nachman L, Chunara R, Srikumar M, Weller A and Xiang A Uncertainty as a Form of Transparency: Measuring, Communicating, and Using Uncertainty Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society, (401-413)
- Miikkulainen R, Meyerson E, Qiu X, Sinha U, Kumar R, Hofmann K, Yan Y, Ye M, Yang J, Caiazza D and Brown S Evaluating medical aesthetics treatments through evolved age-estimation models Proceedings of the Genetic and Evolutionary Computation Conference, (1009-1017)
- Abdulsamad H, Nickl P, Klink P and Peters J A Variational Infinite Mixture for Probabilistic Inverse Dynamics Learning 2021 IEEE International Conference on Robotics and Automation (ICRA), (4216-4222)
- Chen Z, Wang Y, Lin D, Cheng D, Hong L, Chi E and Cui C Beyond Point Estimate: Inferring Ensemble Prediction Variation from Neuron Activation Strength in Recommender Systems Proceedings of the 14th ACM International Conference on Web Search and Data Mining, (76-84)
- Palakkadavath R and Srijith P Bayesian Generative Adversarial Nets with Dropout Inference 8th ACM IKDD CODS and 26th COMAD, (92-100)
- Xie X, Hu X, Gu P, Li S, Ji Y and Xie Y (2020). NNBench-X, ACM Transactions on Architecture and Code Optimization, 17:4, (1-25), Online publication date: 31-Dec-2021.
- Wenzel F, Snoek J, Tran D and Jenatton R Hyperparameter ensembles for robustness and uncertainty quantification Proceedings of the 34th International Conference on Neural Information Processing Systems, (6514-6527)
- Chien J Neural Bayesian Information Processing Proceedings of the 29th ACM International Conference on Information & Knowledge Management, (3501-3502)
- Wang H and Yeung D (2020). A Survey on Bayesian Deep Learning, ACM Computing Surveys, 53:5, (1-37), Online publication date: 15-Oct-2020.
- Piovarči M, Kaufman D, Levin D and Didyk P (2020). Fabrication-in-the-loop co-optimization of surfaces and styli for drawing haptics, ACM Transactions on Graphics, 39:4, (116:1-116:16), Online publication date: 31-Aug-2020.
- Sakhi O, Bonner S, Rohde D and Vasile F BLOB: A Probabilistic Model for Recommendation that Combines Organic and Bandit Signals Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, (783-793)
- Zhang X, Qian B, Cao S, Li Y, Chen H, Zheng Y and Davidson I INPREM: An Interpretable and Trustworthy Predictive Model for Healthcare Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, (450-460)
- Goldstein O, Kachuee M, Karkkainen K and Sarrafzadeh M (2020). Target-Focused Feature Selection Using Uncertainty Measurements in Healthcare Data, ACM Transactions on Computing for Healthcare, 1:3, (1-17), Online publication date: 31-Jul-2020.
- Liu H, Jing L, Wen J, Wu Z, Sun X, Wang J, Xiao L and Yu J Deep Global and Local Generative Model for Recommendation Proceedings of The Web Conference 2020, (551-561)
- Gordon J and Hernández-Lobato J (2020). Combining deep generative and discriminative models for Bayesian semi-supervised learning, Pattern Recognition, 100:C, Online publication date: 1-Apr-2020.
- Zhang X and Mahadevan S (2020). Bayesian neural networks for flight trajectory prediction and safety assessment, Decision Support Systems, 131:C, Online publication date: 1-Apr-2020.
- Chandra R and Kapoor A (2020). Bayesian neural multi-source transfer learning, Neurocomputing, 378:C, (54-64), Online publication date: 22-Feb-2020.
- Gurevich P and Stuke H (2020). Gradient conjugate priors and multi-layer neural networks, Artificial Intelligence, 278:C, Online publication date: 1-Jan-2020.
- Liu H, Jing L, Qian Y and Yu J (2019). Adaptive Local Low-rank Matrix Approximation for Recommendation, ACM Transactions on Information Systems, 37:4, (1-34), Online publication date: 31-Oct-2019.
- Rios G and Tobar F (2019). Compositionally-warped Gaussian processes, Neural Networks, 118:C, (235-246), Online publication date: 1-Oct-2019.
- Chandra R, Jain K, Deo R and Cripps S (2022). Langevin-gradient parallel tempering for Bayesian neural learning, Neurocomputing, 359:C, (315-326), Online publication date: 24-Sep-2019.
- Tsuchida R, Roosta F and Gallagher M Exchangeability and kernel invariance in trained MLPs Proceedings of the 28th International Joint Conference on Artificial Intelligence, (3592-3598)
- Gurevich P and Stuke H (2019). Pairing an arbitrary regressor with an artificial neural network estimating aleatoric uncertainty, Neurocomputing, 350:C, (291-306), Online publication date: 20-Jul-2019.
- Pang G, Yang L and Karniadakis G (2019). Neural-net-induced Gaussian process regression for function approximation and PDE solution, Journal of Computational Physics, 384:C, (270-288), Online publication date: 1-May-2019.
- Wang G, Li W, Aertsen M, Deprest J, Ourselin S and Vercauteren T (2019). Aleatoric uncertainty estimation with test-time augmentation for medical image segmentation with convolutional neural networks, Neurocomputing, 338:C, (34-45), Online publication date: 21-Apr-2019.
- Cardelli L, Kwiatkowska M, Laurenti L and Patane A Robustness guarantees for Bayesian inference with Gaussian processes Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence and Thirty-First Innovative Applications of Artificial Intelligence Conference and Ninth AAAI Symposium on Educational Advances in Artificial Intelligence, (7759-7768)
- Xiao Y and Wang W Quantifying uncertainties in natural language processing tasks Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence and Thirty-First Innovative Applications of Artificial Intelligence Conference and Ninth AAAI Symposium on Educational Advances in Artificial Intelligence, (7322-7329)
- Li C, Chen C, Pu Y, Henao R and Carin L Communication-efficient stochastic gradient MCMC for neural networks Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence and Thirty-First Innovative Applications of Artificial Intelligence Conference and Ninth AAAI Symposium on Educational Advances in Artificial Intelligence, (4173-4180)
- Finn C, Xu K and Levine S Probabilistic model-agnostic meta-learning Proceedings of the 32nd International Conference on Neural Information Processing Systems, (9537-9548)
- Chua K, Calandra R, McAllister R and Levine S Deep reinforcement learning in a handful of trials using probabilistic dynamics models Proceedings of the 32nd International Conference on Neural Information Processing Systems, (4759-4770)
- Uurtio V, Monteiro J, Kandola J, Shawe-Taylor J, Fernandez-Reyes D and Rousu J (2017). A Tutorial on Canonical Correlation Methods, ACM Computing Surveys, 50:6, (1-33), Online publication date: 30-Nov-2018.
- Baghbani G and Eskandari F (2018). Calculating the required cash in bank branches, Neural Computing and Applications, 30:9, (2831-2841), Online publication date: 1-Nov-2018.
- Zsolnai-Fehér K, Wonka P and Wimmer M (2018). Gaussian material synthesis, ACM Transactions on Graphics, 37:4, (1-14), Online publication date: 31-Aug-2018.
- Dunlop M, Girolami M, Stuart A and Teckentrup A (2018). How deep are deep Gaussian processes?, The Journal of Machine Learning Research, 19:1, (2100-2145), Online publication date: 1-Jan-2018.
- Kendall A and Gal Y What uncertainties do we need in Bayesian deep learning for computer vision? Proceedings of the 31st International Conference on Neural Information Processing Systems, (5580-5590)
- Tran D, Ranganath R and Blei D Hierarchical implicit models and likelihood-free variational inference Proceedings of the 31st International Conference on Neural Information Processing Systems, (5529-5539)
- Louizos C, Ullrich K and Welling M Bayesian compression for deep learning Proceedings of the 31st International Conference on Neural Information Processing Systems, (3290-3300)
- Zhao W, Kong Y, Ding Z and Fu Y Deep Active Learning Through Cognitive Information Parcels Proceedings of the 25th ACM international conference on Multimedia, (952-960)
- Louizos C and Welling M Multiplicative normalizing flows for variational Bayesian neural networks Proceedings of the 34th International Conference on Machine Learning - Volume 70, (2218-2227)
- Li Y and Gal Y Dropout inference in Bayesian neural networks with alpha-divergences Proceedings of the 34th International Conference on Machine Learning - Volume 70, (2052-2061)
- Bach F (2017). On the equivalence between kernel quadrature rules and random feature expansions, The Journal of Machine Learning Research, 18:1, (714-751), Online publication date: 1-Jan-2017.
- Bach F (2017). Breaking the curse of dimensionality with convex neural networks, The Journal of Machine Learning Research, 18:1, (629-681), Online publication date: 1-Jan-2017.
- Gal Y and Ghahramani Z A theoretically grounded application of dropout in recurrent neural networks Proceedings of the 30th International Conference on Neural Information Processing Systems, (1027-1035)
- Wang H, Shi X and Yeung D Natural-parameter networks Proceedings of the 30th International Conference on Neural Information Processing Systems, (118-126)
- Li C, Chen C, Fan K and Carin L High-order stochastic gradient thermostats for Bayesian learning of deep models Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, (1795-1801)
- Li C, Chen C, Carlson D and Carin L Preconditioned Stochastic Gradient Langevin Dynamics for deep neural networks Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, (1788-1794)
- Ghosh S, Fave F and Yedidia J Assumed density filtering methods for learning Bayesian neural networks Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, (1589-1595)
- Huang W, Zhao D, Sun F, Liu H and Chang E Scalable Gaussian process regression using deep neural networks Proceedings of the 24th International Conference on Artificial Intelligence, (3576-3582)
- Eggensperger K, Hutter F, Hoos H and Leyton-Brown K Surrogate benchmarks for hyperparameter optimization Proceedings of the 2014 International Conference on Meta-learning and Algorithm Selection - Volume 1201, (24-31)
- Garnett R, Osborne M and Hennig P Active learning of linear embeddings for Gaussian processes Proceedings of the Thirtieth Conference on Uncertainty in Artificial Intelligence, (230-239)
- Bornholt J, Mytkowicz T and McKinley K (2014). Uncertain
, ACM SIGARCH Computer Architecture News, 42:1, (51-66), Online publication date: 5-Apr-2014. - Bornholt J, Mytkowicz T and McKinley K (2014). Uncertain
, ACM SIGPLAN Notices, 49:4, (51-66), Online publication date: 5-Apr-2014. - Bornholt J, Mytkowicz T and McKinley K Uncertain
Proceedings of the 19th international conference on Architectural support for programming languages and operating systems, (51-66) - Bengio Y Deep learning of representations Proceedings of the First international conference on Statistical Language and Speech Processing, (1-37)
- Bengio Y (2009). Learning Deep Architectures for AI, Foundations and Trends® in Machine Learning, 2:1, (1-127), Online publication date: 1-Jan-2009.
- Lee S, Chatalbashev V, Vickrey D and Koller D Learning a meta-level prior for feature relevance from multiple related tasks Proceedings of the 24th international conference on Machine learning, (489-496)
- Poveda J and Vellido A Neural network models for language acquisition Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning, (1346-1357)
- Ting J, D'Souza A and Schaal S Bayesian regression with input noise for high dimensional data Proceedings of the 23rd international conference on Machine learning, (937-944)
- Zapranis A and Livanis E Prediction intervals for neural network models Proceedings of the 9th WSEAS International Conference on Computers, (1-7)
- Hoegaerts L, Suykens J, Vandewalle J and De Moor B (2005). Subset based least squares subspace regression in RKHS, Neurocomputing, 63, (293-323), Online publication date: 1-Jan-2005.
- Girard A and Murray-Smith R Gaussian processes Switching and Learning in Feedback Systems, (158-184)
- Baxter R and Oliver J (2000). Finding overlapping components with MML, Statistics and Computing, 10:1, (5-16), Online publication date: 1-Jan-2000.
Recommendations
Bayesian neural networks
A neural network that uses the basic Hebbian learning rule and the Bayesian combination function is defined. Analogously to Hopfield's neural network, the convergence for the Bayesian neural network that asynchronously updates its neurons' states is ...
Bayesian Perceptron: Towards fully Bayesian Neural Networks
2020 59th IEEE Conference on Decision and Control (CDC)Artificial neural networks (NNs) have become the de facto standard in machine learning. They allow learning highly nonlinear transformations in a plethora of applications. However, NNs usually only provide point estimates without systematically ...
Probabilistic backpropagation for scalable learning of Bayesian neural networks
ICML'15: Proceedings of the 32nd International Conference on International Conference on Machine Learning - Volume 37Large multilayer neural networks trained with backpropagation have recently achieved state-of-the-art results in a wide range of problems. However, using backprop for neural net learning still has some disadvantages, e.g., having to tune a large number ...