Results 11 to 20 of about 41,144 (258)
Bayesian Deep Reinforcement Learning via Deep Kernel Learning [PDF]
International Journal of Computational Intelligence Systems, 2018 Reinforcement learning (RL) aims to resolve the sequential decision-making under uncertainty problem where an agent needs to interact with an unknown environment with the expectation of optimising the cumulative long-term reward. Many real-world problems
Junyu Xuan +3 more
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Deep Learning and Bayesian Methods [PDF]
EPJ Web of Conferences, 2017 A revolution is underway in which deep neural networks are routinely used to solve diffcult problems such as face recognition and natural language understanding.
Prosper Harrison B.
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Deep Bayesian Unsupervised Lifelong Learning [PDF]
Neural Networks, 2022 Lifelong Learning (LL) refers to the ability to continually learn and solve new problems with incremental available information over time while retaining previous knowledge. Much attention has been given lately to Supervised Lifelong Learning (SLL) with a stream of labelled data.
Tingting Zhao +3 more
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A Survey on Bayesian Deep Learning [PDF]
ACM Computing Surveys, 2020 A comprehensive artificial intelligence system needs to not only perceive the environment with different “senses” (e.g., seeing and hearing) but also infer the world’s conditional (or even causal) relations and corresponding uncertainty. The past decade has seen major advances in many perception tasks, such as visual object recognition and
Wang, Hao, Yeung, Dit Yan
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Trans. Mach. Learn. Res., 2023 Uncertainty quantification and robustness to distribution shifts are important goals in machine learning and artificial intelligence. Although Bayesian Neural Networks (BNNs) allow for uncertainty in the predictions to be assessed, different sources of predictive uncertainty cannot be distinguished properly.
Caprio, Michele +6 more
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Bayesian Graph Convolutional Neural Networks via Tempered MCMC
IEEE Access, 2021 Deep learning models, such as convolutional neural networks, have long been applied to image and multi-media tasks, particularly those with structured data.
Rohitash Chandra +3 more
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Bayesian deep learning on a quantum computer [PDF]
Quantum Machine Intelligence, 2019 Bayesian methods in machine learning, such as Gaussian processes, have great advantages com-pared to other techniques. In particular, they provide estimates of the uncertainty associated with a prediction. Extending the Bayesian approach to deep architectures has remained a major challenge. Recent results connected deep feedforward neural networks with
Zhikuan Zhao +3 more
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On the optimization and pruning for Bayesian deep learning
CoRR, 2022 11 ...
Xiongwen Ke, Yanan Fan
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Collapsed Inference for Bayesian Deep Learning
Advances in Neural Information Processing Systems 36, 2023 Bayesian neural networks (BNNs) provide a formalism to quantify and calibrate uncertainty in deep learning. Current inference approaches for BNNs often resort to few-sample estimation for scalability, which can harm predictive performance, while its alternatives tend to be computationally prohibitively expensive. We tackle this challenge by revealing a
Zhe Zeng 0001, Guy Van den Broeck
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Conditional Deep Gaussian Processes: Empirical Bayes Hyperdata Learning
Entropy, 2021 It is desirable to combine the expressive power of deep learning with Gaussian Process (GP) in one expressive Bayesian learning model. Deep kernel learning showed success as a deep network used for feature extraction.
Chi-Ken Lu, Patrick Shafto
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