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https://openreview.net/forum?id=HkxNKk2VKS Global Approximate Inference via Local Linearisation for Temporal Gaussian Processes | OpenReview We unify the extended Kalman filter (EKF) and the state space approach to power expectation propagation (PEP) by solving the intractable moment matching... approximate inference gaussian processes global via local https://drive.google.com/file/d/1LwYJe_Ji2jIOoN7fux8N0jWOC7iJcAhl/view?usp=sharing J_Elsevier_SP_2025_Stochastic_Approximate_Inference_AO_EN.mp3 - Google Drive sp 2025 approximate inference https://github.com/AaltoML/kalman-jax GitHub - AaltoML/kalman-jax: Approximate inference for Markov Gaussian processes using iterated... Approximate inference for Markov Gaussian processes using iterated Kalman smoothing, in JAX - AaltoML/kalman-jax approximate inference https://openreview.net/forum?id=8xAHeICO69 Improving Hyperparameter Learning under Approximate Inference in Gaussian Process Models |... Approximate inference in Gaussian process (GP) models with non-conjugate likelihoods gets entangled with the learning of the model hyperparameters. We improve... approximate inference gaussian process improving hyperparameter learning https://deepai.org/publication/learning-energy-based-approximate-inference-networks-for-structured-applications-in-nlp Learning Energy-Based Approximate Inference Networks for Structured Applications in NLP | DeepAI Aug 27, 2021 - 08/27/21 - Structured prediction in natural language processing (NLP) has a long history. The complex models of structured application come a... approximate inference https://deepai.org/publication/approximate-inference-via-weighted-rademacher-complexity Approximate Inference via Weighted Rademacher Complexity | DeepAI Jan 27, 2018 - 01/27/18 - Rademacher complexity is often used to characterize the learnability of a hypothesis class and is known to be related to the class... approximate inference rademacher complexity via weighted deepai https://jmlr.org/papers/v14/challis13a.html Gaussian Kullback-Leibler Approximate Inference kullback leibler gaussian approximate inference https://warwick.ac.uk/fac/sci/mathsys/people/students/mathsysi/gamper/coreml/topics/intro_approx_inf/ Introduction to Approximate Inference Introduction to Approximate Inference introduction approximate inference https://openreview.net/forum?id=5sg0Uv5H0X Model-based Policy Optimization under Approximate Bayesian Inference | OpenReview Model-based reinforcement learning algorithms~(MBRL) present an exceptional potential to enhance sample efficiency within the realm of online reinforcement... bayesian inference model based policy optimization https://aclanthology.org/2020.emnlp-main.485/ AIN: Fast and Accurate Sequence Labeling with Approximate Inference Network - ACL Anthology Xinyu Wang, Yong Jiang, Nguyen Bach, Tao Wang, Zhongqiang Huang, Fei Huang, Kewei Tu. Proceedings of the 2020 Conference on Empirical Methods in Natural... sequence labeling https://openreview.net/forum?id=QUaKP7557s Non-approximate Inference for Collective Graphical Models on Path Graphs via Discrete Difference of... We propose a new MAP inference algorithm without Stirling's approximation and continuous relaxation for Collective Graphical Models on path graphs. https://openreview.net/forum?id=nbsaJUjHQl Approximate Bayesian Inference via Bitstring Representations | OpenReview The machine learning community has recently put effort into quantized or low-precision arithmetics to scale large models. This paper proposes performing... bayesian inference approximate via bitstring representations https://jmlr.org/papers/v22/20-1009.html Tractable Approximate Gaussian Inference for Bayesian Neural Networks tractable approximate gaussian inference bayesian