Showing 1–20 of 73 results
/ Date/ Name
May 28, 2018Discrete flow posteriors for variational inference in discrete dynamical systemsJul 26, 2014Zipf's law arises naturally in structured, high-dimensional dataMay 18, 2015Synaptic sampling: A connection between PSP variability and uncertainty explains neurophysiological observationsFeb 20, 2024Bayesian Reward Models for LLM AlignmentJul 3, 2014The Hamiltonian brain: efficient probabilistic inference with excitatory-inhibitory neural circuit dynamicsFeb 25, 2025Jacobian Sparse Autoencoders: Sparsify Computations, Not Just ActivationsJul 17, 2024Questionable practices in machine learningMay 17, 2020Global inducing point variational posteriors for Bayesian neural networks and deep Gaussian processesJun 22, 2018Tensor Monte Carlo: particle methods for the GPU eraSep 18, 2023Convolutional Deep Kernel MachinesFeb 29, 2024Batch size invariant AdamMay 23, 2023An Improved Variational Approximate Posterior for the Deep Wishart ProcessSep 29, 2023LoRA ensembles for large language model fine-tuningMar 11, 2025Massively Parallel Expectation Maximization For Approximate PosteriorsJun 26, 2025Learning to Skip the Middle Layers of TransformersJul 24, 2018Sampling-based probabilistic inference emerges from learning in neural circuits with a cost on reliabilityJun 10, 2021Data augmentation in Bayesian neural networks and the cold posterior effectFeb 27, 2023Taylor TD-learningNov 29, 2022Machine learning emulation of a local-scale UK climate modelJun 24, 2022Robustness to corruption in pre-trained Bayesian neural networks