Results 121 to 130 of about 5,051,769 (292)
Variational Sequential Monte Carlo
, 2018 Many recent advances in large scale probabilistic inference rely on variational methods. The success of variational approaches depends on (i) formulating a flexible parametric family of distributions, and (ii) optimizing the parameters to find the member
Blei, David M. +3 more
core
Impact of Asymptomatic Intracranial Hemorrhage on Outcome After Endovascular Stroke Treatment
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Background Endovascular treatment (EVT) achieves high rates of recanalization in acute large‐vessel occlusion (LVO) stroke, but functional recovery remains heterogeneous. While symptomatic intracranial hemorrhage (sICH) has been well studied, the prognostic impact of asymptomatic intracranial hemorrhage (aICH) after EVT is less certain ...
Shihai Yang +22 more
wiley +1 more source
Methods in Ecology and EvolutionDifferential equation models are powerful tools for predicting biological systems, capable of projecting far into the future and incorporating data recorded at arbitrary times.
Maria Tirronen, Anna Kuparinen
doaj +1 more source
Unbiased Implicit Variational Inference
CoRR, 2018 9 pages, 3 ...
Michalis K. Titsias +1 more
openaire +3 more sources
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective Cognitive decline is a disabling and variable feature of Parkinson disease (PD). While cholinergic system degeneration is linked to cognitive impairments in PD, most prior research reported cross‐sectional associations. We aimed to fill this gap by investigating whether baseline regional cerebral vesicular acetylcholine transporter ...
Taylor Brown +6 more
wiley +1 more source
Automatic Differentiation Variational Inference
, 2016 Probabilistic modeling is iterative. A scientist posits a simple model, fits it to her data, refines it according to her analysis, and repeats. However, fitting complex models to large data is a bottleneck in this process.
Blei, David M. +4 more
core
Distribution Matching in Variational Inference
CoRR, 2018 With the increasingly widespread deployment of generative models, there is a mounting need for a deeper understanding of their behaviors and limitations. In this paper, we expose the limitations of Variational Autoencoders (VAEs), which consistently fail to learn marginal distributions in both latent and visible spaces. We show this to be a consequence
Mihaela Rosca +2 more
openaire +2 more sources
Epilepsy‐Associated Variants of a Single SCN1A Codon Exhibit Divergent Functional Properties
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective Pathogenic variants in SCN1A, which encodes the voltage‐gated sodium channel NaV1.1, are associated with multiple epilepsy syndromes exhibiting a range of clinical severity. SCN1A variants are reported in different syndromes, including Dravet syndrome, which is associated with loss‐of‐function, whereas neonatal/infantile‐onset ...
Lanie N. Liebovitz +3 more
wiley +1 more source
MaxEntropy Pursuit Variational Inference [PDF]
, 2019 One of the core problems in variational inference is a choice of approximate posterior distribution. It is crucial to trade-off between efficient inference with simple families as mean-field models and accuracy of inference. We propose a variant of a greedy approximation of the posterior distribution with tractable base learners.
Evgenii Egorov +3 more
openaire +2 more sources
Ketogenic Diet as an Epigenetic Therapy in SETD1B‐Related Epilepsy
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Histone lysine methyltransferases such as SETD1B regulate chromatin structure and gene transcription. Ketone bodies, including butyrate, act as histone deacetylase inhibitors. We report a 4‐year‐old boy with SETD1B‐related absence epilepsy, refractory to conventional medications, who achieved sustained > 90% seizure reduction on the Modified ...
Erica Tsang +10 more
wiley +1 more source