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John P. Cunningham

30 papers · 2007–2024 · 2 conferences · across top CS/AI conferences

Achievements

πŸƒ Academic Marathon (17) 🌈 Renaissance Researcher (5) 🐣 Hot Topic Early Bird 🐝 Cross-Pollinator (15) πŸ—ΊοΈ Taxonomy Completionist (12) πŸŒ‰ Interdisciplinary Bridge 🌍 Conference Polyglot (2) 🧭 Keyword Pioneer 🏠 Conference Loyalist (27) πŸ† Keyword Champion πŸ—ƒοΈ Keyword Collector (157) πŸ“ˆ Trend Setter πŸ’Ž Century Club (30) πŸ”₯ Unstoppable (6) ❓ The Questioner

Conferences

NIPS (27) JMLR (3)

Top co-authors

Liam Paninski (7) Geoff Pleiss (6) Krishna V. Shenoy (5) Maneesh Sahani (4) Gabriel Loaiza-Ganem (4) Byron M. Yu (4) Matthew Whiteway (3) Stephen I. Ryu (2) Scott Linderman (2) Taiga Abe (2)

Keywords

variational inference (12) gaussian process (10) neural population (5) variational autoencoder (4) bayesian inference (4) dimensionality reduction (3) latent variable model (3) uncertainty quantification (3) linear dynamical system (3) normalizing flow (2) model selection (2) latent variable (2) posterior approximation (2) generalized linear model (2) factor analysis (2) generative model (2) hierarchical model (2) point process (2) spiking activity (2) calcium imaging (2)

Papers

Estimating the Hallucination Rate of Generative AI NIPS 2024
Computation-Aware Gaussian Processes: Model Selection And Linear-Time Inference NIPS 2024
Approximation-Aware Bayesian Optimization NIPS 2024
Practical and Asymptotically Exact Conditional Sampling in Diffusion Models NIPS 2023
Data Augmentation for Compositional Data: Advancing Predictive Models of the Microbiome NIPS 2022
Deep Ensembles Work, But Are They Necessary? NIPS 2022
Posterior and Computational Uncertainty in Gaussian Processes NIPS 2022
Posterior Collapse and Latent Variable Non-identifiability NIPS 2021
A general linear-time inference method for Gaussian Processes on one dimension JMLR 2021
Rectangular Flows for Manifold Learning NIPS 2021
The Limitations of Large Width in Neural Networks: A Deep Gaussian Process Perspective NIPS 2021
Invertible Gaussian Reparameterization: Revisiting the Gumbel-Softmax NIPS 2020
Deep Graph Pose: a semi-supervised deep graphical model for improved animal pose tracking NIPS 2020
Recurrent Switching Dynamical Systems Models for Multiple Interacting Neural Populations NIPS 2020
Expectation Propagation as a Way of Life: A Framework for Bayesian Inference on Partitioned Data JMLR 2020
The continuous Bernoulli: fixing a pervasive error in variational autoencoders NIPS 2019
BehaveNet: nonlinear embedding and Bayesian neural decoding of behavioral videos NIPS 2019
Deep Random Splines for Point Process Intensity Estimation of Neural Population Data NIPS 2019
Paraphrase Generation with Latent Bag of Words NIPS 2019
Automated scalable segmentation of neurons from multispectral images NIPS 2016
Linear dynamical neural population models through nonlinear embeddings NIPS 2016
Linear Dimensionality Reduction: Survey, Insights, and Generalizations JMLR 2015
Bayesian Active Model Selection with an Application to Automated Audiometry NIPS 2015
High-dimensional neural spike train analysis with generalized count linear dynamical systems NIPS 2015
Clustered factor analysis of multineuronal spike data NIPS 2014
Fast Kernel Learning for Multidimensional Pattern Extrapolation NIPS 2014
Empirical models of spiking in neural populations NIPS 2011
Dynamical segmentation of single trials from population neural data NIPS 2011
Gaussian-process factor analysis for low-dimensional single-trial analysis of neural population activity NIPS 2008
Inferring Neural Firing Rates from Spike Trains Using Gaussian Processes NIPS 2007