John P. Cunningham
30 papers · 2007–2024 · 2 conferences · across top CS/AI conferences
Achievements
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π§ Keyword Pioneer π Interdisciplinary Bridge π Renaissance Researcher (5) πΊοΈ Taxonomy Completionist (12) π Conference Polyglot (2)
π
Interdisciplinary Bridge
π
Conference Polyglot
(2)
π§
Keyword Pioneer
π
Conference Loyalist
(27)
π
Keyword Champion
ποΈ
Keyword Collector
(157)
π
Trend Setter
π
Century Club
(30)
π₯
Unstoppable
(6)
β
The Questioner
Conferences
NIPS (27)
JMLR (3)
Top co-authors
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