John Paisley - RankMe

26 papers A* 2A 2B 2C 1Journal 15Unranked 4

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	A Minimum Variance Path Principle for Accurate and Stable Score-Based Density Ratio Estimation. CoRR Wei Chen, Jiacheng Li, Shigui Li, Zhiqi Lin, Junmei Yang, John Paisley, Delu Zeng
2026	J	`jnl`	Entropy-informed weighting channel normalizing flow for deep generative models. Pattern Recognit. Wei Chen, Shian Du, Shigui Li, Delu Zeng, John Paisley
2026	J	`jnl`	Tabular diffusion counterfactual explanations. Frontiers Artif. Intell. Wei Zhang, Brian Barr, John Paisley
2025	J	`jnl`	An Explainable Gaussian Process Auto-encoder for Tabular Data. CoRR Wei Zhang, Brian Barr, John Paisley
2025	J	`jnl`	Any-Step Density Ratio Estimation via Interval-Annealed Secant Alignment. CoRR Wei Chen, Shigui Li, Jiacheng Li, Jian Xu, Zhiqi Lin, Junmei Yang, Delu Zeng, John Paisley, Qibin Zhao
2025	A	`conf`	Bayesian Gaussian Process ODEs via Double Normalizing Flows. AISTATS Jian Xu, Shian Du, Junmei Yang, Xinghao Ding, Delu Zeng, John Paisley
2025	J	`jnl`	Consist-Retinex: One-Step Noise-Emphasized Consistency Training Accelerates High-Quality Retinex Enhancement. CoRR Jian Xu, Wei Chen, Shigui Li, Delu Zeng, John Paisley, Qibin Zhao
2025	A*	`conf`	Dequantified Diffusion-Schrödinger Bridge for Density Ratio Estimation. ICML Wei Chen, Shigui Li, Jiacheng Li, Junmei Yang, John Paisley, Delu Zeng
2025	J	`jnl`	Dequantified Diffusion-Schrödinger Bridge for Density Ratio Estimation. CoRR Wei Chen, Shigui Li, Jiacheng Li, Junmei Yang, John Paisley, Delu Zeng
2025	J	`jnl`	Diffusion Bridge Variational Inference for Deep Gaussian Processes. CoRR Jian Xu, Qibin Zhao, John Paisley, Delu Zeng
2025	B	`conf`	Gaussian Process Tilted Nonparametric Density Estimation Using Fisher Divergence Score Matching. IEEE Big Data John Paisley, Wei Zhang, Brian Barr
2025	J	`jnl`	Gaussian Process Tilted Nonparametric Density Estimation using Fisher Divergence Score Matching. CoRR John Paisley, Wei Zhang, Brian Barr
2025	J	`jnl`	Neural Bridge Processes. CoRR Jian Xu, Yican Liu, Qibin Zhao, John Paisley, Delu Zeng
2025	B	`conf`	Stochastic Variational Inference with Tuneable Stochastic Annealing. IEEE Big Data John Paisley, Ghazal Fazelnia, Brian Barr
2025	J	`jnl`	Stochastic Variational Inference with Tuneable Stochastic Annealing. CoRR John Paisley, Ghazal Fazelnia, Brian Barr
2025	J	`jnl`	Tabular Diffusion Counterfactual Explanations. CoRR Wei Zhang, Brian Barr, John Paisley
2025	A	`conf`	Variational Learning of Gaussian Process Latent Variable Models through Stochastic Gradient Annealed Importance Sampling. UAI Jian Xu, Shian Du, Junmei Yang, Qianli Ma, Delu Zeng, John Paisley
2024	A*	`conf`	Gaussian Process Neural Additive Models. AAAI Wei Zhang, Brian Barr, John Paisley
2024	J	`jnl`	Gaussian Process Neural Additive Models. CoRR Wei Zhang, Brian Barr, John Paisley
2024	C	`conf`	Gaussian Process Neural Network Embeddings for Collaborative Filtering. ICMLA Wei Zhang, Brian Barr, John Paisley
2024	—	`conf`	Nonlinear Kalman Filtering with Reparametrization Gradients. ICFSP San Gultekin, Brendan Kitts, Aaron Flores, John Paisley
2023	J	`jnl`	Bayesian Beta-Bernoulli Process Sparse Coding with Deep Neural Networks. CoRR Arunesh Mittal, Kai Yang, Paul Sajda, John Paisley
2022	—	`conf`	An Interpretable Deep Classifier for Counterfactual Generation. ICAIF Wei Zhang, Brian Barr, John Paisley
2022	—	`conf`	Deep Metric Representation Learning for Clinical Resting State fMRI. EMBC Arunesh Mittal, John Paisley, Paul Sajda
2022	—	`conf`	Understanding Counterfactual Generation using Maximum Mean Discrepancy. ICAIF Wei Zhang, Brian Barr, John Paisley
2021	J	`jnl`	Scalable Community Detection in Massive Networks Using Aggregated Relational Data. CoRR Timothy Jones, Yiran Jiang, John Paisley, Tian Zheng