572 citations · 577 across the 4 of their papers we have counts for
4 papers
Applying SVGD to Bayesian Neural Networks for Cyclical Time-Series Prediction and Inference
Xinyu Hu, Paul Szerlip, Theofanis Karaletsos +1
A regression-based BNN model is proposed to predict spatiotemporal quantities like hourly rider demand with calibrated uncertainties. The main contributions of this paper are (i) A…
Pyro: Deep Universal Probabilistic Programming
Eli Bingham, Jonathan P. Chen, Martin Jankowiak +7
Pyro is a probabilistic programming language built on Python as a platform for developing advanced probabilistic models in AI research. To scale to large datasets and high-dimensio…
A Proposed Infrastructure for Adding Online Interaction to Any Evolutionary Domain
Paul Szerlip, Kenneth O. Stanley
To address the difficulty of creating online collaborative evolutionary systems, this paper presents a new prototype library called Worldwide Infrastructure for Neuroevolution (WIN…
Unsupervised Feature Learning through Divergent Discriminative Feature Accumulation
Paul A. Szerlip, Gregory Morse, Justin K. Pugh +1
Unlike unsupervised approaches such as autoencoders that learn to reconstruct their inputs, this paper introduces an alternative approach to unsupervised feature learning called di…