PUBLICATIONS

PUBLICATIONS

Need for AI in Transformer Diagnostics and Prognostics

Sanja Cvijic1, Nidhi Gupta1, Scott Lux2 Proceedings of The 69th Annual Reliability & Maintainability Symposium (RAMS®) (January 2023) Power equipment, such as electrical transformers, undergo performance degradation over time that can prevent them from functioning properly in critical situations or

Probabilistic Programming: Past, Present, and Future

Pfeffer, A. Invited keynote at the 22nd International Conference on Information Fusion (Fusion 2019), Ottawa, Canada (July 2019)  Overview: What is Probabilistic Programming? Probabilistic Programming in Action Probabilistic Programming Inference Algorithms Probabilistic Programming for Long-Lived AI Systems   Download Slides For

Structured Factored Inference for Probabilistic Programming

Pfeffer, A., Ruttenberg, B., Kretschmer, W., and O’Connor, A. Presented at the 21st International Conference on Artificial Intelligence and Statistics (AISTATS 2018), Lanzarote, Canary Islands (April 2018) Probabilistic reasoning on complex real-world models is computationally challenging. Inference algorithms have been

Scruff: A Deep Probabilistic Cognitive Architecture

Pfeffer, A. Invited talk at the Association for the Advancement of Artificial Intelligence’s Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-18), New Orleans, LA (February 2018). Probabilistic programming is able to build rich models of systems that combine prior knowledge with

Using Reinforcement Learning for Probabilistic Program Inference

Pfeffer, A. Extended abstract for the Probabilistic Program Semantics Workshop associated with the Principles of Programming Languages (POPL) conference, Los Angeles, CA (January 2018). Inference in probabilistic programming often involves choosing between different methods. For example, one could use different

Practical Probabilistic Programming

Pfeffer, A. Practical Probabilistic Programming, Manning Publications, Cherry Hill, NJ (2016) Practical Probabilistic Programming introduces the working programmer to probabilistic programming. In it, you’ll learn how to use the PP paradigm to model application domains and then express those probabilistic models in

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Need for AI in Transformer Diagnostics and Prognostics

Sanja Cvijic1, Nidhi Gupta1, Scott Lux2 Proceedings of The 69th Annual Reliability & Maintainability Symposium (RAMS®) (January 2023) Power equipment, such as electrical transformers, undergo performance degradation over time that can prevent them from functioning properly in critical situations or

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Probabilistic Programming: Past, Present, and Future

Pfeffer, A. Invited keynote at the 22nd International Conference on Information Fusion (Fusion 2019), Ottawa, Canada (July 2019)  Overview: What is Probabilistic Programming? Probabilistic Programming in Action Probabilistic Programming Inference Algorithms Probabilistic Programming for Long-Lived AI Systems   Download Slides For

Structured Factored Inference for Probabilistic Programming

Pfeffer, A., Ruttenberg, B., Kretschmer, W., and O’Connor, A. Presented at the 21st International Conference on Artificial Intelligence and Statistics (AISTATS 2018), Lanzarote, Canary Islands (April 2018) Probabilistic reasoning on complex real-world models is computationally challenging. Inference algorithms have been

Scruff: A Deep Probabilistic Cognitive Architecture

Pfeffer, A. Invited talk at the Association for the Advancement of Artificial Intelligence’s Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-18), New Orleans, LA (February 2018). Probabilistic programming is able to build rich models of systems that combine prior knowledge with

Using Reinforcement Learning for Probabilistic Program Inference

Pfeffer, A. Extended abstract for the Probabilistic Program Semantics Workshop associated with the Principles of Programming Languages (POPL) conference, Los Angeles, CA (January 2018). Inference in probabilistic programming often involves choosing between different methods. For example, one could use different

Practical Probabilistic Programming

Pfeffer, A. Practical Probabilistic Programming, Manning Publications, Cherry Hill, NJ (2016) Practical Probabilistic Programming introduces the working programmer to probabilistic programming. In it, you’ll learn how to use the PP paradigm to model application domains and then express those probabilistic models in

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