Conor Durkan

I’m currently based in New York, and working on something new. I was previously a senior research scientist at DeepMind in London, and before that I completed my PhD at the University of Edinburgh. My primary interest is in generative modeling, but I’ve also done some work on likelihood-free inference.

Generative modeling

Imagen 2.0
[Blog]

Lyria
[Blog]

Reduce, Reuse, Recycle: Compositional Generation with Energy-Based Diffusion Models and MCMC
Yilun Du, Conor Durkan, Robin Strudel, Joshua B Tenenbaum, Sander Dieleman, Rob Fergus, Jascha Sohl-Dickstein, Arnaud Doucet, Will Grathwohl
International Conference on Machine Learning, 2023
[arXiv]

Continuous Diffusion for Categorical Data
Sander Dieleman, Laurent Sartran, Arman Roshannai, Nikolay Savinov, Yaroslav Ganin, Pierre H. Richemond, Arnaud Doucet, Robin Strudel, Chris Dyer, Conor Durkan, Curtis Hawthorne, Rémi Reblond, Will Grathwohl, Jonas Adler
Pre-print, 2022
[arXiv]

Maximum Likelihood Training of Score-Based Diffusion Models
Yang Song, Conor Durkan, Iain Murray, Stefano Ermon
Advances in Neural Information Processing Systems (Spotlight), 2021
[arXiv] [GitHub]

Neural Spline Flows
Conor Durkan, Artur Bekasov, Iain Murray, George Papamakarios
Advances in Neural Information Processing Systems, 2019
[arXiv] [GitHub]

Cubic-Spline Flows
Conor Durkan, Artur Bekasov, Iain Murray, George Papamakarios
1st workshop on Invertible Neural Networks and Normalizing Flows (ICML), 2019
[arXiv] [GitHub]

Autoregressive Energy Machines
Charlie Nash, Conor Durkan
International Conference on Machine Learning [Oral], 2019
[arXiv] [GitHub]

Likelihood-free inference

On Contrastive Learning for Likelihood-free Inference
Conor Durkan, Iain Murray, George Papamakarios
International Conference on Machine Learning, 2020
[arXiv] [GitHub]

Sequential Neural Methods for Likelihood-free Inference
Conor Durkan, George Papamakarios, Iain Murray
3rd workshop on Bayesian Deep Learning (NeurIPS). 2018
[arXiv]

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