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Uprooting and Rerooting Higher-Order GraphicalModels Mark…
https://mlg.eng.cam.ac.uk/adrian/uprooting-higher-order.pdf16 Jul 2024: 4], which relates to generalized belief propagation,24) and MAP inference (using loopy belief propagation, LBP [9]). ... InArtificial Intelligence and Statistics (AISTATS), 2016. [24] J. Yedidia, W. Freeman, and Y. -
Working Draft 1 Accountability of AI Under the Law: ...
https://mlg.eng.cam.ac.uk/adrian/SSRN-id3064761-Dec19.pdf16 Jul 2024: 24. Furthermore, an explanation must also provide the correct type of information in order for it to be useful. ... 24 Wachter, Right to Explanation, supra note 18. For a discussion about legibility of algorithmic systems more broadly, see Gianclaudio -
Clamping Improves TRW and Mean Field Approximations Adrian Weller∗ ...
https://mlg.eng.cam.ac.uk/adrian/clamp_aistats_final.pdf16 Jul 2024: Journal of Automated Reasoning, 24(1-2):225–275, 2000. N. Ruozzi. The Bethe partition function of log-supermodular graphical models. -
What Keeps a Bayesian Awake At Night? Part 2: Night Time · Cambridge…
https://mlg.eng.cam.ac.uk/blog/2021/03/31/what-keeps-a-bayesian-awake-at-night-part-2.html12 Apr 2024: Paris (1994, p. 24): “[W]hen an attempt is made to fill in all the details some of the attractiveness of the original is lost.”. -
You Shouldn’t Trust Me: Learning Models WhichConceal Unfairness From…
https://mlg.eng.cam.ac.uk/adrian/ECAI20-You_Shouldn%E2%80%99t_Trust_Me.pdf16 Jul 2024: mostimportant feature) in 2D reduced input space (scikit-learn [24]’s PCA imple-mentation). ... 4765–4774, (2017). [24] F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. -
From Parity to Preference-based Notionsof Fairness in Classification…
https://mlg.eng.cam.ac.uk/adrian/NeurIPS17-from-parity-to-preference.pdf16 Jul 2024: decisionoutcomes. A number of learning mechanisms have been proposed to achieve parity in treatment [24],. ... Angwin. https://github.com/propublica/compas-analysis, 2016. [24] B. T. Luong, S. Ruggieri, and F. -
Methods for Inference in Graphical Models
https://mlg.eng.cam.ac.uk/adrian/phd_FINAL.pdf16 Jul 2024: 3 Additional Background 24. 3.1 MAP Inference and Tractable Cases. 24. -
The Unreasonable Effectiveness of StructuredRandom Orthogonal…
https://mlg.eng.cam.ac.uk/adrian/NeurIPS17-unreasonable-effectiveness.pdf16 Jul 2024: sin2(φ) cos2(ϕ1) cos2(ϕ2) sin2(ϕ1)cos2(ϕ2) sin. 2(ϕ1) sin2(φ) cos2(ϕ1). (24). This observation greatly simplifies the integral from Equation (22) involving the term -
https://mlg.eng.cam.ac.uk/blog/feed.xml
https://mlg.eng.cam.ac.uk/blog/feed.xml12 Apr 2024: Jekyll 2024-04-12T16:32:5900:00 https://mlg.eng.cam.ac.uk/blog/feed.xml MLG Blog Blog of the Machine Learning Group at the University of Cambridge An introduction to Flow Matching 2024-01-20T00:00:0000:00 2024-01-20T00:00:0000:00 -
Ode to an ODE Krzysztof Choromanski ∗Robotics at Google ...
https://mlg.eng.cam.ac.uk/adrian/NeurIPS20-ODEtoODE.pdf16 Jul 2024: MIT Press, 2016. [24] Aditya Grover, Eric Wang, Aaron Zweig, and Stefano Ermon. ... In Proceedings of the 33nd International Conference on Machine Learning,ICML 2016, New York City, NY, USA, June 19-24, 2016, pages 2034–2042, 2016.
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