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Nonparametric Bayesian Sparse Factor Models with application to Gene…
https://mlg.eng.cam.ac.uk/pub/pdf/KnoGha11b.pdf13 Feb 2023: 14 15 16 17 18 19 20 21 22 23 24 25 26 270. ... 2004) (N = 24,D = 100,3000 MCMC iterations). 5.3. Biological data: E. -
One-network Adversarial Fairness
https://mlg.eng.cam.ac.uk/adrian/AAAI2019_OneNetworkAdversarialFairness.pdf19 Jun 2024: log(1/δ). n(24). The term[4nI(x D0). 4nI(x D1). ]is what is estimated. ... From the latter note and (24), the two classifiers of theadversarial formulation proposed in (9) in the main docu-ment can be interpreted w.r.t. -
Bayesian Knowledge Corroboration with LogicalRules and User Feedback…
https://mlg.eng.cam.ac.uk/pub/pdf/KasVanGraHer10.pdf13 Feb 2023: While [23, 24] represent the formalism of first-orderlogic by factor graph models, [27] and [29] deal with Bayesian networks appliedto first-order logic. ... 1094–1099. AAAI Press(2008). 24. Domingos, P., Richardson, M.: Markov Logic Networks. -
1 Graph Kernels by Spectral Transforms Xiaojin Zhu Jaz ...
https://mlg.eng.cam.ac.uk/zoubin/papers/ssl-book.pdf27 Jan 2023: maxµ vec(T )>M µ (1.24). subject to ||M µ|| 1 (1.25). ... 0.27 (86) 0.24 (92) 0.15 0.18 0.40 (85) 0.02 0.12 0.09. -
newroyftp.dvi
https://mlg.eng.cam.ac.uk/zoubin/papers/RGBN.pdf27 Jan 2023: A factor analyzer with 24 hidden units discoversglobal features with both excitatory and inhibitory components (gure 9a). ... a) Weights from the top layer hidden unit to the 24 middle-layer hidden units. -
Structured Evolution with Compact Architectures for Scalable Policy…
https://mlg.eng.cam.ac.uk/adrian/structured_icml_full.pdf19 Jun 2024: Structured Evolution with Compact Architecturesfor Scalable Policy Optimization. Krzysztof Choromanski 1 Mark Rowland 2 Vikas Sindhwani 1 Richard E. Turner 2 Adrian Weller 2 3. AbstractWe present a new method of blackbox optimiza-tion via gradient -
1 Robust Filtering and Smoothing with Gaussian Processes Marc ...
https://mlg.eng.cam.ac.uk/pub/pdf/DeiTurHubetal12.pdf13 Feb 2023: Using the definition of S in (24), the productof the two Gaussians in (36) results in a new (unnormalized) Gaussianc14 N(xt1 |ψi, Ψ) with. ... 24] J. Kocijan, R. Murray-Smith, C. E. Rasmussen, and B. Likar, “PredictiveControl with Gaussian Process -
Leader Stochastic Gradient Descent for DistributedTraining of Deep…
https://mlg.eng.cam.ac.uk/adrian/NeurIPS2019_LSGD_preprint.pdf19 Jun 2024: Landscape symmetries are commonin a plethora of non-convex problems [18, 19, 20, 21, 22], including deep learning [23, 24, 25, 26]. ... Understanding symmetries in deep networks. CoRR,abs/1511.01029, 2015. [24] A. Choromanska, M. -
analogy-aistats2007.dvi
https://mlg.eng.cam.ac.uk/pub/pdf/SilHelGha07a.pdf13 Feb 2023: C1 C2 RB SB1 SB2 C1 C2 RB SB1 SB2. student course f aculty projectcornell 0.87 0.61 0.87 0.84 0.80 0.19 0.04 0.24 ... 0.18 0.18texas 0.55 0.54 0.77 0.62 0.48 0.24 0.07 0.29 0.07 0.12. -
Cambridge Machine Learning Group Publications
https://mlg.eng.cam.ac.uk/pub/authors/13 Feb 2023: Publications, Machine Learning Group, Department of Engineering, Cambridge. current group:. [former members:. [by year:. [Tameem Adel. George Nicholson, Marta Blangiardo, Mark Briers, Peter J Diggle, Tor Erlend Fjelde, Hong Ge, Robert J B Goudie, -
Predictive Automatic Relevance Determinationby Expectation…
https://mlg.eng.cam.ac.uk/pub/pdf/QiMinPic04a.pdf13 Feb 2023: PredictiveARDEPPredictiveProbARDEP. EvidenceARDEPAllFeaturesEPlinear. SVMQP_RegAdaBoostLP_RegAdaBoost. AdaBoost_RegAdaBoost. RBF. 23.5 24.5 25.5 26.5. Test error rate. -
Graph-based Semi-supervised Learning Zoubin Ghahramani Department of…
https://mlg.eng.cam.ac.uk/zoubin/talks/lect3ssl.pdf27 Jan 2023: Background Extraction. date 10/24 11/13 1/6 1/14 1/20 1/21 1/271 128 193 153 4742 256 193 4483 288 305 5934 204 190 3945 266 -
Marc P. Deisenroth, Jan Peters, and Carl E. Rasmussen: ...
https://mlg.eng.cam.ac.uk/pub/pdf/DeiPetRas08.pdf13 Feb 2023: 578 2.0108 9.23 6.2105 9.81012 9.02488 1.2108 9.24 3.1105 1.31012 9.05. -
Gaussian Process Change Point Models
https://mlg.eng.cam.ac.uk/pub/pdf/SaaTurRas10.pdf13 Feb 2023: In FinancialTimes. April 24 2009. Li, D. X. On default correlation: A copula function ap-proach. -
Geometrically Coupled Monte Carlo Sampling Mark Rowland∗University of …
https://mlg.eng.cam.ac.uk/adrian/NeurIPS18-gcmc.pdf19 Jun 2024: Geometrically Coupled Monte Carlo Sampling. Mark RowlandUniversity of Cambridgemr504@cam.ac.uk. Krzysztof ChoromanskiGoogle Brain Roboticskchoro@google.com. François ChalusUniversity of Cambridgechalusf3@gmail.com. Aldo PacchianoUniversity of -
Nonparametric Transforms of Graph Kernelsfor Semi-Supervised Learning …
https://mlg.eng.cam.ac.uk/zoubin/papers/ZhuKanGhaLaf04.pdf27 Jan 2023: 50.27 (86) 0.24 (92) 0.15 0.18 0.40 (85) 0.02 0.12 0.09. ... 0.27 (26) 0.13 (25) 0.03 0.11 0.31 (24) -0.89 -0.80 -0.65100 64.6 2.1 59.0 3.6 58.5 2.9 -
368 A kernel method for unsupervised structured network inference ...
https://mlg.eng.cam.ac.uk/pub/pdf/LipSteGhaetal09.pdf13 Feb 2023: The Countries data set contains in-formation on 24 countries, comprising 4 numeric at-tributes: population size, the GNP per capita, theaverage education level (as expressed by scalars), aswell as energy ... Countries trade data (24 nodes)population size -
A Unified Approach to Quantifying Algorithmic Unfairness: Measuring…
https://mlg.eng.cam.ac.uk/adrian/KDD2018_inequality_indices.pdf19 Jun 2024: 2.3 Axioms for Measuring InequalityBorrowing insights from the rich body of work on the axiomaticcharacterization of inequality indices in economics and social sci-ence [3, 10, 19, 24, 25, 28, -
Gaussian Process
https://mlg.eng.cam.ac.uk/teaching/4f13/1718/gaussian%20process.pdf19 Nov 2023: 64. 20. 24. 6. 6. 4. 2. 0. 2. 4. 6. -
- 4F13: Machine Learning
https://mlg.eng.cam.ac.uk/teaching/4f13/0910/lect01.pdf19 Nov 2023: Ghahramani & Rasmussen (CUED) Lecture 1: Introduction to Machine Learning January 14th, 2010 24 / 26.
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