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Natural-Gradient Variational Inference 2: ImageNet-scale · Cambridge…
https://mlg.eng.cam.ac.uk/blog/2021/11/24/ngvi-bnns-part-2.html12 Apr 2024: Published on 24 November 2021. This is the blog of the Cambridge Machine Learning Group. -
Junction Tree, BP and Variational Methods
https://mlg.eng.cam.ac.uk/adrian/2018-MLSALT4-AW3-approx.pdf16 Jul 2024: 24 / 32. The log-partition function log Z. Since D(q‖p) 0, we have. ... a finite convex hull 3. M(G) = conv {φ(e), e X m} (5.24). -
Understanding the Bethe Approximation: When and How can it ...
https://mlg.eng.cam.ac.uk/adrian/abc.pdf16 Jul 2024: FA8750-14-C-0005. 2 8 16 24 320. 20. 40. 60. 80. 100. ... Maximum coupling strength y. BethelocalBethecycleBethemargTRWlocalTRWcycleTRWmarg. (a) log partition error. 2 8 16 24 320. -
Transparency: Motivations and Challenges? Adrian…
https://mlg.eng.cam.ac.uk/adrian/transparency.pdf16 Jul 2024: Notions of fairness beyond equality, and the role ofrandomness in fairness, were recently explored [24, 74]. ... Advances in experimental social psychology 24, 319–359 (1991). 26. Hammond, R., Axelrod, R.: The evolution of ethnocentrism. -
Directed and Undirected Graphical Models
https://mlg.eng.cam.ac.uk/adrian/2018-MLSALT4-AW1-models.pdf16 Jul 2024: 23 / 26. Directed and undirected models are different. 24 / 26. ... Directed and undirected models are different. With <3 edges,. 24 / 26. -
Bounding the Integrality Distance ofLP Relaxations for Structured…
https://mlg.eng.cam.ac.uk/adrian/OPT2016_paper_3.pdf16 Jul 2024: 24)Using the same approach to upper-bound Equation 22, we assume, without loss of generality, that(δ(µI ) θ) µ̃L (δ(µI ) θ. ′) ... m. (25). Using Equation 25 to upper-bound 22, and Equation 24 to upper-bound 23, we have that. -
Scalingin a Hierar chical Unsupervised Network 1 Zoubin Ghahramani,2…
https://mlg.eng.cam.ac.uk/zoubin/papers/scaling.pdf27 Jan 2023: Eachof the 24 hiddenunits in the middle hiddenlayerwas connectedto 9 consecutive visible units from eacheye,i.e. ... e), 1-24-36(b,f),1-48-72(c,g), 1-72-108(d,h). -
Tightness of LP Relaxations for Almost Balanced Models Adrian ...
https://mlg.eng.cam.ac.uk/adrian/tricam.pdf16 Jul 2024: Tightness of LP Relaxations for Almost Balanced Models. Adrian Weller Mark Rowland David SontagUniversity of Cambridge University of Cambridge New York University. Abstract. Linear programming (LP) relaxations are widelyused to attempt to identify a -
Blind Justice: Fairness with Encrypted Sensitive Attributes
https://mlg.eng.cam.ac.uk/adrian/ICML18-BlindJustice.pdf16 Jul 2024: d features 51 62 7 24 23p sensitive attr. 1 1 7 1 1certification 802 ms 827 ms 288 ms 250 ms 765 mstraining 43 min 51 min 7 min 1 ... Bennett Capers, I. Blind justice. Yale Journal of Law &Humanities, 24:179, 2012. -
An introduction to Flow Matching · Cambridge MLG Blog
https://mlg.eng.cam.ac.uk/blog/2024/01/20/flow-matching.html12 Apr 2024: Figure 24: One-sided conditioning (Lipman et al., 2022). Figure 25: Two-sided conditioning (Tong et al., 2023). -
C:/Users/Adrian/Documents/GitHub/betheClean/docs/nb-UAI.dvi
https://mlg.eng.cam.ac.uk/adrian/nb-UAI.pdf16 Jul 2024: Approximating the Bethe Partition Function. Adrian WellerDepartment of Computer Science. Columbia UniversityNew York NY 10027. adrian@cs.columbia.edu. Tony JebaraDepartment of Computer Science. Columbia UniversityNew York NY 10027. jebara@cs.columbia -
3F3: Signal and Pattern Processing Lecture 5: Dimensionality…
https://mlg.eng.cam.ac.uk/teaching/3f3/1011/lect5.pdf19 Nov 2023: Dataset Data dim. Sample size MLE Regression Corr. dim.Swiss roll 3 1000 2.1(0.02) 1.8(0.03) 2.0(0.24)Faces 64 64 698 4.3 -
Evaluating and Aggregating Feature-based Model Explanations
https://mlg.eng.cam.ac.uk/adrian/IJCAI20_EvaluatingAndAggregating.pdf16 Jul 2024: µC(f, gSHAP) 1.94 0.26 1.36 0.36 2.33 0.23µC(f, gAVA) 1.93 0.24 1.24 0.32 2.61 0.29. -
Scalable Gaussian Process Structured Prediction for Grid Factor Graph …
https://mlg.eng.cam.ac.uk/pub/pdf/BraQuaNowGha14.pdf13 Feb 2023: 24.6. 24.7. 24.8. 24.9. 25.0err. or. rate. GPstruct. CRF LBMO bag. ... 2013. http://arxiv.org/abs/1307.3846. Breiman, Leo. Bagging predictors. Machine Learning, 24(2):123–140, 1996. Domke, Justin. -
Gauged Mini-Bucket Elimination for Approximate Inference Sungsoo Ahn…
https://mlg.eng.cam.ac.uk/adrian/Gauge_for_Holder_Inference.pdf16 Jul 2024: We call these methods collectively Z-invariant methods. See [23, 24, 25] for discussions ofthe differences and relations between these methods. ... 24] G. David Forney Jr and Pascal O. Vontobel. Par-tition functions of normal factor graphs. -
An Introduction to LP Relaxations for MAP Inference
https://mlg.eng.cam.ac.uk/adrian/2018-MLSALT4-AW2-LP.pdf16 Jul 2024: x3. x4. x5. x6. s. balanced almost balanced(attractive up to flipping) 24 / 41. -
Leader Stochastic Gradient Descent for DistributedTraining of Deep…
https://mlg.eng.cam.ac.uk/adrian/NeurIPS2019_LSGD_preprint.pdf16 Jul 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. -
Conditions Beyond Treewidth for Tightness of Higher-order LP…
https://mlg.eng.cam.ac.uk/adrian/conditions.pdf16 Jul 2024: Conditions Beyond Treewidth for Tightness of Higher-order LP Relaxations. Mark Rowland Aldo Pacchiano Adrian WellerUniversity of Cambridge UC Berkeley University of Cambridge. Abstract. Linear programming (LP) relaxations are a pop-ular method to -
erice.dvi
https://mlg.eng.cam.ac.uk/zoubin/papers/erice.pdf27 Jan 2023: d) Weights from the toplayer binary logistic unit to the 24 middle layer binary logistic units. ... a) Weights from the top layer linear-Gaussian unit tothe 24 middle layer linear-Gaussian units. -
Gaussian Process
https://mlg.eng.cam.ac.uk/teaching/4f13/2122/gaussian%20process.pdf19 Nov 2023: 64. 20. 24. 6. 6. 4. 2. 0. 2. 4. 6.
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