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1 - 10 of 18 search results for Economics middle test |u:mlg.eng.cam.ac.uk where 2 match all words and 16 match some words.

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  2. Orthogonal Estimation of Wasserstein Distances Mark Rowland∗1 Jiri…

    https://mlg.eng.cam.ac.uk/adrian/AISTATS19-slicedwasserstein.pdf
    16 May 2024: physics (Jordan et al., 1998) and economics(Galichon, 2016), and are increasingly used in machinelearning (Arjovsky et al., 2017; Gulrajani et al., 2017;Peyré and Cuturi, 2018). ... 5.1 Distance estimation. We begin with a test bed of small-scale

  3. Methods for Inference in Graphical Models

    https://mlg.eng.cam.ac.uk/adrian/phd_FINAL.pdf
    16 May 2024: 91. 7.6.2 Test sets. 93. 7.7 Conclusions. 95. 8 Clamping Variables and Approximate Inference 96. ... 7.2 Log partition function and approximations for ABC triangle. 86. 7.3 Bethe free energy ESB with stationary points highlighted (top), then entropy SB

  4. Results that match 2 of 3 words

  5. Discovering Interpretable Representations for Both Deep Generative…

    https://mlg.eng.cam.ac.uk/adrian/ICML18-Discovering.pdf
    16 May 2024: Significant re-sults are identified using a paired t-test with p = 0.05. ... The SVHN dataset contains 73,257training digits (instances) and 26,032 test digits.

  6. TibGM: A Transferable and Information-Based Graphical Model Approach…

    https://mlg.eng.cam.ac.uk/adrian/ICML2019-TibGM.pdf
    16 May 2024: Thick linesin the middle of each curve indicate the average performance,while the standard deviations over 50 random seeds are shownby the shaded regions. ... Significance is tested usingthe same paired t-test described above. TibGM ProMP

  7. From Parity to Preference-based Notionsof Fairness in Classification…

    https://mlg.eng.cam.ac.uk/adrian/NeurIPS17-from-parity-to-preference.pdf
    16 May 2024: In this paper, we draw inspiration from the fair-division and envy-freeness literature in economics and game theory and proposepreference-based notions of fairness—given the choice between various sets ... Finally,we train the five classifiers

  8. Adrian Weller

    https://mlg.eng.cam.ac.uk/adrian/
    16 May 2024: Adrian serves on the boards of several organizations. He is a member of the World Economic Forum Global Future Council on the Future of AI, and is co-director of the ... Train and test tightness of LP relaxations in structured prediction.

  9. Transparency: Motivations and Challenges? Adrian…

    https://mlg.eng.cam.ac.uk/adrian/transparency.pdf
    16 May 2024: truth.”. Defining criteria and tests for practical faithfulness are important open pro-blems. ... 53. Prat, A.: The wrong kind of transparency. American Economic Review 95(3),862–877 (2005).

  10. Exploring Properties of the Deep Image Prior Andreas…

    https://mlg.eng.cam.ac.uk/adrian/NeurIPS_2019_DIP7.pdf
    16 May 2024: To test the nature of these outputs we introduce a novel saliencymap approach, termed MIG-SG. ... To test this, we evaluated the sensitivity of DIP to changes innetwork architecture.

  11. Understanding the Bethe Approximation: When and How can it ...

    https://mlg.eng.cam.ac.uk/adrian/abc.pdf
    16 May 2024: Ex-periments are described in 6, where we examine test cases.Conclusions are discussed in 7. ... Given this performance, we used FW for all Bethe opti-mizations on the test cases.

  12. The Geometry of Random Features Krzysztof Choromanski∗1 Mark…

    https://mlg.eng.cam.ac.uk/adrian/geometry.pdf
    16 May 2024: On the left: Gaussian kernel, inthe middle: kernel defined by the PD function φ(‖z‖) =(1 ‖z‖2. ... pre-dictive distribution obtained by an exactly-trained GP, and(ii) predictive RMSE on test sets.

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