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21 - 30 of 45 search results for Economics test |u:mlg.eng.cam.ac.uk where 12 match all words and 33 match some words.

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  2. Structured Prediction Models for Chord Transcription of Music Audioā€¦

    https://mlg.eng.cam.ac.uk/adrian/icmla09adrian.pdf
    19 Jun 2024: Table 2 shows p-values for paired t-tests examining outperformance of each model compared to the baseline HMMv approach. ... Figure 2 shows the TOM test accuracies for all the models trained using Hamming distance as before.

  3. ML-IRL: Machine Learning in Real Life Workshop at ICLR ...

    https://mlg.eng.cam.ac.uk/adrian/ML_IRL_2020-CLUE.pdf
    19 Jun 2024: We evaluate the test set of fours, sevens, and nines with our BNN. ... This group was able to reach an accuracyof 88% on unseen test points.

  4. ML-IRL: Machine Learning in Real Life Workshop at ICLR ...

    https://mlg.eng.cam.ac.uk/adrian/ML_IRL_2020-Counterfactual_Accuracy.pdf
    19 Jun 2024: would we have to give up so that the predictionfor the test point would change? ... 2017)), and then we constrain fora random test point to obtain counterfactual accuracy.

  5. Now You See Me (CME): Concept-based Model Extraction

    https://mlg.eng.cam.ac.uk/adrian/AIMLAI20-CME.pdf
    19 Jun 2024: For everyš‘“ , we evaluated its fidelity and its task performance,using a held-out sample test set. ... 96.4 0.5%on a held-out test set (averaged over 5 runs).

  6. Orthogonal estimation of Wasserstein distances Mark Rowland*, Jiriā€¦

    https://mlg.eng.cam.ac.uk/adrian/slicedwasserstein_poster.pdf
    19 Jun 2024: Naturally incorporate spatial information. ā€¢ Applications from economics to machine learning.

  7. Evaluating and Aggregating Feature-based Model Explanations

    https://mlg.eng.cam.ac.uk/adrian/IJCAI20_EvaluatingAndAggregating.pdf
    19 Jun 2024: For Iris [Dua and Graff, 2017], we train our modelto 96% test accuracy. ... 45). Table 2: Faithfulness ĀµF averaged over a test set: (Zero Baseline,Training Average Baseline).

  8. The Geometry of Random Features Krzysztof Choromanskiāˆ—1 Markā€¦

    https://mlg.eng.cam.ac.uk/adrian/geometry.pdf
    19 Jun 2024: pre-dictive distribution obtained by an exactly-trained GP, and(ii) predictive RMSE on test sets. ... Figure 8: Approximate GP regression results on Bostondataset. Reported numbers are average test RMSE, alongwith bootstrap estimates of standard error

  9. Leader Stochastic Gradient Descent for DistributedTraining of Deepā€¦

    https://mlg.eng.cam.ac.uk/adrian/NeurIPS2019_LSGD_preprint.pdf
    19 Jun 2024: Test error for the center variableversus wall-clock time (original plot on the left and zoomed onthe right). ... Test loss is reported in Figure 13 in the Supplement. Finally, in Figure 6 we report theempirical results for ResNet50run on ImageNet.

  10. What Keeps a Bayesian Awake At Night? Part 2: Night Time Ā· Cambridgeā€¦

    https://mlg.eng.cam.ac.uk/blog/2021/03/31/what-keeps-a-bayesian-awake-at-night-part-2.html
    12 Apr 2024: Practitioners should also test the hell out of their inference schemes to gain confidence in them. ... The acid test is whether your inference scheme works on the real world data you care about, so test cases also need to replicate aspects of this

  11. Bounding the Integrality Distance ofLP Relaxations for Structuredā€¦

    https://mlg.eng.cam.ac.uk/adrian/OPT2016_paper_3.pdf
    19 Jun 2024: Thus, the more training data we have, the better we can estimate theexpected integrality distance at test time.Remark 1. ... 9] O. Meshi, M. Mahdavi, A. Weller, and D. Sontag. Train and test tightness of LP relaxations instructured prediction.

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