Search

Search Funnelback University

Search powered by Funnelback
10 results 20 results 50 results
Relevance Date (Newest first) Date (Oldest first) Title (A-Z) Title (Z-A) Distance URL (A-Z) URL (Z-A) Shuffle
1 - 10 of 15 search results for KaKaoTalk:vb20 200 |u:mlg.eng.cam.ac.uk where 0 match all words and 15 match some words.

  1. Results that match 1 of 2 words

  2. preferential_fairness_nips_2017.pages

    https://mlg.eng.cam.ac.uk/adrian/preferential_fairness_nips_2017.pdf
    21 Mar 2024: 4. New notions of fairness. M (100). W (100)M (200) W (200). ... Benefit: 0% (M), 67% (W). M (100). W (100)M (200) W (200).

  3. Exploring Properties of the Deep Image Prior Andreas…

    https://mlg.eng.cam.ac.uk/adrian/NeurIPS_2019_DIP7.pdf
    21 Mar 2024: This is confirmed by the confidenceof the DIP output, which increased to > 0:5 after just 200 iterations. ... 2. (a) 100 iterationsConf.: 0.004. (b) 200 iterationsConf.: 0.52. (c) 300 iterationsConf.: 0.72.

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

    https://mlg.eng.cam.ac.uk/adrian/ML_IRL_2020-CLUE.pdf
    21 Mar 2024: MNIST 2 1200 6 -LSAT 2 200 3 300. COMPAS 2 200 3 300.

  5. Network Ranking With Bethe Pseudomarginals Kui TangColumbia…

    https://mlg.eng.cam.ac.uk/adrian/2013_NeurIPS_DiscML_Network.pdf
    21 Mar 2024: We drew independent nodescores from a mixture of Gaussians and a scale free network (100 nodes, 200 edges) from the Barabsi-Albertmodel [12].

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

    https://mlg.eng.cam.ac.uk/adrian/ICML2019-TibGM.pdf
    21 Mar 2024: 0 100 200 300 400 500. 103 steps0. 50. 100. 150. ... 200. 250. 300. 350. retu. rn. (a) Swimmer (rllab). 0 250 500 750 1000 1250 1500 1750 2000.

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

    https://mlg.eng.cam.ac.uk/adrian/NeurIPS17-from-parity-to-preference.pdf
    21 Mar 2024: W (100)M (200) W (200). f2. f1Acc: 0.83. Benefit: 0% (M), 67% (W). ... M (100). W (100)M (200) W (200). f2. f1Acc: 0.72. Benefit: 22% (M), 22% (W)Acc: 1.00.

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

    https://mlg.eng.cam.ac.uk/adrian/abc.pdf
    21 Mar 2024: Understanding the Bethe Approximation: When and How can it go Wrong? Adrian WellerColumbia UniversityNew York NY 10027. adrian@cs.columbia.edu. Kui TangColumbia UniversityNew York NY 10027. kt2384@cs.columbia.edu. David SontagNew York UniversityNew

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

    https://mlg.eng.cam.ac.uk/adrian/AIMLAI20-CME.pdf
    21 Mar 2024: This dataset consists of 11,788 im-ages of 200 bird species with every image annotatedusing 312 binary concept labels (e.g. ... 28] C. Wah, S. Branson, P. Welinder, P. Perona, S. Be-longie, The caltech-ucsd birds-200-2011 dataset(2011).

  10. 2018 Formatting Instructions for Authors Using LaTeX

    https://mlg.eng.cam.ac.uk/adrian/AIES18-crowd_signals.pdf
    21 Mar 2024: Weuse this set of 200 labeled tweets as our ground truth dataset.

  11. Beyond Distributive Fairness in Algorithmic Decision Making: Feature…

    https://mlg.eng.cam.ac.uk/adrian/AAAI18-BeyondDistributiveFairness.pdf
    21 Mar 2024: For a given dataset, we gather responses to the abovequestions from 200 different AMT workers (that is, each fea-ture is judged by 200 different workers).

Refine your results

clear all

Date

Search history

Recently clicked results

Recently clicked results

Your click history is empty.

Recent searches

Recent searches

Your search history is empty.