Theory and evaluation metrics for learning disentangled representations v2
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This document proposes a formal definition of disentangled representations and new evaluation metrics. It defines disentangled representations as having three key properties: informativeness, separability, and interpretability. Mathematical formulas are provided for these properties. Two new metrics are proposed to measure separability and interpretability in both supervised and unsupervised settings. The metrics aim to overcome limitations of prior work by not requiring knowledge of all ground truth factors or additional training. Experiments applying the new metrics to models are referenced but not described in detail.
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which categories data based on numerical or categorical variables. To classify the data many algorithms are
proposed, out of them five algorithms are comparatively studied for data mining through classification. There are
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Paper sharing_Explaining Data-Driven Decisions made by AI Systems_The Counter...
MIS QUARTERLY
Fernandez, Carlos & Provost, Foster & Han, Xintian
Presenter ：CHEN,YOU-SHENG
(Shane) 2022/12/15
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Hx3115011506
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لموعد الإثنين 03 يناير 2022 143 مبادرة #تواصل_تطوير المحاضرة ال 143 من المباد...
لموعد الإثنين 03 يناير 2022 143 مبادرة #تواصل_تطوير المحاضرة ال 143 من المباد...Egyptian Engineers Association
الموعد الإثنين 03 يناير 2022
143
مبادرة
#تواصل_تطوير
المحاضرة ال 143 من المبادرة
المهندس / محمد الرافعي طرباي
نقيب المبرمجين بالدقهلية
بعنوان
"IT INDUSTRY"
How To Getting Into IT With Zero Experience
وذلك يوم الإثنين 03 يناير2022
السابعة مساء توقيت القاهرة
الثامنة مساء توقيت مكة المكرمة
و الحضور من تطبيق زووم
https://us02web.zoom.us/meeting/register/tZUpf-GsrD4jH9N9AxO39J013c1D4bqJNTcu
علما ان هناك بث مباشر للمحاضرة على القنوات الخاصة بجمعية المهندسين المصريين
ونأمل أن نوفق في تقديم ما ينفع المهندس ومهمة الهندسة في عالمنا العربي
والله الموفق
للتواصل مع إدارة المبادرة عبر قناة التليجرام
https://t.me/EEAKSA
ومتابعة المبادرة والبث المباشر عبر نوافذنا المختلفة
رابط اللينكدان والمكتبة الالكترونية
https://www.linkedin.com/company/eeaksa-egyptian-engineers-association/
رابط قناة التويتر
https://twitter.com/eeaksa
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https://www.facebook.com/EEAKSA
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https://www.youtube.com/user/EEAchannal
رابط التسجيل العام للمحاضرات
https://forms.gle/vVmw7L187tiATRPw9
ملحوظة : توجد شهادات حضور مجانية لمن يسجل فى رابط التقيم اخر المحاضرةEnhancing a Social Science Model-building Workflow with Interactive Visualisa...
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This presentation summarizes how the presenter would analyze and present findings from 5 chat interviews conducted with a student regarding a university module's support in developing research skills. The presenter would collate the data by checking reliability, removing personal details, and transferring the data to a usable format. They would analyze the data by categorizing comments, carefully coding them, and potentially mapping relationships. Findings would be presented by establishing the research's validity and reliability, directly answering the research question, and extracting quotes to support conclusions. Both strengths like providing depth and weaknesses like potential bias are acknowledged.
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لموعد الإثنين 03 يناير 2022 143 مبادرة #تواصل_تطوير المحاضرة ال 143 من المباد...
لموعد الإثنين 03 يناير 2022 143 مبادرة #تواصل_تطوير المحاضرة ال 143 من المباد...
Enhancing a Social Science Model-building Workflow with Interactive Visualisa...
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Explainability in AI and Recommender systems: let’s make it interactive!
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Theory and evaluation metrics for learning disentangled representations v2
- 1. Theory and Evaluation Metrics for Learning Disentangled Representations Kien Do and Truyen Tran Applied AI Institute (A2I2), Deakin University, Australia 12020
- 2. Examples about disentangled representations 2 Overcoming the Disentanglement vs Reconstruction Trade-off via Jacobian Supervision (Lezama et. al., ICLR-2019)
- 3. Current challenges • The mathematic interpretation of disentangled representations is still not clear • Most evaluations metrics are heuristic and not robust 3
- 4. Our goals • Provide a mathematically formal definition of disentangled representations • Propose proper, theoretically driven metrics for fair evaluations 4
- 5. An informal definition Disentanglement: A process of decorrelating data into separate pieces of information, each of which corresponds to a predefined concept. This suggests 3 main properties of disentangled representations: • Informativeness: Large enough pieces of information • Separability: Pieces of information are not overlapped • Interpretability: Each piece of information corresponds to a concept 6
- 6. Mathematic formulas of these properties • The informativeness of is defined by: • are separable w.r.t. if: • are fully separable (independent) if: • is fully interpretable w.r.t. if: 7
- 7. Definition of disentangled representations 8
- 8. Compared to other related works • Higgins et. al., 2018 [1] attempted to define disentanglement based on group theory. Their definition shares similar observations as ours but is less convenient for probabilistic model design. • Eastwood et. al., 2018 [2] did NOT provide explicit definition of disentangled representations but characterized them along 3 dimensions: disentanglement, compactness, and informativeness. • The main drawback of disentanglement and compactness is that they require knowledge of ALL ground truth factors of variation. 9 [1] Towards a definition of disentangled representations (Higgins et. al., ICLR-2018) [2] A framework for the quantitative evaluation of disentangled representations (Eastwood et. al., ICLR-2018)
- 9. Our proposed metrics • Informativeness: • Separability + Informativeness (no labeled factors available): 10 where
- 10. Our proposed metrics (cont.) • Separability + Interpretability (labeled factors provided): where and is the highest and second highest MI values between every and . 11
- 11. Advantages of our metrics • Support both supervised/unsupervised models • Can be applied for real datasets • Do not require any training procedure • Provide consistent results across different models • Agree with visual results 12
- 12. Experiments and Results Please refer to our paper! 13
- 13. 14 Thank you for listening!