Published inTDS ArchiveMADE — Masked Autoencoder for Distribution EstimationAn in-depth explanation of Masked Autoencoder for distribution estimation (MADE)Feb 18, 20213Feb 18, 20213
Published inTDS ArchiveWhat is Rejection Sampling?Rejection sampling is a Monte Carlo algorithm to sample data from a sophisticated (“difficult to sample from”) distribution.Feb 15, 20216Feb 15, 20216
Published inTDS ArchiveBut what is Entropy?Gain intuitive and mathematical insights about entropy with examples from a variety of perspectivesJul 13, 20203Jul 13, 20203
Published inTDS Archive[Paper Summary] Distilling the Knowledge in a Neural NetworkKey Insights from the paper that started the research area of Knowledge DistillationJun 30, 20202Jun 30, 20202
Published inTDS Archive[Knowledge Distillation] FitNets : Hints For Thin Deep NetsReview of techniques to distill information from teacher to student by using the outputs of the middle layers of the teacher networkJun 29, 2020Jun 29, 2020
Published inTDS Archive[Paper Summary] On the detection of digital face manipulationHow to detect fake face images and localize the manipulated parts by using attention mapsDec 2, 20191Dec 2, 20191
Published inTDS Archive[Paper Summary] Deep Tree Learning for Zero-Shot Face Anti-SpoofingAn interesting and viable take on how to detect unknown face spoof types described by a best paper finalist at CVPR2019Dec 1, 20191Dec 1, 20191
Published inTDS ArchiveFind thy hyper-parameters for scikit-learn pipelines using Microsoft NNIHow to use Microsoft NNI and automate hyper-parameter search for scikit-learn pipelines using scikit-nniNov 6, 2019Nov 6, 2019
Published inTDS ArchiveBut what is a Random Variable ?Clear and simple explanations for what Random Variables are and their connections to probability theory.Nov 1, 20193Nov 1, 20193
Published inTDS ArchiveA unified method for downloading, extracting, processing & using datasetsIntroduction to tensorflow_datasets and how to use it for private datasetsSep 18, 2019Sep 18, 2019
![[Knowledge Distillation] FitNets : Hints For Thin Deep Nets](https://miro.medium.com/v2/resize:fill:160:106/1*KJ2tWAevo6yjufHAnviPJQ.jpeg)
![[Knowledge Distillation] FitNets : Hints For Thin Deep Nets](https://miro.medium.com/v2/resize:fill:320:214/1*KJ2tWAevo6yjufHAnviPJQ.jpeg)
![[Paper Summary] On the detection of digital face manipulation](https://miro.medium.com/v2/resize:fill:160:106/1*m7WzTS5kLYjvcIL0meXyNw.jpeg)
![[Paper Summary] On the detection of digital face manipulation](https://miro.medium.com/v2/resize:fill:320:214/1*m7WzTS5kLYjvcIL0meXyNw.jpeg)
![[Paper Summary] Deep Tree Learning for Zero-Shot Face Anti-Spoofing](https://miro.medium.com/v2/resize:fill:160:106/1*Z6UBHjbDFsuj20VS27ZIHQ.jpeg)
![[Paper Summary] Deep Tree Learning for Zero-Shot Face Anti-Spoofing](https://miro.medium.com/v2/resize:fill:320:214/1*Z6UBHjbDFsuj20VS27ZIHQ.jpeg)
