cs230.stanford.educs230.stanford.edu/projects_spring_2019/reports/18677789.pdf · and CQT may not...

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Transcript of cs230.stanford.educs230.stanford.edu/projects_spring_2019/reports/18677789.pdf · and CQT may not...

Page 1: cs230.stanford.educs230.stanford.edu/projects_spring_2019/reports/18677789.pdf · and CQT may not represent all the properties of the audio wave. Few observations made while increasing
Page 2: cs230.stanford.educs230.stanford.edu/projects_spring_2019/reports/18677789.pdf · and CQT may not represent all the properties of the audio wave. Few observations made while increasing
Page 3: cs230.stanford.educs230.stanford.edu/projects_spring_2019/reports/18677789.pdf · and CQT may not represent all the properties of the audio wave. Few observations made while increasing
Page 4: cs230.stanford.educs230.stanford.edu/projects_spring_2019/reports/18677789.pdf · and CQT may not represent all the properties of the audio wave. Few observations made while increasing
Page 5: cs230.stanford.educs230.stanford.edu/projects_spring_2019/reports/18677789.pdf · and CQT may not represent all the properties of the audio wave. Few observations made while increasing
Page 6: cs230.stanford.educs230.stanford.edu/projects_spring_2019/reports/18677789.pdf · and CQT may not represent all the properties of the audio wave. Few observations made while increasing

cs230.stanford.educs230.stanford.edu/projects_spring_2019/reports/18681189.pdfMore broadly our project is part of the growing field of object detection and classification. A future

cs230.stanford.educs230.stanford.edu/projects_spring_2019/reports/18681189.pdfMore broadly our project is part of the growing field of object detection and classification. A future

cs230.stanford.educs230.stanford.edu/projects_spring_2019/reports/18681514.pdfFinal part:8x8x2048 1001 Auxiliary Classifier Figure 5: Original InceptionV3 Neural Network Schema(17)

cs230.stanford.educs230.stanford.edu/projects_spring_2019/reports/18681514.pdfFinal part:8x8x2048 1001 Auxiliary Classifier Figure 5: Original InceptionV3 Neural Network Schema(17)

Ilja Gerhardt QUANTUM OPTICS CQT GROUP Ilja Gerhardt, Matthew P. Peloso, Caleb Ho, Antía Lamas-Linares and Christian Kurtsiefer Entanglement-based Free.

Ilja Gerhardt QUANTUM OPTICS CQT GROUP Ilja Gerhardt, Matthew P. Peloso, Caleb Ho, Antía Lamas-Linares and Christian Kurtsiefer Entanglement-based Free.

cs230.stanford.educs230.stanford.edu/projects_spring_2019/reports/18681331.pdf · for a specific digit in a "hand written digit recognition problem". This may lead to an inaccurate

cs230.stanford.educs230.stanford.edu/projects_spring_2019/reports/18681331.pdf · for a specific digit in a "hand written digit recognition problem". This may lead to an inaccurate

cs230.stanford.educs230.stanford.edu/projects_spring_2019/reports/18681243.pdf · connected layers to obtain their object category and confidence level. We keep all the patches with

cs230.stanford.educs230.stanford.edu/projects_spring_2019/reports/18681243.pdf · connected layers to obtain their object category and confidence level. We keep all the patches with

cs230.stanford.educs230.stanford.edu/projects_spring_2019/reports/18679631.pdf · 2019-06-13 · train v2.csv - the updated training set - contains user transactions from August 1st

cs230.stanford.educs230.stanford.edu/projects_spring_2019/reports/18679631.pdf · 2019-06-13 · train v2.csv - the updated training set - contains user transactions from August 1st

cs230.stanford.educs230.stanford.edu/projects_spring_2019/reports/18681618.pdf · Tool detection:Used Fast-RCNN for spatial detection of surgical tools and VGG16 for classification

cs230.stanford.educs230.stanford.edu/projects_spring_2019/reports/18681618.pdf · Tool detection:Used Fast-RCNN for spatial detection of surgical tools and VGG16 for classification

cs230.stanford.educs230.stanford.edu/projects_spring_2018/reports/8270111.pdf · With our efforts through this quarter, we have successfully built a speaker identification algorithm

cs230.stanford.educs230.stanford.edu/projects_spring_2018/reports/8270111.pdf · With our efforts through this quarter, we have successfully built a speaker identification algorithm

cs230.stanford.educs230.stanford.edu/projects_winter_2019/reports/15766721.pdf · and representations of the results), media monitoring, newsletters, social media marketing, question

cs230.stanford.educs230.stanford.edu/projects_winter_2019/reports/15766721.pdf · and representations of the results), media monitoring, newsletters, social media marketing, question

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Ì · ü w S U A p b { « F ° É ¿ Ä ë « t b Ô ù .CQT F · F ¹ .CQT w Í s ¢ S U A Þ ÿ Í w 7 G S q î M w S x s b w p ] « X i ^ M { « Q y z ± Ï µ º 0 U Í 7 G .CQT

CS230 Deep Learningcs230.stanford.edu/projects_spring_2019/reports/18678885.pdfis a treasure trove of information, including that related to virality, user sentiment, networks, and

CS230 Deep Learningcs230.stanford.edu/projects_spring_2019/reports/18678885.pdfis a treasure trove of information, including that related to virality, user sentiment, networks, and

Entanglement sampling and applications Omar Fawzi (ETH Zürich) Joint work with Frédéric Dupuis (Aarhus University) and Stephanie Wehner (CQT, Singapore)

Entanglement sampling and applications Omar Fawzi (ETH Zürich) Joint work with Frédéric Dupuis (Aarhus University) and Stephanie Wehner (CQT, Singapore)

cs230.stanford.educs230.stanford.edu/projects_winter_2019/reports/15813330.pdf · According to the Federal Statistics Office, 2013, the number of newly opened insolvency proceedings

cs230.stanford.educs230.stanford.edu/projects_winter_2019/reports/15813330.pdf · According to the Federal Statistics Office, 2013, the number of newly opened insolvency proceedings

CS230 Deep Learningcs230.stanford.edu/projects_spring_2019/posters/18673358.pdfThe ability to synthesize subsections of large volumes of texts into a concise, summarative format will

CS230 Deep Learningcs230.stanford.edu/projects_spring_2019/posters/18673358.pdfThe ability to synthesize subsections of large volumes of texts into a concise, summarative format will

Wimax-dt&Cqt Test Guide

Wimax-dt&Cqt Test Guide

cs230.stanford.educs230.stanford.edu/files_winter_2018/projects/6940224.pdf · Exploring Knowledge Distillation of Deep Neural Networks for Efficient Hardware Solutions Haitong Li

cs230.stanford.educs230.stanford.edu/files_winter_2018/projects/6940224.pdf · Exploring Knowledge Distillation of Deep Neural Networks for Efficient Hardware Solutions Haitong Li

cs230.stanford.educs230.stanford.edu/projects_winter_2019/reports/15813002.pdf · global education equity (4). CS230: Deep Learning, Winter 2019, Stanford ... To evolve beyond our

cs230.stanford.educs230.stanford.edu/projects_winter_2019/reports/15813002.pdf · global education equity (4). CS230: Deep Learning, Winter 2019, Stanford ... To evolve beyond our

CS230 Deep Learningcs230.stanford.edu/projects_spring_2019/reports/18680161.pdf · separation (BSS) eval in particular, source signal-to-distortion ratio (SDR) and signal-to-interference

CS230 Deep Learningcs230.stanford.edu/projects_spring_2019/reports/18680161.pdf · separation (BSS) eval in particular, source signal-to-distortion ratio (SDR) and signal-to-interference

IMPLEMENTATION OF THE CQT METHODOLOGY FOR BUSINESS PROCESS OPTIMIZATION · 2014-01-07 · IMPLEMENTATION OF THE CQT METHODOLOGY FOR BUSINESS PROCESS OPTIMIZATION ... (Cost-Quality-Time)

IMPLEMENTATION OF THE CQT METHODOLOGY FOR BUSINESS PROCESS OPTIMIZATION · 2014-01-07 · IMPLEMENTATION OF THE CQT METHODOLOGY FOR BUSINESS PROCESS OPTIMIZATION ... (Cost-Quality-Time)

cs230.stanford.educs230.stanford.edu/projects_spring_2019/reports/18675538.pdfconvolutional neural networks. " Convolutional Neural Networks for Visual Recognition 2 (2016). [3] Sharma,

cs230.stanford.educs230.stanford.edu/projects_spring_2019/reports/18675538.pdfconvolutional neural networks. " Convolutional Neural Networks for Visual Recognition 2 (2016). [3] Sharma,