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Page 1: 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

Page 2: 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

Page 3: 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

Page 4: 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

Page 5: 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

Page 6: 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 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.stanford.educs230.stanford.edu/projects_spring_2019/reports/18676218.pdfProblem Statement: The purpose of this project was to create a system - based on neural networks - that

Plankton Classiﬁcation Using VGG16 Networknoiselab.ucsd.edu/ECE228/FinalProjects/Group16.pdf · tional neural networks ”learn”. 1. Introduction ... on machine learning techniques

cs230.stanford.educs230.stanford.edu/projects_spring_2018/reports/8289547.pdf · MOOCs and online courses have notoriously high attrition [1]. One challenge is ... a student's performance

CS230 Deep Learningcs230.stanford.edu/projects_spring_2019/reports/18681615.pdfStanford University 1050 Arastradero Rd., Stanford, CA kkaganov [ at ] stanford.edu Abstract In order

cs230.stanford.educs230.stanford.edu/projects_spring_2018/posters/8285590.pdf · melody. Chord arrangement involves both conventional rules and creativity. Ideal model: Generate chords

Comparison of FairMOT-VGG16 and MCMOT Implementation for

Object detection: Comparison of VGG16 and SSDhomepages.cae.wisc.edu/~ece539/project/f18/palani_rpt.pdf · 2018. 12. 22. · prediction networks have been trained on PASCAL VOC dataset

cs230.stanford.educs230.stanford.edu/projects_fall_2018/posters/12377987.pdf · U.S. Timely, accurate diagnosis is a critical factor in determining patient outcomes. Currently, pneumonia

cs230.stanford.educs230.stanford.edu/projects_spring_2019/reports/18662951.pdffrom the logs for different layers of the software stack, and abstract from a high level (later cnn

cs230.stanford.educs230.stanford.edu/projects_spring_2019/reports/18679149.pdf · U-Net is a popular network choice for image segmentation tasks. Its simple structure makes it easy

cs230.stanford.educs230.stanford.edu/projects_winter_2019/reports/15802276.pdf · each artist. The resulting model attained good performance over the baseline, and provided subjectively

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_2018/reports/8291220.pdf · Much of the published research on applying DL techniques in financial market applications is based

cs230.stanford.educs230.stanford.edu/projects_spring_2019/reports/18680194.pdf · capture short term trends in the market under the set of assumptions they impose on the underlying

Weakly supervised object detection (WSOD) · Diba CVPR 2017 (VGG16) Bilen CVPR 2016 (VGG16) Bilen CVPR 2016 (AlexNet) Cinbis PAMI 2016 (AlexNet+FV) Bilen CVPR 2015 Wang ECCV 2014

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/18681176.pdfreviews, whether they are movie reviews, Amazon reviews, workplace reviews is a common occurrence in

cs230.stanford.educs230.stanford.edu/projects_spring_2018/posters/8285130.pdf · Image Restoration of Noisy and Low-Quality Retinal Images Katherine Sytwul, Fariah Hayee2 Dept. of

cs230.stanford.educs230.stanford.edu/projects_winter_2019/posters/15811897.pdfdeep reinforcement learning networks to play simple cooperative games. This project utilizes a simulated

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_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_winter_2019 › posters › 15794817.pdf · on the signal of similar pixels2. Here we use the scikit-image fast-mode implementation

cs230.stanford.educs230.stanford.edu/projects_spring_2019/reports/18681630.pdf · (Ng) "LSTM (long short term memory) unit" In the above formulas, the top equation of c represents

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

arXiv:1910.03676v1 [cs.CV] 8 Oct 2019 · arXiv:1910.03676v1 [cs.CV] 8 Oct 2019. Shade 1 Shade 2 Shade 3 Shade 4 Shade 5 Shade 6 1 0 Average Face per Shade Baseline VGG16 BR-Net VGG16

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

Combining textual and visual representations for ...ceur-ws.org/Vol-2125/paper_219.pdf · representation is extracting using an average VGG16/ResNet50, while textual representation

cs230.stanford.educs230.stanford.edu/projects_spring_2018/reports/8289614.pdfduration, or only textual features, such as project description and keywords. To our knowledge, we are

cs230.stanford.educs230.stanford.edu/projects_fall_2018/reports/12447290.pdf · Emanuel Mendiola emanuelm@stanf ord. edu As techniques for creating photo realistic imagery evolve,

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

Deep Compression and EIE - NVIDIA · 2016-04-14 · Part1: Deep Compression • AlexNet: 35×, 240MB => 6.9MB => 0.47MB (510x) • VGG16: 49×, 552MB => 11.3MB • With no loss of

cs230.stanford.educs230.stanford.edu/projects_winter_2019/reports/15813424.pdf · [1] Alexander Toshev and Christian Szegedy. Deeppose: Human pose estimation via deep neural networks

Visual Speech Recognition using VGG16 Convolutional Neural

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