2023 LeSN lab

1.SNU Isabelle 2023 (Day 3)

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2.SNU Isabelle 2023 (Day2)

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3.SNU Isabelle 2023 (Day1)

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4.[연구생보고서] SSV lab 6/19~7/11

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5.[PPT 발표 자료] Mutation-Based Fuzzing

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6.[논문리뷰] ModelDiff: Testing-Based DNN Similarity Comparison for Model Reuse Detection

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7.[논문리뷰] 정보과학회지: 심층신경망 경량화 기술 및 활용 사례 소개

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8.[책 리뷰] 로지코믹스: 러셀의 삶을 통해 보는 수학의 원리

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9.AAAI 2022 Tutorial: "Formal Verification of Deep Neural Networks: Theory and Practice"

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10.TensorFlow를 활용한 모델 양자화 및 계층별 분석 방법 (Using Keras, Tensorflow 2.0's Top-level API)

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11.A Survey of Quantization Methods for Efficient Neural Network Inference

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12.Repairing Deep Neural Networks: Fix Patterns and Challenges

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13.[논문리뷰]Safety and Liveness of MCS Lock—Layer by Layer

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14.Difference Between PyTorch and TF(TensorFlow)

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15.PyTorch로 구현된 연구를 TensorFlow로 변환하려는 경우 고려사항

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16.Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference

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17.[책]밑바닥부터 시작하는 딥러닝

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18.[책] fuzzingbook

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19.[논문 리뷰] DeepArc: Modularizing Neural Networks for the Model Maintenance

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20.[Fuzzing book] 3. Code Coverage

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21.[논문리뷰] On Decomposing a Deep Neural Network into Modules

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23.PyTorch layer별 값 비교, 출력

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24.[논문리뷰]SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation

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25.TensorFlow C++ 코드로 연산 구현하기

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26.[Fuzzing Book] Greybox Fuzzing

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27.[논문리뷰]TensorFuzz: Debugging Neural Networks with Coverage-Guided Fuzzing

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28.[DeepArc] 환경설정 및 코드 설명

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29.[논문리뷰] NNrepair: Constraint-based Repair of Neural Network Classifiers

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30.[논문리뷰] Weight Fixing Networks

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31.Pycharm error: Process 641 is still running

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32.Centered Kernel Alignment (CKA) 계산, 시각화 및 배경지식

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33.[논문리뷰] Similarity of Neural Network Representations Revisited

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34.블랙박스 테스팅 vs 화이트박스 테스팅 차이 비교

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35.화이트박스 테스팅 기법 종류와 코드

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36.NNrepair github 살펴보기

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37.Modeldiff github 살펴보기

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38.[논문]What is multi-bit quantization?

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39.Java duplicate class error in IntelliJ

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40.[논문]ReluDiff: Differential Verification of Deep Neural Networks

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41.[논문]NeuroDiff: Scalable Differential Verification of Neural Networks using Fine-Grained Approximation

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42.[논문]On Neural Network Equivalence Checking using SMT Solvers

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43.[논문]Can Differential Testing Improve Automatic Speech Recognition Systems?

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44.RNN(순환신경망) vs CNN(합성곱 신경망) 차이점과 각각의 응용 분야

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45.쉽게 이해하는 딥러닝 양자화-Quantization이란?, 중요한 이유, 주의할 점

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46.ModelDiff: 쉽게 이해하는 딥러닝 검증 도구

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47.Engineering issue(공학적 문제) vs Scientific issue(과학적 문제)

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48.BERT vs ResNet 비교하기

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49.Comparing SMT solver vs SAT solver vs etc.

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50.SMT solver code

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