Domain - 國立臺灣大學
Training a classifier is ... Little but labeled • Idea: training a model by source data, then fine-tune the model by target data • Challenge: only limited target data, so be careful about overfitting. Domain Adaptation Source Domain (with labeled data) 4 ^ ì _ ^ í _ Knowledge of target domain
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Introduction of Reinforcement Learning - 國立臺灣大學
speech.ee.ntu.edu.twScenario of Reinforcement Learning Agent Environment Observation Action Don’t do Reward that State Change the environment
Self-supervised Learning
speech.ee.ntu.edu.tw•Corpus of Linguistic Acceptability (CoLA) •Stanford Sentiment Treebank (SST-2) •Microsoft Research Paraphrase Corpus (MRPC) •Quora Question Pairs (QQP) ... Sentiment analysis Random initialization Init by pre-train This is the model to be learned. this is good
Convolutional Neural Network - 國立臺灣大學
speech.ee.ntu.edu.twFully Connected Feedforward network output. ... object detection and semantic segmentation”, CVPR, 2014. Convolution Max Pooling Convolution Max Pooling input 25 3x3 filters 50 3x3 ... “Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps”, ICLR, 2014 | ...
Transformer
speech.ee.ntu.edu.twBeam Search A B A B A B A B A B A B A B 0.4 0.9 0.9 0.6 0.4 0.4 0.6 0.6 The green path is the best one. Not possible to check all the paths … Assume there are only two tokens (V=2). The red path is Greedy Decoding. →Beam Search
Machine Learning PyTorch Tutorial - 國立臺灣大學
speech.ee.ntu.edu.twPyTorch Tutorial TA：張恆瑞 (Heng-Jui Chang) 2021.03.05. Outline Prerequisites What is PyTorch? PyTorch v.s. TensorFlow Overview of the DNN Training Procedure ... C++, JavaScript, Swift Debug Easier Difficult (easier in 2.0) Application Research Production. Overview of the DNN Training Procedure Define Neural Network Loss Function Optimizer ...
Machine Learning 2020 - NTU Speech Processing Laboratory
speech.ee.ntu.edu.twText-to-Speech Synthesis Machine Translation Text (Chinese) Text (English) ... •All the assignments have sample codes based on Python. •You need to be able to read and modify the sample ... 3/12 Deep Learning Classification 3/19 Theory of ML (Prof. Pei-Yuan Wu) 3/26 Self-attention CNN / Self-attention
Convolutional Neural Network - 國立臺灣大學
speech.ee.ntu.edu.twConvolutional Neural Network (CNN) Network Architecture designed for Image 1. Image Classification Model ... Benefit of Convolutional Layer Fully Connected Layer •Some patterns are much smaller than the whole image. Receptive Field …
AUTO-ENCODER
speech.ee.ntu.edu.twVincent, Pascal, et al. "Extracting and composing robust features with denoising autoencoders." ICML, 2008. Add noises The idea sounds familiar? ☺ ...
Generation - NTU Speech Processing Laboratory
speech.ee.ntu.edu.twminimize cross entropy = class 1 class 2 Train a binary classifier . Discriminator ... Image Style Transfer Domain Domain ... Unsupervised Conditional Generation . Learning from Unpaired Data Network 70 Domain Domain ...
You can listen to the English version of this course at ...
speech.ee.ntu.edu.tw•Math: Calculus (微積分), Linear algebra (線性代數) and Probability (機率) •Programming •All the assignments have sample codes based on Python. •You need to be able to read and modify the sample codes. This course will not teach Python. •This course will not teach any Python package, except PyTorch. •Only focus on ML.
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SVM &GA-CLUSTERING BASED FEATURE SELECTION …
aircconline.comirrespective of the classifier used. This procedure, however, lacks the classifier relationship; each feature is interpreted separately from functional dependence. Wrapper's approach depends on classification. Classifier results are used to assess …
Tutorial on Support Vector Machine (SVM)
course.ccs.neu.edumaximum margin classifier or hyper plane as an apparent solution. The next illustration gives the maximum margin classifier example which provides a solution to the above mentioned problem [8]. Figure 4: Illustration of Linear SVM. ( Taken from Andrew W. Moore slides 2003) [2]. Note the legend is not described as they are
Maximum Classifier Discrepancy for Unsupervised Domain ...
openaccess.thecvf.comMaximum Classifier Discrepancy for Unsupervised Domain Adaptation Kuniaki Saito1, Kohei Watanabe1, Yoshitaka Ushiku1, and Tatsuya Harada1,2 1The University of Tokyo, 2RIKEN {k-saito,watanabe,ushiku,harada}@mi.t.u-tokyo.ac.jp Abstract In this work, we present a method for unsupervised do-
Explainable Artificial Intelligence (XAI)
sites.cc.gatech.eduany classifier in a faithful way, by approximating it locally with an interpretable model. Electric Guitar . p = 0.32 . Acoustic Guitar . p = 0.24 • SP-LIME is . a method that selects a set of representative instances with explanations as a way to characterize the entire model. Example Explanation . 12 . Black-box Induction
Introduction to Boosted Trees - New Jersey Institute of ...
web.njit.edu•How can we build a boosted tree classifier to do weighted regression problem, such that each instance have a importance weight? Define objective, calculate , feed it to the old tree learning algorithm we have for un-weighted version Again think …
CS340 Machine learning Lecture 4 K-nearest neighbors
www.cs.ubc.caNearest neighbor classifier • Remember all the training data (non-parametric classifier) ... Approaches to model selection • We can choose the model which optimizes the fit to the training data minus a complexity penalty • Complexity can be measured in various ways