PDF Model Selection and Evaluation in Supervised Machine Learning
Supervised Machine Learning Author: Max Westphal Supervisor: Prof. Dr. Werner Brannath A thesis submitted in partial fulfilment of the requirements for the degree of Dr. rer. nat. in the Working Group of Applied Statistics and Biometry Faculty 3: Mathematics and Computer Science April 6, 2020
DataSpace: Towards Understanding Self-Supervised Representation Learning
While supervised learning sparked the deep learning boom, it has some critical shortcomings: (1) it requires an abundance of expensive labeled data, and (2) it solves tasks from scratch rather than the human-like approach of leveraging knowledge and skills acquired from prior experiences. ... In this thesis we present works that initiate and ...
(PDF) Supervised Learning
Definition. Supervised Learning is a machine learning paradigm for acquiring the input-output relationship information. of a system based on a given set of paired input-output training s amples ...
A Systematic Review on Supervised and Unsupervised Machine Learning
The supervised learning algorithms are further classified into classification and regression algorithms [3, 4]. Conversely, unsupervised data learning involves pattern recognition without the involvement of a target attribute. That is, all the variables used in the analysis are used as inputs and because of the approach, the techniques are ...
PDF Semi-Supervised Learning with Graphs
•reinforcement learning. The learning system repeatedly observes the envi-ronment x, performs an action a, and receives a reward r. The goal is to choose the actions that maximize the future rewards. This thesis focuses on classification, which is traditionally a supervised lear n-ing task.
PDF Enhancing Self-Supervised Learning through Transformations in Higher
Self-supervised learning [15] is a method of training deep models on large unlabeled datasets to learn transferable representations for downstream tasks. This is accomplished by defining a pre-training or pretext task, which generates pseudo-labels for the unlabeled data, on which the model can be trained. As unlabeled data is typically more ...
PDF RECURSIVE DEEP LEARNING A DISSERTATION
The main three chapters of the thesis explore three recursive deep learning modeling choices. The rst modeling choice I investigate is the overall objective function that crucially guides what the RNNs need to capture. I explore unsupervised, supervised and semi-supervised learning for structure prediction (parsing), structured sentiment
[PDF] Semi-Supervised Learning
This thesis designs a meta-semi-supervised learning algorithm called SemiBoost, which wraps around the underlying supervised algorithm and improve its performance using the unlabeled data and a similarity function and proposes a non-parametric mixture model for data clustering in order to be flexible enough to detect arbitrarily shaped clusters.
PDF Applying Supervised Learning Algorithms and a New Feature
A sub eld of data science is supervised learning theory, which formalizes the algorithmic notion of learning and building predictions from observed data. A classical ... This thesis rst explains in detail two widely used learning algorithms in litera-ture, the k-Nearest Neighbour (k-NN) classi er and the Random Forest classi er, and ...
Theoretical insights on generalization in supervised and self
This thesis seeks to gain a better theoretical understanding of generalization in deep learning. First, we study factors influencing generalization in supervised settings where all data are labeled, obtaining improved generalization bounds for neural networks by considering additional data-dependent properties of the model.
Dissertation or Thesis
Supervised learning problems are commonly seen in a wide range of scientific fields such as medicine and neuroscience. Given data with predictors and responses, an important goal of supervised learning is to find the underlying relationship between predictors and responses for future prediction.
A Comparison of Supervised Machine Learning Classification Techniques
Gmyzin, D. (2017) A Comparison of Supervised Machine Learning Classification Techniques and Theory-Driven Approaches for the Prediction of Subjective Mental Workload. Masters dissertation, Technological University Dublin, 2017. doi:10.21427/D7533X
Imposing and Uncovering Group Structure in Weakly-Supervised Learning
Our thesis focuses on learning from data characterized by weak supervision, delving into the interrelationships among group members. ... Therefore, in the final section, we shift our focus to minimizing the assumptions required when learning from weakly supervised data and simultaneously deducing the group structure during the learning process ...
PDF Semi-Supervised Learning for Natural Language
Statistical supervised learning techniques have been successful for many natural lan-guage processing tasks, but they require labeled datasets, which can be expensive to obtain. On the other hand, unlabeled data (raw text) is often available \for free" in large quantities. Unlabeled data has shown promise in improving the performance
Advanced topics in weakly supervised learning
This doctoral thesis is devoted to investigating some advanced topics in weakly supervised learning, including complementary-label learning and partial-label learning. Complementary-label learning solves the problem where each training example is supplied with a single complementary label (CL), which only specifies one of the classes that the ...
PDF Exploring Probabilistic Models for Semi-supervised Learning
Semi-supervised Learning Jianfeng Wang Linacre College University of Oxford A thesis submitted for the degree of Doctor of Philosophy Trinity 2023 arXiv:2404.04199v1 [cs.LG] 5 Apr 2024. Declarations I solemnly affirm that, unless explicitly acknowledged, the contents of this dissertation
PDF Self-Supervised Learning
Can self-supervised learning help? •Self-supervised learning (informal definition): supervise using labels generated from the data without any manual or weak label sources •Idea: Hide or modify part of the input. Ask model to recover input or classify what changed. •Self-supervised task referred to as the pretext task 6
PDF Self-supervised Video Representation Learning
1.1 Illustration of supervised and self-supervised video rep-resentation learning. Supervised video representation learning: training labels are annotated by human beings. For example, re-garding the typical action recognition problem, a neural network is trained with action classes annotated by human for video repre-sentation learning.
PDF Fundamental Limitations of Semi-Supervised Learning
Abstract. The emergence of a new paradigm in machine learning known as semi-supervised learning (SSL) has seen bene ts to many applications where labeled data is ex-pensive to obtain. However, unlike supervised learning (SL), which enjoys a rich and deep theoretical foundation, semi-supervised learning, which uses additional unlabeled data for ...
Self-supervised visual learning in the low-data regime: a comparative
Self-Supervised Learning (SSL) is a valuable and robust training methodology for contemporary Deep Neural Networks (DNNs), enabling unsupervised pretraining on a `pretext task' that does not require ground-truth labels/annotation. This allows efficient representation learning from massive amounts of unlabeled training data, which in turn leads to increased accuracy in a `downstream task' by ...
Doctoral Thesis: Self-Supervised Learning for Speech Processing
this thesis, we explore the use of self-supervised learning—a learning paradigm where the. learning target is generated from the input itself—for leveraging such easily scalable resources. to improve the performance of spoken language technology. Specifically, we propose two. self-supervised algorithms, one based on the idea of "future ...
PDF Self-supervised Scene Representation Learning
Preface In this thesis, Self-supervised Scene Representation Learning, we propose novel approaches to enable artificial intelligence models to infer representations of 3D environments conditioned exclusively on posed images. •We propose to exploit 3D-structured feature spaces in the form of voxelgrids of features,
Doctoral Thesis: Understanding and Improving Representational
Thesis Committee: Luca Daniel, Duane Boning * Pin-Yu Chen. Details. Date: Friday, May 3; Time: 10:00 am - 11:30 am; Location: Haus Room (36-428) ... For a generic non-smooth network, we find a link between self-supervised contrastive learning and supervised neighborhood component analysis, which naturally allows us to propose a general ...
Effective master's thesis supervision
The master's thesis therefore provides a key learning experience for students that holds a prominent place in the graduate school curriculum. In working on their thesis, students are guided by a master's thesis supervisor (or advisor) ... while being supervised by someone with a different cultural background. These studies highlight that the ...
Supervisor and Student Perspectives on Undergraduate Thesis Supervision
Diagnosing teachers are teachers who perceive diagnostic information about students' learning process, interpret these aspects, decide how to respond, and act based on this diagnostic decision. During supervision meetings about the undergraduate thesis supervisors make in-the-moment decisions while interacting with their students.
Lessons From the Front Lines of Canada's Fentanyl Crisis
In a visit supported by the U.S. government, a group of Mexican experts came to British Columbia to discuss ways of responding to rampant opioid deaths.
COMMENTS
Supervised Machine Learning Author: Max Westphal Supervisor: Prof. Dr. Werner Brannath A thesis submitted in partial fulfilment of the requirements for the degree of Dr. rer. nat. in the Working Group of Applied Statistics and Biometry Faculty 3: Mathematics and Computer Science April 6, 2020
While supervised learning sparked the deep learning boom, it has some critical shortcomings: (1) it requires an abundance of expensive labeled data, and (2) it solves tasks from scratch rather than the human-like approach of leveraging knowledge and skills acquired from prior experiences. ... In this thesis we present works that initiate and ...
Definition. Supervised Learning is a machine learning paradigm for acquiring the input-output relationship information. of a system based on a given set of paired input-output training s amples ...
The supervised learning algorithms are further classified into classification and regression algorithms [3, 4]. Conversely, unsupervised data learning involves pattern recognition without the involvement of a target attribute. That is, all the variables used in the analysis are used as inputs and because of the approach, the techniques are ...
•reinforcement learning. The learning system repeatedly observes the envi-ronment x, performs an action a, and receives a reward r. The goal is to choose the actions that maximize the future rewards. This thesis focuses on classification, which is traditionally a supervised lear n-ing task.
Self-supervised learning [15] is a method of training deep models on large unlabeled datasets to learn transferable representations for downstream tasks. This is accomplished by defining a pre-training or pretext task, which generates pseudo-labels for the unlabeled data, on which the model can be trained. As unlabeled data is typically more ...
The main three chapters of the thesis explore three recursive deep learning modeling choices. The rst modeling choice I investigate is the overall objective function that crucially guides what the RNNs need to capture. I explore unsupervised, supervised and semi-supervised learning for structure prediction (parsing), structured sentiment
This thesis designs a meta-semi-supervised learning algorithm called SemiBoost, which wraps around the underlying supervised algorithm and improve its performance using the unlabeled data and a similarity function and proposes a non-parametric mixture model for data clustering in order to be flexible enough to detect arbitrarily shaped clusters.
A sub eld of data science is supervised learning theory, which formalizes the algorithmic notion of learning and building predictions from observed data. A classical ... This thesis rst explains in detail two widely used learning algorithms in litera-ture, the k-Nearest Neighbour (k-NN) classi er and the Random Forest classi er, and ...
This thesis seeks to gain a better theoretical understanding of generalization in deep learning. First, we study factors influencing generalization in supervised settings where all data are labeled, obtaining improved generalization bounds for neural networks by considering additional data-dependent properties of the model.
Supervised learning problems are commonly seen in a wide range of scientific fields such as medicine and neuroscience. Given data with predictors and responses, an important goal of supervised learning is to find the underlying relationship between predictors and responses for future prediction.
Gmyzin, D. (2017) A Comparison of Supervised Machine Learning Classification Techniques and Theory-Driven Approaches for the Prediction of Subjective Mental Workload. Masters dissertation, Technological University Dublin, 2017. doi:10.21427/D7533X
Our thesis focuses on learning from data characterized by weak supervision, delving into the interrelationships among group members. ... Therefore, in the final section, we shift our focus to minimizing the assumptions required when learning from weakly supervised data and simultaneously deducing the group structure during the learning process ...
Statistical supervised learning techniques have been successful for many natural lan-guage processing tasks, but they require labeled datasets, which can be expensive to obtain. On the other hand, unlabeled data (raw text) is often available \for free" in large quantities. Unlabeled data has shown promise in improving the performance
This doctoral thesis is devoted to investigating some advanced topics in weakly supervised learning, including complementary-label learning and partial-label learning. Complementary-label learning solves the problem where each training example is supplied with a single complementary label (CL), which only specifies one of the classes that the ...
Semi-supervised Learning Jianfeng Wang Linacre College University of Oxford A thesis submitted for the degree of Doctor of Philosophy Trinity 2023 arXiv:2404.04199v1 [cs.LG] 5 Apr 2024. Declarations I solemnly affirm that, unless explicitly acknowledged, the contents of this dissertation
Can self-supervised learning help? •Self-supervised learning (informal definition): supervise using labels generated from the data without any manual or weak label sources •Idea: Hide or modify part of the input. Ask model to recover input or classify what changed. •Self-supervised task referred to as the pretext task 6
1.1 Illustration of supervised and self-supervised video rep-resentation learning. Supervised video representation learning: training labels are annotated by human beings. For example, re-garding the typical action recognition problem, a neural network is trained with action classes annotated by human for video repre-sentation learning.
Abstract. The emergence of a new paradigm in machine learning known as semi-supervised learning (SSL) has seen bene ts to many applications where labeled data is ex-pensive to obtain. However, unlike supervised learning (SL), which enjoys a rich and deep theoretical foundation, semi-supervised learning, which uses additional unlabeled data for ...
Self-Supervised Learning (SSL) is a valuable and robust training methodology for contemporary Deep Neural Networks (DNNs), enabling unsupervised pretraining on a `pretext task' that does not require ground-truth labels/annotation. This allows efficient representation learning from massive amounts of unlabeled training data, which in turn leads to increased accuracy in a `downstream task' by ...
this thesis, we explore the use of self-supervised learning—a learning paradigm where the. learning target is generated from the input itself—for leveraging such easily scalable resources. to improve the performance of spoken language technology. Specifically, we propose two. self-supervised algorithms, one based on the idea of "future ...
Preface In this thesis, Self-supervised Scene Representation Learning, we propose novel approaches to enable artificial intelligence models to infer representations of 3D environments conditioned exclusively on posed images. •We propose to exploit 3D-structured feature spaces in the form of voxelgrids of features,
Thesis Committee: Luca Daniel, Duane Boning * Pin-Yu Chen. Details. Date: Friday, May 3; Time: 10:00 am - 11:30 am; Location: Haus Room (36-428) ... For a generic non-smooth network, we find a link between self-supervised contrastive learning and supervised neighborhood component analysis, which naturally allows us to propose a general ...
The master's thesis therefore provides a key learning experience for students that holds a prominent place in the graduate school curriculum. In working on their thesis, students are guided by a master's thesis supervisor (or advisor) ... while being supervised by someone with a different cultural background. These studies highlight that the ...
Diagnosing teachers are teachers who perceive diagnostic information about students' learning process, interpret these aspects, decide how to respond, and act based on this diagnostic decision. During supervision meetings about the undergraduate thesis supervisors make in-the-moment decisions while interacting with their students.
In a visit supported by the U.S. government, a group of Mexican experts came to British Columbia to discuss ways of responding to rampant opioid deaths.