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Seminar on Applied Math

Applie Math Seminar:Qualifying Talk

Speaker: Devin Willmott

Title: Generative Neural Networks in Semi-Supervised Learning



Abstract: Semi-supervised learning is a relatively new machine learning concept that seeks to use both labeled and unlabeled data to perform supervised learning tasks. We will look at two network types with some promising applications to semi-supervised learning: ladder networks and adversarial networks. For each, we will discuss the motivations behind their architectures & training methods, and derive some favorable theoretical properties about their capabilities.

Date:
-

Applie Math Seminar:Qualifying Talk

Speaker: Devin Willmott

Title: Generative Neural Networks in Semi-Supervised Learning



Abstract: Semi-supervised learning is a relatively new machine learning concept that seeks to use both labeled and unlabeled data to perform supervised learning tasks. We will look at two network types with some promising applications to semi-supervised learning: ladder networks and adversarial networks. For each, we will discuss the motivations behind their architectures & training methods, and derive some favorable theoretical properties about their capabilities.

Date:
-

Applie Math Seminar:Qualifying Talk

Speaker: Devin Willmott

Title: Generative Neural Networks in Semi-Supervised Learning



Abstract: Semi-supervised learning is a relatively new machine learning concept that seeks to use both labeled and unlabeled data to perform supervised learning tasks. We will look at two network types with some promising applications to semi-supervised learning: ladder networks and adversarial networks. For each, we will discuss the motivations behind their architectures & training methods, and derive some favorable theoretical properties about their capabilities.

Date:
-

Applie Math Seminar:Qualifying Talk

Speaker: Devin Willmott

Title: Generative Neural Networks in Semi-Supervised Learning



Abstract: Semi-supervised learning is a relatively new machine learning concept that seeks to use both labeled and unlabeled data to perform supervised learning tasks. We will look at two network types with some promising applications to semi-supervised learning: ladder networks and adversarial networks. For each, we will discuss the motivations behind their architectures & training methods, and derive some favorable theoretical properties about their capabilities.

Date:
-

Applie Math Seminar:Qualifying Talk

Speaker: Devin Willmott

Title: Generative Neural Networks in Semi-Supervised Learning



Abstract: Semi-supervised learning is a relatively new machine learning concept that seeks to use both labeled and unlabeled data to perform supervised learning tasks. We will look at two network types with some promising applications to semi-supervised learning: ladder networks and adversarial networks. For each, we will discuss the motivations behind their architectures & training methods, and derive some favorable theoretical properties about their capabilities.

Date:
-

Applie Math Seminar:Qualifying Talk

Speaker: Devin Willmott

Title: Generative Neural Networks in Semi-Supervised Learning



Abstract: Semi-supervised learning is a relatively new machine learning concept that seeks to use both labeled and unlabeled data to perform supervised learning tasks. We will look at two network types with some promising applications to semi-supervised learning: ladder networks and adversarial networks. For each, we will discuss the motivations behind their architectures & training methods, and derive some favorable theoretical properties about their capabilities.

Date:
-

Applie Math Seminar:Qualifying Talk

Speaker: Devin Willmott

Title: Generative Neural Networks in Semi-Supervised Learning



Abstract: Semi-supervised learning is a relatively new machine learning concept that seeks to use both labeled and unlabeled data to perform supervised learning tasks. We will look at two network types with some promising applications to semi-supervised learning: ladder networks and adversarial networks. For each, we will discuss the motivations behind their architectures & training methods, and derive some favorable theoretical properties about their capabilities.

Date:
-

Applied Math Seminar: Master's Talk

Title:   Matrix Factorization Techniques for Recommender Systems

Abstract: Recommendation Systems apply Information Retrieval techniques to select the online information relevant to a given user. Collaborative Filtering (CF) is currently most widely used approach to build Recommendation System. To address this issue, the collaborative filtering recommendation algorithm is based on singular value decomposition (SVD) . How the SVD works to make recommendations is presented in this master talk.

Date:
-
Location:
POT 110

Applied Math Seminar: Master's Talk

Title:   Matrix Factorization Techniques for Recommender Systems

Abstract: Recommendation Systems apply Information Retrieval techniques to select the online information relevant to a given user. Collaborative Filtering (CF) is currently most widely used approach to build Recommendation System. To address this issue, the collaborative filtering recommendation algorithm is based on singular value decomposition (SVD) . How the SVD works to make recommendations is presented in this master talk.

Date:
-
Location:
POT 110

Applied Math Seminar: Master's Talk

Title:   Matrix Factorization Techniques for Recommender Systems

Abstract: Recommendation Systems apply Information Retrieval techniques to select the online information relevant to a given user. Collaborative Filtering (CF) is currently most widely used approach to build Recommendation System. To address this issue, the collaborative filtering recommendation algorithm is based on singular value decomposition (SVD) . How the SVD works to make recommendations is presented in this master talk.

Date:
-
Location:
POT 110