Overview of From image descriptions to visual denotations: New similarity metrics for semantic inference over event descriptions — Semantic interpretation, or making inferences on the meaning of atext is a fundamental step in language understanding. Drawing from our human ability to envisionmental images when prompted by a description, this paper introduces a novel approach for automaticallydenoting similarities between descriptions of everyday situations via sets of images and captions…

An Overview of Segmenting Scenes by Matching Image Composites Introduction One of the major goals in computer vision is semantic object segmentation, which is essentially identifying different objects that have semantic meaning (toothbrush or tree) within an image. …

Review of Microsoft’s Deep Residual Learning for Image Recognition — Note: This paper was a bit difficult for me and I relied heavily on other sources to help me understand the material. …

A Review of Joseph Halpern’s A Modification of the Halpern-Pearl Definition of Causality Waves: An Introduction Just as the waves ebb and flow, the principles of causality, cause and effect push and pull the world around us into the way that it is. Despite the fact that causality in principle is something that…

Finding a Balance Using First Order Logic and Markov Random Fields — There is a huge need in computer science, specifically in developing technologies of artificial intelligence and machine learning for representing knowledge as well as making predictions on the world/for future. There are two primary approaches to solving this problem: There is the logical mindset where we approach this problem through…