PPT-Image2Vec: Learning word and image representations for reasoning

Lerrel J Pinto Gunnar A Sigurdsson How would you summarize this image in 3 words Joint word and image embedding Given an image get words that approximate . IT 530, Lecture Notes. Introduction: Complete and over-complete bases. Signals are often represented as a linear combination of basis functions (e.g. Fourier or wavelet representation).. The basis functions always have the same dimensionality as the (discrete) signals they represent.. Patricia A. Alexander. Forward a claim about the association between relational reasoning with metacognition theory and research. Consider the nature of percepts and concepts in human learning and performance. What does the brain actually do?. Some possible answers:. “The mind”. Information processing…. Transforms of mental representations. Execution of mental representations of actions. First Principles. Manaal Faruqui. Sujay. . Jauhar. , Jesse Dodge. Chris Dyer, Noah Smith. Distributional Semantics. “You shall know a word by the company it keeps”. (Harris 1954; Firth, 1957). …I will take what is mine with . Anne Watson. South West 2013. Key ideas. Generalise relationships. Equivalent expressions. Solve equations. Express situations. Relate representations. New from old. Notation. Non-statutory Guidance yr 6. via Brain simulations . Andrew . Ng. Stanford University. Adam Coates Quoc Le Honglak Lee Andrew Saxe Andrew Maas Chris Manning Jiquan Ngiam Richard Socher Will Zou . Thanks to:. Natural Language Processing. Tomas Mikolov, Facebook. ML Prague 2016. Structure of this talk. Motivation. Word2vec. Architecture. Evaluation. Examples. Discussion. Motivation. Representation of text is very important for performance of many real-world applications: search, ads recommendation, ranking, spam filtering, …. Chao Xing. CSLT Tsinghua. Why?. Chris Dyer. group had gotten a lot of brilliant achievements in 2015, and their research interest match to ours. . And in some area, we two groups almost think same way, but we didn’t do so well as they did.. Michael . Elad. The Computer Science Department. The . Technion. – Israel Institute of technology. Haifa 32000, . Israel. David L. Donoho. Statistics Department Stanford USA. and their Compositionality. Presenter: Haotian Xu. Roadmap. Overview. The Skip-gram Model with Different . Objective Functions. Subsampling of Frequent Words. Learning Phrases. CNN for Text Classification. The Treachery of Images. (1928-9). Ren. é. Magritte: . Two Mysteries. (1966). Representation and reality. Re-presentations: we think that the model (‘reality’) precedes, pre-exists the representation . William L. Hamilton, Rex Ying, Jure . Leskovec. Keshav Balasubramanian. Outline. Main goal: generating node embeddings. Survey of past methods. GCNs. GraphSAGE. Algorithm. Optimization and learning. Aggregators. Jay McClelland, . Stanford University. Effects of . Hippocampal. Lesions in Humans. Intact performance on tests of general intelligence, world knowledge, language, digit span, … . Dramatic . deficits in formation of some types of new . Deep Learning for Medical Applications (IN2107). Student: Kristina Diery. Tutor: Chantal Pellegrini. Agenda. 1. Introduction. 1.1 Problem Statement. 1.2 Contrastive Learning. 2. Applications. 2.1 Classification, Retrieval.