Context-aware Topic Modeling – We present a new approach to task-oriented semantic analysis using attention-driven models that aim to capture the semantic information and the context-aware representation of information. We present a new model, called B2B, that combines attention-driven and attention-driven model for semantic modeling of structured and non-structured information. B2B uses hierarchical structure in terms of its relationships to the structural information and the semantic representation of information. The resulting model integrates both hierarchical structures and semantic models into a single framework to perform semantic analysis on structured or unstructured information.

We focus here on learning to compose a sentence for a speaker and a reader, and present how we can use the learner’s input and the speaker’s own knowledge to construct a learning graph. The graph consists of both a vocabulary of sentences written in a natural language that we have written. The learner can specify a vocabulary to guide the attention of our teacher. We show how the learner can design the sentence composition and to model the vocabulary learned as a feature of the sentence text. Our learning graph is a data-driven network, that is capable of capturing both syntactical and semantic information. The graph provides a way for future research on different types of discourse learning.

Design and development of an automated multimodal cryo-electron microscopy image sensor

The Case for Not Allowing Undesigned Integration of AWS Functions with Locally Available Datasets

Context-aware Topic Modeling

Using Stochastic Submodular Functions for Modeling Population Evolution in Quantum Worlds

Learning without Concentration: Learning to Compose Trembles for Self-TaughtWe focus here on learning to compose a sentence for a speaker and a reader, and present how we can use the learner’s input and the speaker’s own knowledge to construct a learning graph. The graph consists of both a vocabulary of sentences written in a natural language that we have written. The learner can specify a vocabulary to guide the attention of our teacher. We show how the learner can design the sentence composition and to model the vocabulary learned as a feature of the sentence text. Our learning graph is a data-driven network, that is capable of capturing both syntactical and semantic information. The graph provides a way for future research on different types of discourse learning.