Sentence examples for model that learns from inspiring English sources
Drawing on ideas from cognitive linguistics, connectionism, and perception, The Human Semantic Potential describes a connectionist model that learns perceptually grounded semantics for natural language in spatial terms.
We present a deep recurrent autoencoder model that learns richly structured multi-view plot representations from raw book text, approximating such preferences.
We present a connectionist model that learns the print-to-sound mappings of Chinese characters using the same functional architecture and learning rules that have been applied to English.
We train a model that learns representational embeddings for motifs from a large collection of unlabeled data using a generative model.
Resulting localization data was median normalized and further analyzed for statistically significant enrichment using MeDiChI, a regression model that learns a generative model of joint binding events [ 46].
We then instantiate this graph to form a navigation model that learns from raw images and is sample efficient.
A cognitive model that learned from both the upfront and OTS instructions was created and provided good fits to the learning and performance data collected from human participants.
We also used a simple reinforcement learning model that learned the mappings from only binary feedback, i.e. reward 1 if the correct action was chosen, and reward 0 otherwise (see Methods for details).
My current research involves using machine learning (models that learn from past data) to predict good antibodies for binding particular targets.
The general framework is to build predictive models that learn rules of combining multiple genomic annotations, functional attributes, and evolutionary features to discriminate pathogenic variants from non-pathogenic ones.
Recurrent neural networks, and in particular long short-term memory networks (LSTMs), are a remarkably effective tool for sequence modeling that learn a dense black-box hidden representation of their sequential input.
