Toward large-scale machine learning: Fast, accurate, high-performance training of deep learning models – The state of the art in natural language processing has been largely dominated by big data. Deep neural networks (DNNs) have been widely used in this task. For this reason, the state of the art in DNNs is very close to traditional deep learning frameworks. However, this work aims at a general purpose framework, instead of merely considering the state of the art, in which the data representation is a non-linear structure. We present a novel approach which simultaneously supports and enables the DNNs to learn the data representation. Empirically, our framework learns to predict non-linear and non-linear temporal relationships of the observed temporal variables in the DNNs and is able to efficiently learn the relationship for each time window to be estimated and perform predictive inference. We believe it significantly improves our work on state-of-the-art DNN models, and further helps in generalize to new datasets. We conduct experiments in order to compare the performance of our new approach using real-world datasets.

Translational information can be integrated into semantic modeling of natural language and its semantic semantic representation by convex optimization. We argue that the convex model is more robust to the use of a constraint on a priori information than the normal convex model. Specifically, we demonstrate that it significantly improves the performance of an autoencoder trained on a fully convex representation of natural language. The convex representation is an iterative, nonconvex solution to the unconstrained problem of optimizing the underlying vector. We develop and analyze an efficient algorithm, which can exploit the constraints and regularity of the embeddings to better achieve an upper bound on the error rate of the model. We use examples taken from the literature to demonstrate the value of this new representation.

Relevance Annotation as a Learning Task in Analytics

Using Generalized Cross-Domain-Universal Representations for Topic Modeling

Toward large-scale machine learning: Fast, accurate, high-performance training of deep learning models

The Fuzzy Box Model — The Best of Both Worlds

Boosting Invertible Embeddings Using Sparse Transforming TextTranslational information can be integrated into semantic modeling of natural language and its semantic semantic representation by convex optimization. We argue that the convex model is more robust to the use of a constraint on a priori information than the normal convex model. Specifically, we demonstrate that it significantly improves the performance of an autoencoder trained on a fully convex representation of natural language. The convex representation is an iterative, nonconvex solution to the unconstrained problem of optimizing the underlying vector. We develop and analyze an efficient algorithm, which can exploit the constraints and regularity of the embeddings to better achieve an upper bound on the error rate of the model. We use examples taken from the literature to demonstrate the value of this new representation.