Domain-specific hardware is becoming a promising topic in the backdrop of improvement
slow down for general-purpose processors due to the foreseeable end of Moore's Law …

Sentiment analysis is an emerging technology that aims to explore people's attitudes toward
an entity. It can be applied in a variety of different fields and scenarios, such as product …

Tensors, or multidimensional arrays, are data structures that can naturally represent visual
data of multiple dimensions. Inherently able to efficiently capture structured, latent semantic …

Tensors are higher-order extensions of matrices. While matrix methods form the cornerstone
of traditional machine learning and data analysis, tensor methods have been gaining …

Visual recognition is currently one of the most important and active research areas in
computer vision, pattern recognition, and even the general field of artificial intelligence. It …

Deep neural networks have demonstrated state-of-the-art performance in a variety of real-
world applications. In order to obtain performance gains, these networks have grown larger …

We propose meta-curvature (MC), a framework to learn curvature information for better
generalization and fast model adaptation. MC expands on the model-agnostic meta-learner …

Inspired by coarse-graining approaches used in physics, we show how similar algorithms
can be adapted for data. The resulting algorithms are based on layered tree tensor networks …

Abstract Recurrent Neural Networks (RNNs) are powerful sequence modeling tools.
However, when dealing with high dimensional inputs, the training of RNNs becomes …

The model we developed is a novel comprehensive solution to compress and accelerate the
Visual Question Answering systems. In our algorithm Convolutional Neural Network is …