Through a combination of advanced training techniques and neural network architectural components, it is now possible to create neural networks that can handle tabular data, images, text, and audio as both input and output.
Recurrent Neural Networks (RNNs) with Long Short-Term Memory units (LSTM) are widely used because they are expressive and are easy to train.
Robotic automation is a key driver for the advancement of technology.
Robotics
The programming of robotic assembly tasks is a key component in manufacturing and automation.
This thesis report studies methods to solve Visual Question-Answering (VQA) tasks with a Deep Learning framework.
Dynamic analysis enables detecting Windows malware by executing programs in a controlled environment, and storing their actions in log reports.
RoseNNa is a non-invasive, lightweight (1000 lines), and performant tool for neural network inference, with focus on the smaller networks used to augment PDE solvers, like those of CFD, which are typically written in C/C++ or Fortran.
In this paper, we propose a novel and low-cost feature extraction approach, and an effective deep neural network architecture for accurate and fast malware detection.
In this paper we apply neural networks and Artificial Intelligence (AI) to historical records of high-risk cryptocurrency coins to train a prediction model that guesses their price.