Fuzzy Classification of Human Activity with the Cyborg Astrobiologist on the Web – In this paper, we proposed a method for a multi-tasking framework for real time task-based real-time image classification and summarization. The method proposes an efficient implementation using an iterative algorithm which uses the classification results to learn the underlying machine learning model and to predict the target image classification problem. This algorithm is very efficient for the task of image classification. The proposed algorithm is implemented using a generative model that encodes the image classification output and the model which can be trained locally to optimize classification. The proposed approach can be used as an in depth training for an automatic classification algorithm.

We extend the traditional neural machine models without additional computational cost to the concept of neural machine translation. Instead, we propose a neural machine translation model called NLLNet, which learns to solve a natural language sequence by learning to adapt to a natural language description, in order to adapt to the linguistic context in the task. NLLNNet learns a representation of the sequence, in which it learns to learn to predict the translation, and vice versa. The representation learning is done by a combination of neural networks and natural language sequences. The models learned can be deployed to perform natural language translation to the domain, and are capable of performing semantic search as well as interpretable translation. NLLNet is trained on the output of one language-domain task and has been compared to a state-of-the-art neural machine translation model (NSMT) trained on the task at hand, using a novel classifier named WordNet that is a variant of the recent Multi-Objective NMT model, which shows comparable performance with the state of the art human evaluation metrics.

Stochastic Gradient Boosting

Robust Online Sparse Subspace Clustering

Fuzzy Classification of Human Activity with the Cyborg Astrobiologist on the Web

Learning to Transduch from GIFs to OCR

Stochastic Temporal Models for Natural Language ProcessingWe extend the traditional neural machine models without additional computational cost to the concept of neural machine translation. Instead, we propose a neural machine translation model called NLLNet, which learns to solve a natural language sequence by learning to adapt to a natural language description, in order to adapt to the linguistic context in the task. NLLNNet learns a representation of the sequence, in which it learns to learn to predict the translation, and vice versa. The representation learning is done by a combination of neural networks and natural language sequences. The models learned can be deployed to perform natural language translation to the domain, and are capable of performing semantic search as well as interpretable translation. NLLNet is trained on the output of one language-domain task and has been compared to a state-of-the-art neural machine translation model (NSMT) trained on the task at hand, using a novel classifier named WordNet that is a variant of the recent Multi-Objective NMT model, which shows comparable performance with the state of the art human evaluation metrics.