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Found 10 papers, 3 papers with code
Systematic Investigation of Strategies Tailored for Low-Resource Settings for Sanskrit Dependency Parsing
On the other hand, purely data-driven approaches do not match the performance of hybrid approaches due to the labelled data sparsity.
Prabhupadavani: A Code-mixed Speech Translation Data for 25 Languages
Nowadays, code-mixing has become ubiquitous in Natural Language Processing (NLP); however, no efforts have been made to address this phenomenon for Speech Translation (ST) task.
Evaluating Neural Word Embeddings for Sanskrit
To effectively use such readily available resources, it is very much essential to perform a systematic study on word embedding approaches for the Sanskrit language.
A Little Pretraining Goes a Long Way: A Case Study on Dependency Parsing Task for Low-resource Morphologically Rich Languages
In this work, we focus on dependency parsing for morphological rich languages (MRLs) in a low-resource setting.
DeepMI: A Mutual Information Based Framework For Unsupervised Deep Learning of Tasks
The primary motivation behind this work is the limitation of the traditional loss functions for unsupervised learning of a given task.
A Method for Handling Multi-class Imbalanced Data by Geometry based Information Sampling and Class Prioritized Synthetic Data Generation (GICaPS)
This is achieved by finding \emph{no man's land} based on Euclidean distance between the feature vectors.
Domain Independent Unsupervised Learning to grasp the Novel Objects
In this paper, we present a novel unsupervised learning based algorithm for the selection of feasible grasp regions.
Real-time Grasp Pose Estimation for Novel Objects in Densely Cluttered Environment
In this paper, a real-time grasp pose estimation strategy for novel objects in robotic pick and place applications is proposed.
Robotics Image and Video Processing
Optimal Convergence Rate in Feed Forward Neural Networks using HJB Equation
It is hoped that the proposed algorithm will bring in a lot of interest in researchers working in developing fast learning algorithms and global optimization.
