Knowledge Transfer

Knowledge transfer, often referred to as transfer learning in the field of machine learning and artificial intelligence, is a technique where knowledge learned from a task is re-used to boost performance on a related but different task. For example, in training a classifier to predict whether an image contains a dog, we can use the knowledge gained during training to recognize cats. 

Network Compression

Deep neural networks have been showing outstanding performance in many applications, however, this performance comes at high computational and storage requirements. Network compression methods focus on reducing the number of parameters while maintaining high performance. Neural network compression is an important step for deploying deep networks where speed is of high importance, or on devices with limited resources.

Computer Vision

Computer vision is a field of artificial intelligence that aims to interpret and understand the contents of an image or a video. In computer vision, the most commonly used tasks are image classification, object detection, and image segmentation. Owing to the substantial advance in deep learning, the applications of computer vision techniques extend to emerging areas.

Statistical Learning

Statistical learning theory is a framework for machine learning drawing from the fields of statistics and functional analysis. Statistical learning theory deals with the statistical inference problem of finding a predictive function based on data. Statistical learning theory has led to successful applications in fields such as computer vision, speech recognition, and bioinformatics.

Anomaly Detection

In data analysis, anomaly detection is generally understood to be the identification of rare items, events or observations which deviate significantly from the majority of the data and do not conform to a well defined notion of normal behavior. Typically, anomaly detection finds application in many domains including cyber-security, medicine, machine vision, statistics, neuroscience, law enforcement and financial fraud.

Time-series Analysis

Time series analysis comprises methods for analyzing time series data in order to extract meaningful statistics and other characteristics of the data. Time series forecasting is the use of a model to predict future values based on previously observed values. Online learning for time series prediction involves updating parameters with new instances without refitting the entire process.

Explainable AI

Explainable AI (XAI), as the word implies is a type of artificial intelligence which enables the explanation of learning models and focuses on why the system arrived at a particular decision, exploring its logical paradigms, contrary to the inherent black box nature of artificial intelligence. Model interpretability allows users to comprehend the results of the learning models by providing reasoning for the decisions that it has arrived at. XAI is particularly helpful in the context of AI applications pertaining to healthcare and medical diagnosis.