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Temporal Point Processes for Forecasting Events in Higher-Order Networks
Real-world systems consisting of interacting entities can be effectively represented as time-evolving networks or graphs, where the entities are depicted as nodes, and the interactions between them are represented as ...
Barrier Function Inspired Reward Shaping in Reinforcement Learning
Reinforcement Learning (RL) has progressed from simple control tasks to complex real-world challenges with large state spaces. During initial iterations of training in most Reinforcement Learning (RL) algorithms, agents ...
A Context-Aware Neural Approach for Explainable Citation Link Prediction
Citations have become an integral part of scientific publications. They play a crucial role in supporting authors’ claims throughout a scientific paper. However, citing related work is a challenging and laborious task, ...
Performance Characterization and Optimizations of Traditional ML Applications
Even in the era of Deep Learning based methods, traditional machine learning methods with large data sets continue to attract significant attention. However, we find an apparent lack of a detailed performance characterization ...
A Novel Neural Network Architecture for Sentiment-oriented Aspect-Opinion Pair Extraction
Over the years, fine-grained opinion mining in online reviews has received great attention from the
NLP research community. It involves different tasks such as Aspect Term Extraction (ATE), Opinion Term Extraction (OTE), ...
Representing Networks: Centrality, Node Embeddings, Community Outliers and Graph Representation
Networks are ubiquitous. We start our technical work in this thesis by exploring the classical concept of node centrality (also known as influence measure) in information networks. Like clustering, node centrality is also ...
Novel Neural Architecture for Multi-Hop Question Answering
Natural language understanding has been one of the key drivers responsible for advancing the
eld of AI. To this end, automated Question Answering (QA) has served as an effective way
of measuring the language understanding ...
Design of AI-based Computational Framework for Accurate Detection of Polycystic Ovarian Disease and Ovarian Cancer Using Ultrasound, CT and Histopathology Images
Polycystic Ovarian Disease (PCOD) and Ovarian Cancer (OC) represent critical health challenges affecting millions of women globally. Early and accurate detection of these conditions is essential for effective clinical ...
Protecting Deep Learning Models on Cloud Platforms with Trusted Execution Environments
Deep learning is rapidly integrated into different applications, from medical imaging to financial products. Organisations are spending enormous financial resources to train deep learning models. Often, many organisations ...