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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 ...
Data Efficient Domain Generalization
Deep neural networks has brought tremendous success in many areas of computer vision, such
as image classification, retrieval, segmentation , etc. However, this success is
mostly measured under two conditions namely (1) ...
Sparse Input View Synthesis: 3D Representations and Reliable Priors
Novel view synthesis refers to the problem of synthesizing novel viewpoints of a scene given the images from a few viewpoints. This is a fundamental problem in computer vision and graphics, and enables a vast variety of ...
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 ...
Model-based Deep Learning Algorithms in Mimo Receivers : Channel Estimation & Symbol Detection
With the advent of Massive multiple-input-multiple-output (MIMO) wireless communication systems, users can now enjoy high spectral efficiency and throughput leading to a better quality of service. The efficacy of these ...
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 ...