Browsing Division of Electrical, Electronics, and Computer Science (EECS) by Subject "Deep Learning"
Now showing items 1-7 of 7
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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, ... -
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) ... -
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 ... -
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), ... -
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 ... -
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 ... -
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 ...