IIIT-Delhi Institutional Repository

Browsing by Author "Majumdar, Angshul (Advisor)"

Browsing by Author "Majumdar, Angshul (Advisor)"

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  • Addressing coldstart problem in recommender systems 
    Banerjee, Shisagnee; Majumdar, Angshul (Advisor) (2016-09-15)
    In the following three chapters of my thesis, I have applied several methods to solve the coldstart problem in Recommender Systems. The coldstart problem is the situation where a user or item is new to a website and ...
  • Analysis dictionary learning for classification 
    Bhattacharjee, Protim; Majumdar, Angshul (Advisor) (2016-09-13)
    Data classification is the core of leading technologies today. With the explosion of data through mobility and growth of the Internet, analysis and classification of data is the immediate process after acquisition. Most ...
  • Class-wise deep dictionary learning 
    Khurana, Prerna; Majumdar, Angshul (Advisor) (2016-09-13)
    In this work we propose a classification framework called class-wise deep dictionary learning (CWDDL). For each class, multiple levels of dictionaries are learnt using features from the previous level as inputs (for first ...
  • A computational approach to assist healthcare professionals in selecting antibacterial drugs to treat bacterial infections 
    Chatterjee, Sayantika; Majumdar, Angshul (Advisor) (IIIT-Delhi, 2023-05)
    The reproducibility of experiments has been a long-standing obstruction for farther scientific evolution. Computational methods are being involved to accelerate and to economize drug discovery and the development process. ...
  • Deep clustering 
    Goel, Anurag; Majumdar, Angshul (Advisor) (IIIT-Delhi, 2023-11)
    The traditional way of clustering is first extracting the feature vectors according to domain-specific knowledge and then employing a clustering algorithm on the extracted features. Deep learning approaches attempt to ...
  • Deep dictionary learning 
    Tariyal, Snigdha; Majumdar, Angshul (Advisor) (2016-09-13)
    This Thesis focuses on combining the two well researched concepts of representation learning – Dictionary Learning and Deep Learning. These two learning paradigms have been known for long. Ever since, plethora of papers ...
  • Deep dictionary learning 
    Singhal, Vanika; Majumdar, Angshul (Advisor) (IIIT-Delhi, 2019-08)
    Currently there are three basic frameworks in deep learning - stacked autoencoders (SAE), deep belief network (DBN) and convolutional neural network (CNN); SAE and DBN can be applied to arbitrary inputs but CNN can only ...
  • Deep transform learning 
    Maggu, Jyoti; Majumdar, Angshul (Advisor) (IIIT-Delhi, 2019-12)
    Conventional dictionary learning is a synthesis formulation; it learns a dictionary to generate/synthesize the data from the learned coefficients. Transform learning is its analysis equivalent. The transform analyzes ...
  • Demand-side analytics for sustainable energy 
    Gaur, Megha; Majumdar, Angshul (Advisor) (IIITD-Delhi, 2019-10-01)
    The rapidly growing demand for energy poses one of the biggest challenges in our society. This challenge is critical not just for the power utilities but also for the environment as it leads to increased carbon footprint ...
  • Dictionary based time series modelling 
    Sharma, Shalini; Majumdar, Angshul (Advisor) (IIIT-Delhi, 2022-09)
    Time series analytics is the practice of determining future values of correlated signals. In seminal works, time series were modeled using classical techniques such as ARMA (autoregressive moving average), and its variants ...
  • Feature engineering for low dimensional representation of genes’ expression and pathological activities across diverse human tissues 
    Rai, Priyadarshini; Sengupta, Debarka (Advisor); Majumdar, Angshul (Advisor) (IIIT-Delhi, 2022-10)
    The advent of tissue and single cell based transcriptomic profiling technologies has allowed precise characterization of tissue specific gene activities in the context of development and disease. Human cells express about ...
  • Information fusion using convolutional transform learning 
    Gupta, Pooja; Majumdar, Angshul (Advisor) (IIIT-Delhi, 2023-09)
    There are many real-world problems pertaining to the need for the fusion of information from multiple sources. Consider, for example, the problem of demand forecasting that requires estimating the power consumption at a ...
  • Latent factor models for collaborative filtering 
    Gogna, Anuprriya; Majumdar, Angshul (Advisor) (IIIT-Delhi, 2017-04)
    The enormous growth in online availability of information content has made Recommender Systems (RS) an integral part of most online portals and e-commerce sites. Most websites and service portals, be it movie rental services, ...
  • Learning algorithms for non intrusive load monitoring 
    Singh, Shikha; Majumdar, Angshul (Advisor) (IIIT-Delhi, 2022-07)
    Non-Intrusive Load Monitoring (NILM), also known as Energy Disaggregation, is the process of segregating a building’s total electricity consumption into its appliances by appliance consumption from the smart meter data. ...
  • Matrix completion techniques in bioinformatics 
    Mongia, Aanchal; Majumdar, Angshul (Advisor) (IIIT-Delhi, 2020-09)
    Data analytics and computational techniques applied to biological sciences aid rapid technological advances, swift discoveries, and reliable analysis. A broad range of bountiful tools and algorithms have played pivotal ...
  • Random forest of imputation trees (RITS) for sparse single cell genomics data 
    Sharma, Rachesh; Majumdar, Angshul (Advisor); Kumar, Vibhor (Advisor) (IIIT-Delhi, 2019-04)
    A human body has billions of cells specialized with their own function and each cell carries genome in its nucleus. The activity of the genome is controlled by a multitude of molecular complexes called as epigenome. Previously ...
  • Regularized autoencoders 
    Gupta, Kavya; Majumdar, Angshul (Advisor) (2016-11-09)
    Autoencoders are Neural Networks trained in order to map input to its output. Autoencoders are designed to enable the network to copy input to output as close as possible. Following this, the network will be able to learn ...
  • Robust autoencoders 
    Mehta, Janki; Majumdar, Angshul (Advisor) (2016-09-13)
    An autoencoder is an artificial neural network used for learning efficient codings. The aim of an autoencoder is to learn a representation of data, which can then be used for better classification or any such application. ...
  • Solving matrix factorization from lower dimensional projections : applications in collaborative filtering and magnetic resonance imaging 
    Rajani, Anuj; Majumdar, Angshul (Advisor) (2014-07-10)
    Low-rank matrix factorization finds applications in large number of problems in signal processing and machine learning. Stochastic gradient descent (SGD) is a standard technique for solving large scale matrix factorization ...
  • Sparse recovery techniques for hyperspectral imaging 
    Aggarwal, Hemant Kumar; Majumdar, Angshul (Advisor) (2016-12-23)
    Human vision is a powerful imaging system that can capture and interpret light energy coming from different sources although it is limited to visible light. There are various applications such as face recognition, medical ...

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