Browsing E-print Articles by Title

Browsing E-print Articles by Title

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  • Anand, Mrinal; Garg, Aditya (Cornell University Library, 2021-11)
    We witnessed a massive growth in the supervised learning paradigm in the past decade. Supervised learning requires a large amount of labeled data to reach state-of-the-art performance. However, labeling the samples requires ...
  • Misra, Neeldhara; Mittal, Harshil; Sethia, Aditi (Cornell University Library, 2020-05)
    Given a set R of red points and a set B of blue points in the plane, the Red-Blue point separation problem asks if there are at most k lines that separate R from B, that is, each cell induced by the lines of the solution ...
  • Hazra, Rima; Singh, Mayank; Goyal, Pawan; Adhikari, Bibhas; Mukherjee, Animesh (Cornell University Library, 2019-08)
    The distinction between sciences is becoming increasingly more artificial -- an approach from one area can be easily applied to the other. More exciting research nowadays is happening perhaps at the interfaces of disciplines ...
  • Choudhari, Jayesh; Dasgupta, Anirban; Misra, Neeldhara; Ramanujan, M. S. (Cornell University Library, 2017-05)
  • Dasgupta, Anirban; Sengupta, Srijan (Cornell University Library, 2020-07)
    Infectious or contagious diseases can be transmitted from one person to another through social contact networks. In today's interconnected global society, such contagion processes can cause global public health hazards, ...
  • Garg, Ayush; Kagi, Sammed Shantinath; Singh, Mayank (Cornell University Library, 2020-06)
  • Sadekar, Kaustubh; Tiwari, Ashish; Raman, Shanmuganathan (Cornell University Library, 2021-07)
    While recent learning based methods have been observed to be superior for several vision-related applications, their potential in generating artistic effects has not been explored much. One such interesting application is ...
  • Kumawat, Sudhakar; Kanojia, Gagan; Raman, Shanmuganathan (Cornell University Library, 2021-06)
    Deep neural networks have enormous representational power which leads them to overfit on most datasets. Thus, regularizing them is important in order to reduce overfitting and enhance their generalization capabilities. ...
  • Gupta, Manoj; Khan, Shahbaz (Cornell University Library, 2018-04)
  • Das, Bireswar; Sharma, Shivdutt; Vaidyanathan, P. R. (Cornell University Library, 2020-02)
  • Garg, Dinesh; Kakkar, Vishal; Shevade, Shirish Krishnaj; Sundararajan, S. (Cornell University Library, 2016-12)
    AUC (Area under the ROC curve) is an important performance measure for applications where the data is highly imbalanced. Learning to maximize AUC performance is thus an important research problem. Using a max-margin based ...
  • Dhakal, Aditya; Cho, Junguk; Kulkarni, Sameer G.; Ramakrishnan, K. K.; Sharma, Puneet (Cornell University Library, 2020-08)
    GPUs are used for training, inference, and tuning the machine learning models. However, Deep Neural Network (DNN) vary widely in their ability to exploit the full power of high-performance GPUs. Spatial sharing of GPU ...
  • Adhikary, Rishiraj; Lodhavia, Dhruvi; Francis, Chris; Patil, Rohit; Srivastava, Tanmay; Khanna, Prerna; Batra, Nipun; Breda, Joe; Peplinski, Jacob; Patel, Shwetak (Cornell University Library, 2022-01)
    According to the World Health Organisation (WHO), 235 million people suffer from respiratory illnesses and four million people die annually due to air pollution. Regular lung health monitoring can lead to prognoses about ...
  • Sadasivan, Vinu Sankar; Dasgupta, Anirban (Cornell University Library, 2021-02)
    Curriculum learning is a training strategy that sorts the training examples by some measure of their difficulty and gradually exposes them to the learner to improve the network performance. In this work, we propose two ...
  • Chhaya, Rachit; Choudhari, Jayesh; Dasgupta, Anirban; Shit, Supratim (Cornell University Library, 2020-06)
  • Malaviya, Jayesh (Cornell University Library, 2021-04)
    The event sequence of many diverse systems is represented as a sequence of discrete events in a continuous space. Examples of such an event sequence are earthquake aftershock events, financial transactions, e-commerce ...
  • Dash, Saloni; Dutta, Ritik; Guyon, Isabelle; Pavao, Adrien; Yale, Andrew; Bennett, Kristin P. (Cornell University Library, 2019-11)
    Synthetic medical data which preserves privacy while maintaining utility can be used as an alternative to real medical data, which has privacy costs and resource constraints associated with it. At present, most models focus ...
  • Desai, Harsh; Kayal, Pratik; Singh, Mayank (Cornell University, 2021-05)
    Information Extraction (IE) from the tables present in scientific articles is challenging due to complicated tabular representations and complex embedded text. This paper presents TabLeX, a large-scale benchmark dataset ...
  • Singh, Prajwal; Sadekar, Kaustubh; Raman, Shanmuganathan (Cornell University Library, 2021-10)
    Point cloud is an efficient way of representing and storing 3D geometric data. Deep learning algorithms on point clouds are time and memory efficient. Several methods such as PointNet and FoldingNet have been proposed for ...
  • Jain, Naman; Singh, Mayank (Cornell University Library, 2021-06)
    Nowadays, researchers have moved to platforms like Twitter to spread information about their ideas and empirical evidence. Recent studies have shown that social media affects the scientific impact of a paper. However, these ...

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