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  • Jia,Yiling; Batra,Nipun; Wang,Hongning (Cornell University Library, 2019-09)
    Residential homes constitute roughly one-fourth of the total energy usage worldwide. Providing appliance-level energy breakdown has been shown to induce positive behavioral changes that can reduce energy consumption by ...
  • Agarwal, Deepesh; Srivastava, Pravesh; Martin-del-Campo, Sergio; Natarajan, Balasubramaniam; Srinivasan, Babji (Cornell University Library, 2021-10)
    Active Learning (AL) is a powerful tool to address modern machine learning problems with significantly fewer labeled training instances. However, implementation of traditional AL methodologies in practical scenarios is ...
  • Lawhatre, Prashant; Shiraguppi, Bharatesh R.; Sharma, Esha; Miyapuram, Krishna Prasad; Lomas, Derek (Cornell University Library, 2020-10)
    This research study aims to use machine learning methods to characterize the EEG response to music. Specifically, we investigate how resonance in the EEG response correlates with individual aesthetic enjoyment. Inspired ...
  • Bedathur, Srikanta; Bhattacharya, Indrajit; Choudhari, Jayesh; Dasgupta, Anirban (Cornell University Library, 2018-09)
    Social media conversations unfold based on complex interactions between users, topics and time. While recent models have been proposed to capture network strengths between users, users' topical preferences and temporal ...
  • Jain, Harshil; Agarwal, Akshat; Shridhar, Kumar; Kleyko, Denis (Cornell University Library, 2020-10)
    Deep neural networks have demonstrated their superior performance in almost every Natural Language Processing task, however, their increasing complexity raises concerns. In particular, these networks require high expenses ...
  • Sai, M. (Indian Institute of Technology Gandhinagar, 2018)
  • Nath, Biplob; Barai, Samit; Kumar, Pardeep; Srinivasan, Babji; Mohapatra, Nihar Ranjan (Institute of Electrical and Electronics Engineers, 2021-08)
    This work proposes a methodology to find lithography yield detractors using Design Rule Checks (DRC) that are derived from a supervised Machine Learning (ML) model. The probability of being an outlier in layout parameter ...
  • Chierichetti, Flavio; Dasgupta, Anirban; Kumar, Ravi (Cornell University Library, 2020-10)
  • 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 ...
  • 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 ...
  • 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 ...
  • 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 ...

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