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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 ...
  • 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)
  • Chierichetti, Flavio; Dasgupta, Anirban; Kumar, Ravi (Cornell University Library, 2020-10)
  • 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 ...

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