• Causal discovery toolbox: uncovering causal relationships in Python 

  Kalainathan, Diviyan; Goudet, Olivier; Dutta, Ritik (Journal of Machine Learning Research, 2020-03)

  This paper presents a new open source Python framework for causal discovery from observational data and domain background knowledge, aimed at causal graph and causal mechanism modeling. The cdt package implements an ...
• Space efficient representations of finite groups 

  Das, Bireswar; Sharma, Shivdutt; Vaidyanathan, P. R. (Cornell University Library, 2020-02)
• On the parameterized complexity of spanning trees with small vertex covers 

  Kaur, Chamanvir; Misra, Neeldhara (13-02-20)
• Effect of feature hashing on fair classification 

  Dutta, Ritik; Gohil, Varun; Jain, Atishay (05-01-20)
• Weakly-supervised deep learning for domain invariant sentiment classification 

  Kayal, Pratik; Singh, Mayank; Goyal, Pawan (2020-01-05)
• PlantDoc: a dataset for visual plant disease detection 

  Singh, Davinder; Jain, Naman; Jain, Pranjali; Kayal, Pratik; Kumawat, Sudhakar; Batra, Nipun (2020-01-05)
• Innovation and revenue: deep diving into the temporal rank-shifts of fortune 500 companies 

  Singh, Mayank; Pal, Arindam; Dey, Lipika; Mukherjee, Animesh (2020-01-05)
• Understanding the political inclination of WhatsApp chats 

  Yadav, Anshuman; Garg, Aditya; Aglawe, Anup; Agarwal, Ayush; Srivastava, Vivek (2020-01-05)
• Estimation of PM2.5 using satellite and meteorological data 

  Roy, Souvik; Batra, Nipun; Gupta, Pawan (2020-01-05)
• Depthwise-STFT based separable Convolutional Neural Networks 

  Kumawat, Sudhakar; Raman, Shanmuganathan (Cornell University Library, 2020-01)

  In this paper, we propose a new convolutional layer called Depthwise-STFT Separable layer that can serve as an alternative to the standard depthwise separable convolutional layer. The construction of the proposed layer is ...
• CARO: an empathetic health conversational chatbot for people with major depression 

  Harilal, Nidhin; Shah, Rushil; Sharma, Saumitra; Bhutani, Vedanta (2020-01-05)
• Lessons and insights from super-resolution of energy data 

  Kukunuri, Rithwik; Batra, Nipun; Wang, Hongning (2020-01-05)
• Depthwise-STFT based separable convolutional neural networks 

  Kumawat, Sudhakar; Raman, Shanmuganathan (2020-05-04)
• Solar energy forecasting using machine learning 

  Kumar, Karan; Batra, Nipun (2020-01-05)
• Active learning for air quality station location recommendation 

  Narayanan, S. Deepak; Agnihotri, Apoorv; Batra, Nipun (2020-01-05)
• Analysis of reference and citation copying in evolving bibliographic networks 

  Pandey, Pradumn Kumar; Singh, Mayank; Goyal, Pawan; Mukherjee, Animesh; Chakrabarti, Soumen (Cornell University Library, 2019-12)

  Extensive literature demonstrates how the copying of references (links) canlead to the emergence of various structural properties (e.g., power-law degreedistribution and bipartite cores) in bibliographic and other similar ...
• Analysis of reference and citation copying in evolving bibliographic networks 

  Pandey, Pradumn Kumar; Singh, Mayank; Goyal, Pawan; Mukherjee, Animesh; Chakrabarti, Soumen (Elsevier, 2019-12)
• DCIL: Deep Contextual Internal Learning for image restoration and image retargeting 

  Mastan, Indra Deep; Raman, Shanmuganathan (Cornell University Library, 2019-12)

  Recently, there is a vast interest in developing methods which are independent of the training samples such as deep image prior, zero-shot learning, and internal learning. The methods above are based on the common goal of ...
• DCIL: Deep Contextual Internal Learning for image restoration and image retargeting 

  Mastan, Indra Deep; Raman, Shanmuganathan (02-03-20)
• NLPExplorer: exploring the universe of NLP papers 

  Parmar, Monarch; Jain, Naman; Jain, Pranjali; Sahit, P. Jayakrishna; Pachpande, Soham; Singh, Shruti; Singh, Mayank (14-04-20)

View more