Browsing E-print Articles by Title

Browsing E-print Articles by Title

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  • Harilal, Nidhin; Bhatia, Udit; Ganguly, Auroop R. (Cornell University Library, 2021-06)
    Advances in neural architecture search, as well as explainability and interpretability of connectionist architectures, have been reported in the recent literature. However, our understanding of how to design Bayesian Deep ...
  • Goyal, Navin; Gupta, Manoj (Cornell University Library, 2016-01)
    In their seminal paper [Sleator and Tarjan, J.ACM, 1985], the authors conjectured that the splay tree is dynamically optimal binary search tree (BST). In spite of decades of intensive research, the problem remains open. ...
  • Jain, Harshil; Patil, Rohit; Mastan, Indra Deep; Raman, Shanmuganathan (Cornell University Library, 2020-11)
    Blind motion deblurring involves reconstructing a sharp image from an observation that is blurry. It is a problem that is ill-posed and lies in the categories of image restoration problems. The training data-based methods ...
  • Jain, Naman; Chauhan, Ankush; Chewale, Atharva; Mithbavkar, Ojas; Shah, Ujjaval; Singh, Mayank (Cornell University Library, 2020-07)
  • Pandey, Pankaj; Miyapuram, Krishna Prasad (Cornell University Library, 2021-06)
    Several Convolutional Deep Learning models have been proposed to classify the cognitive states utilizing several neuro-imaging domains. These models have achieved significant results, but they are heavily designed with ...
  • Srivastava, Vivek; Singh, Mayank (Cornell University Library, 2021-06)
    Multilingualism refers to the high degree of proficiency in two or more languages in the written and oral communication modes. It often results in language mixing, a.k.a. code-mixing, when a multilingual speaker switches ...
  • Singh, Shruti; Singh, Mayank; Goyal, Pawan (Cornell University Library, 2021-08)
    Comparing research papers is a conventional method to demonstrate progress in experimental research. We present COMPARE, a taxonomy and a dataset of comparison discussions in peer reviews of research papers in the domain ...
  • Dey, Palash; Misra, Neeldhara (Cornell University Library, 2016-04)
    The Coalitional Manipulation problem has been studied extensively in the literature for many voting rules. However, most studies have focused on the complete information setting, wherein the manipulators know the votes of ...
  • 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 ...
  • Mastan, Indra Deep; Raman, Shanmuganathan (Cornell University Library, 2020-11)
    Recently, there is a vast interest in developing image feature learning methods that are independent of the training data, such as deep image prior, InGAN, SinGAN, and DCIL. These methods are unsupervised and are used to ...
  • Mastan, Indra Deep; Raman, Shanmuganathan (Cornell University Library, 2020-12)
    One of the major challenges of style transfer is the appropriate image features supervision between the output image and the input (style and content) images. An efficient strategy would be to define an object map between ...
  • Singh, Shubham Kumar; Miyapuram, Krishna P.; Raman, Shanmuganathan (Cornell University Library, 2019-11)
    Person re-identification aims to associate images of the same person over multiple non-overlapping camera views at different times. Depending on the human operator, manual re-identification in large camera networks is ...
  • Kumawat, Sudhakar; Verma, Manisha; Nakashima, Yuta; Raman, Shanmuganathan (Cornell University Library, 2020-07)
    Conventional 3D convolutional neural networks (CNNs) are computationally expensive, memory intensive, prone to overfitting, and most importantly, there is a need to improve their feature learning capabilities. To address ...
  • 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 ...
  • Mastan, Indra Deep; Raman, Shanmuganathan (Cornell University Library, 2020-12)
    We consider the generic deep image enhancement problem where an input image is transformed into a perceptually better-looking image. Recent methods for image enhancement consider the problem by performing style transfer ...
  • 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 ...
  • Banerjee, Suman; Pal, Bithika (Cornell University Library, 2020-04)
    Given a graph, and a set of query vertices (subset of the vertices), the dynamic skyline query problem returns a subset of data vertices (other than query vertices) which are not dominated by other data vertices based on ...
  • Banerjee, Suman; Jenamani, Mamata; Pratihar, Dilip Kumar (Cornell University Library, 2020-04)
    Given a social network with nonuniform selection cost of the users, the problem of \textit{Budgeted Influence Maximization} (BIM in short) asks for selecting a subset of the nodes within an allocated budget for initial ...
  • Doshi, Ishita; Sajjalla, Sreekalyan; Choudhari, Jayesh; Bhatt, Rushi; Dasgupta, Anirban (Cornell University Library, 2020-08)
    We address the problem of large scale real time classification of content posted on social networks, along with the need to rapidly identify novel spam types. Obtaining manual labels for user generated content using editorial ...

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