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Now showing items 1-9 of 9

TweetPap: a dataset to study the social media discourse of scientific papers

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 ...
ShuffleBlock: Shuffle to regularize deep convolutional neural networks

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. ...
Scalable estimation of epidemic thresholds via node sampling

Dasgupta, Anirban; Sengupta, Srijan (Springer Nature, 2021-06)

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, ...
Permissive runtime information flow control in the presence of exceptions

Bichhawat, Abhishek; Rajani, Vineet; Garg, Deepak; Hammer, Christian (IOS Press, 2021-06)
A parameterized perspective on protecting elections

Dey, Palash; Misra, Neeldhara; Nath, Swaprava; Shakya, Garima (Elsevier, 2021-06)

We study the parameterized complexity of the Optimal Defense and Optimal Attack problems in voting. In both the problems, the input is a set of voter groups (every voter group is a district consisting of a set of votes) ...
On adversarial robustness of synthetic code generation

Anand, Mrinal; Kayal, Pratik; Singh, Mayank (Cornell University Library, 2021-06)

Automatic code synthesis from natural language descriptions is a challenging task. We witness massive progress in developing code generation systems for domain-specific languages (DSLs) employing sequence-to-sequence deep ...
Challenges and considerations with code-mixed NLP for multilingual societies

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 ...
BRAIN2DEPTH: lightweight CNN model for classification of cognitive states from EEG recordings

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 ...
Bayesian deep learning hyperparameter search for robust function mapping to polynomials with noise

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 ...

Now showing items 1-9 of 9

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