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Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks
Although deep neural networks (DNNs) have achieved great success in many tasks, they can often be fooled by \emph{adversarial examples} that are generated by adding small but purposeful distortionsExpand
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Black-Box Generation of Adversarial Text Sequences to Evade Deep Learning Classifiers
Although various techniques have been proposed to generate adversarial samples for white-box attacks on text, little attention has been paid to a black-box attack, which is a more realistic scenario.Expand
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Learning to rank with (a lot of) word features
In this article we present Supervised Semantic Indexing which defines a class of nonlinear (quadratic) models that are discriminatively trained to directly map from the word content in aExpand
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Automatically Evading Classifiers: A Case Study on PDF Malware Classifiers
Machine learning is widely used to develop classifiers for security tasks. However, the robustness of these methods against motivated adversaries is uncertain. In this work, we propose a genericExpand
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Prediction of Interactions Between HIV-1 and Human Proteins by Information Integration
Human immunodeficiency virus-1 (HIV-1) in acquired immune deficiency syndrome (AIDS) relies on human host cell proteins in virtually every aspect of its life cycle. Knowledge of the set ofExpand
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Sentiment classification based on supervised latent n-gram analysis
In this paper, we propose an efficient embedding for modeling higher-order (n-gram) phrases that projects the n-grams to low-dimensional latent semantic space, where a classification function can beExpand
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Sparse Latent Semantic Analysis
Latent semantic analysis (LSA), as one of the most popular unsupervised dimension reduction tools, has a wide range of applications in text mining and information retrieval. The key idea of LSA is toExpand
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Protein complex identification by supervised graph local clustering
Motivation: Protein complexes integrate multiple gene products to coordinate many biological functions. Given a graph representing pairwise protein interaction data one can search for subgraphsExpand
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A critical assessment of Mus musculus gene function prediction using integrated genomic evidence
Background:Several years after sequencing the human genome and the mouse genome, much remains to be discovered about the functions of most human and mouse genes. Computational prediction of geneExpand
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DeepChrome: deep-learning for predicting gene expression from histone modifications
MOTIVATION Histone modifications are among the most important factors that control gene regulation. Computational methods that predict gene expression from histone modification signals are highlyExpand
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