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General Multi-label Image Classification with Transformers
Multi-label image classification is the task of predicting a set of labe...
11/27/2020 ∙ by Jack Lanchantin, et al. ∙
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Reevaluating Adversarial Examples in Natural Language
State-of-the-art attacks on NLP models have different definitions of wha...
04/25/2020 ∙ by John X. Morris, et al. ∙
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Neural Message Passing for Multi-Label Classification
Multi-label classification (MLC) is the task of assigning a set of targe...
04/17/2019 ∙ by Jack Lanchantin, et al. ∙
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Exploring the Naturalness of Buggy Code with Recurrent Neural Networks
Statistical language models are powerful tools which have been used for ...
03/21/2018 ∙ by Jack Lanchantin, et al. ∙
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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 s...
01/13/2018 ∙ by Ji Gao, et al. ∙
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Prototype Matching Networks for Large-Scale Multi-label Genomic Sequence Classification
One of the fundamental tasks in understanding genomics is the problem of...
10/30/2017 ∙ by Jack Lanchantin, et al. ∙
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Attend and Predict: Understanding Gene Regulation by Selective Attention on Chromatin
The past decade has seen a revolution in genomic technologies that enabl...
08/01/2017 ∙ by Ritambhara Singh, et al. ∙
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GaKCo: a Fast GApped k-mer string Kernel using COunting
String Kernel (SK) techniques, especially those using gapped k-mers as f...
04/24/2017 ∙ by Ritambhara Singh, et al. ∙
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Memory Matching Networks for Genomic Sequence Classification
When analyzing the genome, researchers have discovered that proteins bin...
02/22/2017 ∙ by Jack Lanchantin, et al. ∙
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Deep Motif Dashboard: Visualizing and Understanding Genomic Sequences Using Deep Neural Networks
Deep neural network (DNN) models have recently obtained state-of-the-art...
08/12/2016 ∙ by Jack Lanchantin, et al. ∙
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