Paper Citation Record · LEDGER

Learning Credible Deep Neural Networks with Rationale Regularization

As of 19 August 2026, this Paper Citation Record lists 45 of 45 outbound references and 0 inbound Pith citation observations for arXiv:1908.05601.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
1908.05601 v1

Coverage vector

measured 45 of 45 reference resolution

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measured 45 of 45 standing notices

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Pith citing papers itemized under the disclosed page cap.

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A source-named dated measurement, never combined with another source.

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Reference resolution

45 of 45 outbound references displayed

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External citation measurements

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Outbound references

Observation 9398a4ec-1d00-4f91-99e9-c7fa612facd8 · outbound

This paper cites Methods for interpreting and understanding deep neural networks,.

Learning Credible Deep Neural Networks with Rationale Regularization Methods for interpreting and understanding deep neural networks,

Reference 1

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This paper cites Techniques for interpretable machine learning,.

Learning Credible Deep Neural Networks with Rationale Regularization Techniques for interpretable machine learning,

Reference 2

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This paper cites Towards explanation of dnn-based prediction with guided feature inversion,.

Learning Credible Deep Neural Networks with Rationale Regularization Towards explanation of dnn-based prediction with guided feature inversion,

Reference 3

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This paper cites On attribution of recurrent neural network predictions via additive decomposition,.

Learning Credible Deep Neural Networks with Rationale Regularization On attribution of recurrent neural network predictions via additive decomposition,

Reference 4

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This paper cites Learning credible models,.

Learning Credible Deep Neural Networks with Rationale Regularization Learning credible models,

Reference 5

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This paper cites Did the model understand the question?.

Learning Credible Deep Neural Networks with Rationale Regularization Did the model understand the question?

Reference 6

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Observation d87631ca-c017-4627-91e6-6cc815d83864 · outbound

This paper cites Does it care what you asked? understanding importance of verbs in deep learning qa system,.

Learning Credible Deep Neural Networks with Rationale Regularization Does it care what you asked? understanding importance of verbs in deep learning qa system,

Reference 7

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Observation a64acb46-1b91-42e0-a35c-064c743408a3 · outbound

This paper cites Why should i trust you?: Explaining the predictions of any classiﬁer,.

Learning Credible Deep Neural Networks with Rationale Regularization Why should i trust you?: Explaining the predictions of any classiﬁer,

Reference 8

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This paper cites Annotation artifacts in natural language inference data,.

Learning Credible Deep Neural Networks with Rationale Regularization Annotation artifacts in natural language inference data,

Reference 9

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Observation 97573f56-e70a-443f-ac8d-f5d0dc02b47f · outbound

This paper cites Man is to computer programmer as woman is to homemaker? debiasing word embeddings,.

Learning Credible Deep Neural Networks with Rationale Regularization Man is to computer programmer as woman is to homemaker? debiasing word embeddings,

Reference 10

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Observation ce46a128-d364-43a7-856b-705e431ff1a9 · outbound

This paper cites Swag: A large-scale adversarial dataset for grounded commonsense inference,.

Learning Credible Deep Neural Networks with Rationale Regularization Swag: A large-scale adversarial dataset for grounded commonsense inference,

Reference 11

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Observation dbee6eb1-2033-4ae6-932b-3caef965ee6f · outbound

This paper cites Harnessing deep neural networks with logic rules,.

Learning Credible Deep Neural Networks with Rationale Regularization Harnessing deep neural networks with logic rules,

Reference 12

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Observation f436f5fd-ad9b-4e7f-b3b1-a55df422c657 · outbound

This paper cites Knowledgeable reader: Enhancing cloze- style reading comprehension with external commonsense knowledge,.

Learning Credible Deep Neural Networks with Rationale Regularization Knowledgeable reader: Enhancing cloze- style reading comprehension with external commonsense knowledge,

Reference 13

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This paper cites Rationale-augmented convo- lutional neural networks for text classiﬁcation,.

Learning Credible Deep Neural Networks with Rationale Regularization Rationale-augmented convo- lutional neural networks for text classiﬁcation,

Reference 14

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Observation 64e6a360-744e-467b-a040-3ddbcdf85035 · outbound

This paper cites Using annotator rationales to improve machine learning for text categorization,.

Learning Credible Deep Neural Networks with Rationale Regularization Using annotator rationales to improve machine learning for text categorization,

Reference 15

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Observation 85a30795-218f-4cf1-aefd-4bb9bd079dab · outbound

This paper cites Rationalizing neural predictions,.

Learning Credible Deep Neural Networks with Rationale Regularization Rationalizing neural predictions,

Reference 16

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Observation fe50bf51-b741-492e-941d-ab1e3a28f3c3 · outbound

This paper cites Annotator rationales for visual recogni- tion,.

Learning Credible Deep Neural Networks with Rationale Regularization Annotator rationales for visual recogni- tion,

Reference 17

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This paper cites Why is that relevant? collecting annotator rationales for relevance judgments,.

Learning Credible Deep Neural Networks with Rationale Regularization Why is that relevant? collecting annotator rationales for relevance judgments,

Reference 18

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This paper cites Towards A Rigorous Science of Interpretable Machine Learning.

Learning Credible Deep Neural Networks with Rationale Regularization Towards A Rigorous Science of Interpretable Machine Learning

Reference 19

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This paper cites Evaluating Explanation Without Ground Truth in Interpretable Machine Learning.

Learning Credible Deep Neural Networks with Rationale Regularization Evaluating Explanation Without Ground Truth in Interpretable Machine Learning

Reference 20

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This paper cites Representation interpretation with spatial encoding and multimodal analytics,.

Learning Credible Deep Neural Networks with Rationale Regularization Representation interpretation with spatial encoding and multimodal analytics,

Reference 21

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Observation ca09b4b7-1cc0-4c90-a2ab-6d2336e49fc5 · outbound

This paper cites Don’t just assume; look and answer: Overcoming priors for visual question answering,.

Learning Credible Deep Neural Networks with Rationale Regularization Don’t just assume; look and answer: Overcoming priors for visual question answering,

Reference 22

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This paper cites Women also snowboard: Overcoming bias in captioning models,.

Learning Credible Deep Neural Networks with Rationale Regularization Women also snowboard: Overcoming bias in captioning models,

Reference 23

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This paper cites Overcoming language priors in visual question answering with adversarial regularization,.

Learning Credible Deep Neural Networks with Rationale Regularization Overcoming language priors in visual question answering with adversarial regularization,

Reference 24

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This paper cites Know what you don’t know: Unan- swerable questions for squad,.

Learning Credible Deep Neural Networks with Rationale Regularization Know what you don’t know: Unan- swerable questions for squad,

Reference 25

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Observation e8feac95-5715-49e2-ac77-3a59048bd545 · outbound

This paper cites Sequence classiﬁcation with human attention,.

Learning Credible Deep Neural Networks with Rationale Regularization Sequence classiﬁcation with human attention,

Reference 26

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This paper cites Deriving machine attention from human rationales,.

Learning Credible Deep Neural Networks with Rationale Regularization Deriving machine attention from human rationales,

Reference 27

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This paper cites Understanding Neural Networks through Representation Erasure.

Learning Credible Deep Neural Networks with Rationale Regularization Understanding Neural Networks through Representation Erasure

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This paper cites Representation of linguistic form and function in recurrent neural networks,.

Learning Credible Deep Neural Networks with Rationale Regularization Representation of linguistic form and function in recurrent neural networks,

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Learning Credible Deep Neural Networks with Rationale Regularization Interpretable structure induc- tion via sparse attention,

Reference 30

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Learning Credible Deep Neural Networks with Rationale Regularization Sparse and constrained attention for neural machine translation,

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This paper cites The Mythos of Model Interpretability.

Learning Credible Deep Neural Networks with Rationale Regularization The Mythos of Model Interpretability

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Learning Credible Deep Neural Networks with Rationale Regularization Convolutional neural networks for sentence classiﬁcation,

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Learning Credible Deep Neural Networks with Rationale Regularization Long short-term memory,

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Learning Credible Deep Neural Networks with Rationale Regularization A structured self-attentive sentence embedding,

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Learning Credible Deep Neural Networks with Rationale Regularization Distilling the Knowledge in a Neural Network

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This paper cites A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts,.

Learning Credible Deep Neural Networks with Rationale Regularization A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts,

Reference 37

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Learning Credible Deep Neural Networks with Rationale Regularization Learning attitudes and attributes from multi-aspect reviews,

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Learning Credible Deep Neural Networks with Rationale Regularization Improving neural machine translation models with monolingual data,

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Learning Credible Deep Neural Networks with Rationale Regularization Investigating Backtranslation in Neural Machine Translation

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This paper cites Distributed representations of words and phrases and their composition- ality,.

Learning Credible Deep Neural Networks with Rationale Regularization Distributed representations of words and phrases and their composition- ality,

Reference 41

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This paper cites Adam: A Method for Stochastic Optimization.

Learning Credible Deep Neural Networks with Rationale Regularization Adam: A Method for Stochastic Optimization

Reference 42

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This paper cites Dropout: a simple way to prevent neural networks from overﬁt- ting,.

Learning Credible Deep Neural Networks with Rationale Regularization Dropout: a simple way to prevent neural networks from overﬁt- ting,

Reference 43

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This paper cites Adversarially regularising neural nli models to integrate logical background knowledge,.

Learning Credible Deep Neural Networks with Rationale Regularization Adversarially regularising neural nli models to integrate logical background knowledge,

Reference 44

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This paper cites Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales,.

Learning Credible Deep Neural Networks with Rationale Regularization Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales,

Reference 45

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Pith citing papers

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