diff --git a/.github/citation/citation.json b/.github/citation/citation.json index 49ccd1a..5fbbe3a 100644 --- a/.github/citation/citation.json +++ b/.github/citation/citation.json @@ -1 +1 @@ -{"A Semantic Loss Function for Deep Learning with Symbolic Knowledge": {"citation": 502, "last update": "2024-07-04"}, "Neurologic decoding:(un) supervised neural text generation with predicate logic constraints": {"citation": 118, "last update": "2024-07-04"}, "A review of some techniques for inclusion of domain-knowledge into deep neural networks": {"citation": 129, "last update": "2024-07-04"}, "Informed machine learning-a taxonomy and survey of integrating prior knowledge into learning systems": {"citation": 687, "last update": "2024-07-04"}, "The Connectionist Inductive Learning and Logic Programming System": {"citation": 248, "last update": "2024-07-04"}, "Harnessing Deep Neural Networks with Logic Rules": {"citation": 760, "last update": "2024-07-04"}, "Deep Learning with Logical Constraints": {"citation": 56, "last update": "2024-07-04"}, "A Pseudo-Semantic Loss for Autoregressive Models with Logical Constraints": {"citation": 1, "last update": "2024-07-05"}, "Hyper-class Augmented and Regularized Deep Learning for Fine-grained Image Classification": {"citation": 232, "last update": "2024-07-05"}, "Injecting Logical Constraints into Neural Networks via Straight-Through Estimators": {"citation": 22, "last update": "2024-07-05"}, "Rewriting a Deep Generative Model": {"citation": 119, "last update": "2024-07-05"}, "DeepProbLog: Neural Probabilistic Logic Programming": {"citation": 579, "last update": "2024-07-05"}, "Guided Open Vocabulary Image Captioning with Constrained Beam Search": {"citation": 244, "last update": "2024-07-05"}, "DeepEdit: Knowledge Editing as Decoding with Constraints": {"citation": 9, "last update": "2024-07-05"}, "Don't Pour Cereal into Coffee: Differentiable Temporal Logic for Temporal Action Segmentation": {"citation": 21, "last update": "2024-07-05"}, "VerSe: A Vertebrae Labelling and Segmentation Benchmark for Multi-detector CT Images": {"citation": 212, "last update": "2024-07-05"}, "MultiplexNet: Towards Fully Satisfied Logical Constraints in Neural Networks": {"citation": 51, "last update": "2024-07-05"}, "Interpretability Beyond Feature Attribution:Quantitative Testing with Concept Activation Vectors (TCAV)": {"citation": 1886, "last update": "2024-07-05"}, "Label-free Concept Bottleneck Models": {"citation": 85, "last update": "2024-07-05"}, "Language in a Bottle: Language Model Guided Concept Bottlenecks for Interpretable Image Classification": {"citation": 115, "last update": "2024-07-05"}, "Editing a classifier by rewriting its prediction rules": {"citation": 67, "last update": "2024-07-05"}, "Concept Bottleneck Models": {"citation": 663, "last update": "2024-07-05"}, "Interactive Concept Bottleneck Models": {"citation": 36, "last update": "2024-07-05"}, "Promises and Pitfalls of Black-Box Concept Learning Models": {"citation": 74, "last update": "2024-07-05"}, "Addressing Leakage in Concept Bottleneck Models": {"citation": 41, "last update": "2024-07-05"}, "CommonGen: A Constrained Text Generation Challenge for Generative Commonsense Reasoning": {"citation": 321, "last update": "2024-07-05"}, "POST-HOC CONCEPT BOTTLENECK MODELS": {"citation": 137, "last update": "2024-07-05"}, "Dividing and Conquering a BlackBox to a Mixture of Interpretable Models: Route, Interpret, Repeat": {"citation": 6, "last update": "2024-07-05"}} \ No newline at end of file +{"Dividing and Conquering a BlackBox to a Mixture of Interpretable Models: Route, Interpret, Repeat": {"citation": 6, "last update": "2024-07-05"}, "A Semantic Loss Function for Deep Learning with Symbolic Knowledge": {"citation": 503, "last update": "2024-07-06"}, "Neurologic decoding:(un) supervised neural text generation with predicate logic constraints": {"citation": 118, "last update": "2024-07-06"}, "A review of some techniques for inclusion of domain-knowledge into deep neural networks": {"citation": 129, "last update": "2024-07-06"}, "Informed machine learning-a taxonomy and survey of integrating prior knowledge into learning systems": {"citation": 690, "last update": "2024-07-06"}, "The Connectionist Inductive Learning and Logic Programming System": {"citation": 248, "last update": "2024-07-06"}, "Harnessing Deep Neural Networks with Logic Rules": {"citation": 760, "last update": "2024-07-06"}, "Deep Learning with Logical Constraints": {"citation": 56, "last update": "2024-07-06"}, "A Pseudo-Semantic Loss for Autoregressive Models with Logical Constraints": {"citation": 1, "last update": "2024-07-06"}, "Hyper-class Augmented and Regularized Deep Learning for Fine-grained Image Classification": {"citation": 232, "last update": "2024-07-06"}, "Injecting Logical Constraints into Neural Networks via Straight-Through Estimators": {"citation": 22, "last update": "2024-07-06"}, "Rewriting a Deep Generative Model": {"citation": 118, "last update": "2024-07-06"}, "DeepProbLog: Neural Probabilistic Logic Programming": {"citation": 580, "last update": "2024-07-06"}, "Guided Open Vocabulary Image Captioning with Constrained Beam Search": {"citation": 244, "last update": "2024-07-06"}, "DeepEdit: Knowledge Editing as Decoding with Constraints": {"citation": 9, "last update": "2024-07-06"}, "Don't Pour Cereal into Coffee: Differentiable Temporal Logic for Temporal Action Segmentation": {"citation": 21, "last update": "2024-07-06"}, "VerSe: A Vertebrae Labelling and Segmentation Benchmark for Multi-detector CT Images": {"citation": 212, "last update": "2024-07-06"}, "MultiplexNet: Towards Fully Satisfied Logical Constraints in Neural Networks": {"citation": 51, "last update": "2024-07-06"}, "Interpretability Beyond Feature Attribution:Quantitative Testing with Concept Activation Vectors (TCAV)": {"citation": 1886, "last update": "2024-07-06"}, "Label-free Concept Bottleneck Models": {"citation": 84, "last update": "2024-07-06"}, "Language in a Bottle: Language Model Guided Concept Bottlenecks for Interpretable Image Classification": {"citation": 116, "last update": "2024-07-06"}, "Editing a classifier by rewriting its prediction rules": {"citation": 67, "last update": "2024-07-06"}, "Concept Bottleneck Models": {"citation": 663, "last update": "2024-07-06"}, "Interactive Concept Bottleneck Models": {"citation": 36, "last update": "2024-07-06"}, "Promises and Pitfalls of Black-Box Concept Learning Models": {"citation": 74, "last update": "2024-07-06"}, "Addressing Leakage in Concept Bottleneck Models": {"citation": 41, "last update": "2024-07-06"}, "CommonGen: A Constrained Text Generation Challenge for Generative Commonsense Reasoning": {"citation": 322, "last update": "2024-07-06"}, "POST-HOC CONCEPT BOTTLENECK MODELS": {"citation": 137, "last update": "2024-07-06"}} \ No newline at end of file diff --git a/README.md b/README.md index 09a00a9..6d909ff 100644 --- a/README.md +++ b/README.md @@ -14,12 +14,12 @@ A list of awesome resources related to constraint learning | Venue | Title | Affiliation |       Link       |   Source   | | :---: | :---: | :---------: | :---: | :----: | |IJCAI 2022|Deep Learning with Logical Constraints|University of Oxford| [[paper]](https://arxiv.org/pdf/2205.00523.pdf)![Scholar citations](https://img.shields.io/badge/Citations-56-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)|| -|TKDE 2019|Informed machine learning-a taxonomy and survey of integrating prior knowledge into learning systems|Fraunhofer IAIS| [[paper]](https://arxiv.org/pdf/1903.12394.pdf)![Scholar citations](https://img.shields.io/badge/Citations-687-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)|| +|TKDE 2019|Informed machine learning-a taxonomy and survey of integrating prior knowledge into learning systems|Fraunhofer IAIS| [[paper]](https://arxiv.org/pdf/1903.12394.pdf)![Scholar citations](https://img.shields.io/badge/Citations-690-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)|| |Scientific Reports 2022|A review of some techniques for inclusion of domain-knowledge into deep neural networks|| [[paper]](https://www.nature.com/articles/s41598-021-04590-0)![Scholar citations](https://img.shields.io/badge/Citations-129-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)|| ### Benchmark | Venue | Title | Affiliation |       Link       |   Source   | | :---: | :---: | :---------: | :---: | :----: | -|EMNLP 2020|CommonGen: A Constrained Text Generation Challenge for Generative Commonsense Reasoning|USC| [[paper]](https://arxiv.org/pdf/1911.03705.pdf)![Scholar citations](https://img.shields.io/badge/Citations-321-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/INK-USC/CommonGen)![GitHub stars](https://img.shields.io/github/stars/INK-USC/CommonGen.svg?logo=github&label=Stars)| +|EMNLP 2020|CommonGen: A Constrained Text Generation Challenge for Generative Commonsense Reasoning|USC| [[paper]](https://arxiv.org/pdf/1911.03705.pdf)![Scholar citations](https://img.shields.io/badge/Citations-322-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/INK-USC/CommonGen)![GitHub stars](https://img.shields.io/github/stars/INK-USC/CommonGen.svg?logo=github&label=Stars)| |Medical image analysis 2021|VerSe: A Vertebrae Labelling and Segmentation Benchmark for Multi-detector CT Images|Technical University of Munich| [[paper]](https://arxiv.org/abs/2001.09193)![Scholar citations](https://img.shields.io/badge/Citations-212-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/anjany/verse)![GitHub stars](https://img.shields.io/github/stars/anjany/verse.svg?logo=github&label=Stars)| ### Data Augmentation | Venue | Title | Affiliation |       Link       |   Source   | @@ -31,12 +31,12 @@ A list of awesome resources related to constraint learning |NIPS 2023|A Pseudo-Semantic Loss for Autoregressive Models with Logical Constraints|UCLA| [[paper]](https://arxiv.org/pdf/2312.03905.pdf)![Scholar citations](https://img.shields.io/badge/Citations-1-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/UCLA-StarAI/PseudoSL)![GitHub stars](https://img.shields.io/github/stars/UCLA-StarAI/PseudoSL.svg?logo=github&label=Stars)| |ACL 2016|Harnessing Deep Neural Networks with Logic Rules|CMU| [[paper]](https://arxiv.org/pdf/1603.06318.pdf)![Scholar citations](https://img.shields.io/badge/Citations-760-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/ZhitingHu/logicnn)![GitHub stars](https://img.shields.io/github/stars/ZhitingHu/logicnn.svg?logo=github&label=Stars)| |Applied Intelligence 1999|The Connectionist Inductive Learning and Logic Programming System|IC| [[paper]](https://link.springer.com/article/10.1023/A:1008328630915)![Scholar citations](https://img.shields.io/badge/Citations-248-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)|| -|ICML 2018|A Semantic Loss Function for Deep Learning with Symbolic Knowledge|UCLA| [[paper]](https://proceedings.mlr.press/v80/xu18h/xu18h.pdf)![Scholar citations](https://img.shields.io/badge/Citations-502-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/UCLA-StarAI/Semantic-Loss)![GitHub stars](https://img.shields.io/github/stars/UCLA-StarAI/Semantic-Loss.svg?logo=github&label=Stars)| +|ICML 2018|A Semantic Loss Function for Deep Learning with Symbolic Knowledge|UCLA| [[paper]](https://proceedings.mlr.press/v80/xu18h/xu18h.pdf)![Scholar citations](https://img.shields.io/badge/Citations-503-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/UCLA-StarAI/Semantic-Loss)![GitHub stars](https://img.shields.io/github/stars/UCLA-StarAI/Semantic-Loss.svg?logo=github&label=Stars)| |NAACL 2021|Neurologic decoding:(un) supervised neural text generation with predicate logic constraints|UW| [[paper]](https://arxiv.org/pdf/2010.12884.pdf)![Scholar citations](https://img.shields.io/badge/Citations-118-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/GXimingLu/neurologic_decoding)![GitHub stars](https://img.shields.io/github/stars/GXimingLu/neurologic_decoding.svg?logo=github&label=Stars)| |EMNLP 2017|Guided Open Vocabulary Image Captioning with Constrained Beam Search|The Australian National University| [[paper]](https://aclanthology.org/D17-1098.pdf)![Scholar citations](https://img.shields.io/badge/Citations-244-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/nocaps-org/updown-baseline)![GitHub stars](https://img.shields.io/github/stars/nocaps-org/updown-baseline.svg?logo=github&label=Stars)| |NIPS 2022|Don't Pour Cereal into Coffee: Differentiable Temporal Logic for Temporal Action Segmentation|NUS| [[paper]](https://diff-tl.github.io/assets/docs/dtl_neurips2022.pdf)![Scholar citations](https://img.shields.io/badge/Citations-21-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/ZiweiXU/DTL-action-segmentation)![GitHub stars](https://img.shields.io/github/stars/ZiweiXU/DTL-action-segmentation.svg?logo=github&label=Stars)| |AAAI 2021|MultiplexNet: Towards Fully Satisfied Logical Constraints in Neural Networks|University of Edinburgh| [[paper]](https://arxiv.org/pdf/2111.01564.pdf)![Scholar citations](https://img.shields.io/badge/Citations-51-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/NickHoernle/semantic_loss)![GitHub stars](https://img.shields.io/github/stars/NickHoernle/semantic_loss.svg?logo=github&label=Stars)| -|NIPS 2018|DeepProbLog: Neural Probabilistic Logic Programming|KU Leuven| [[paper]](https://arxiv.org/pdf/1805.10872.pdf)![Scholar citations](https://img.shields.io/badge/Citations-579-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/ML-KULeuven/deepproblog)![GitHub stars](https://img.shields.io/github/stars/ML-KULeuven/deepproblog.svg?logo=github&label=Stars)| +|NIPS 2018|DeepProbLog: Neural Probabilistic Logic Programming|KU Leuven| [[paper]](https://arxiv.org/pdf/1805.10872.pdf)![Scholar citations](https://img.shields.io/badge/Citations-580-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/ML-KULeuven/deepproblog)![GitHub stars](https://img.shields.io/github/stars/ML-KULeuven/deepproblog.svg?logo=github&label=Stars)| |ICML 2022|Injecting Logical Constraints into Neural Networks via Straight-Through Estimators|Arizona State University| [[paper]](https://arxiv.org/pdf/2307.04347.pdf)![Scholar citations](https://img.shields.io/badge/Citations-22-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/azreasoners/cl-ste)![GitHub stars](https://img.shields.io/github/stars/azreasoners/cl-ste.svg?logo=github&label=Stars)| ### Concept bottleneck models | Venue | Title | Affiliation |       Link       |   Source   | @@ -44,8 +44,8 @@ A list of awesome resources related to constraint learning |ICML 2020|Concept Bottleneck Models|Standard University| [[paper]](https://arxiv.org/pdf/2007.04612.pdf)![Scholar citations](https://img.shields.io/badge/Citations-663-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/yewsiang/ConceptBottleneck)![GitHub stars](https://img.shields.io/github/stars/yewsiang/ConceptBottleneck.svg?logo=github&label=Stars)| |ICLR 2023|POST-HOC CONCEPT BOTTLENECK MODELS|Standard University| [[paper]](https://arxiv.org/pdf/2205.15480.pdf)![Scholar citations](https://img.shields.io/badge/Citations-137-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/mertyg/post-hoc-cbm)![GitHub stars](https://img.shields.io/github/stars/mertyg/post-hoc-cbm.svg?logo=github&label=Stars)| |ICML 2023|Dividing and Conquering a BlackBox to a Mixture of Interpretable Models: Route, Interpret, Repeat|BU| [[paper]](https://arxiv.org/pdf/2307.05350.pdf)![Scholar citations](https://img.shields.io/badge/Citations-6-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/batmanlab/ICML-2023-Route-interpret-repeat)![GitHub stars](https://img.shields.io/github/stars/batmanlab/ICML-2023-Route-interpret-repeat.svg?logo=github&label=Stars)| -|CVPR 2023|Language in a Bottle: Language Model Guided Concept Bottlenecks for Interpretable Image Classification|UPENN| [[paper]](https://openaccess.thecvf.com/content/CVPR2023/papers/Yang_Language_in_a_Bottle_Language_Model_Guided_Concept_Bottlenecks_for_CVPR_2023_paper.pdf)![Scholar citations](https://img.shields.io/badge/Citations-115-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/YueYANG1996/LaBo)![GitHub stars](https://img.shields.io/github/stars/YueYANG1996/LaBo.svg?logo=github&label=Stars)| -|ICLR 2023|Label-free Concept Bottleneck Models|UCSD| [[paper]](https://openreview.net/pdf?id=FlCg47MNvBA)![Scholar citations](https://img.shields.io/badge/Citations-85-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/Trustworthy-ML-Lab/Label-free-CBM)![GitHub stars](https://img.shields.io/github/stars/Trustworthy-ML-Lab/Label-free-CBM.svg?logo=github&label=Stars)| +|CVPR 2023|Language in a Bottle: Language Model Guided Concept Bottlenecks for Interpretable Image Classification|UPENN| [[paper]](https://openaccess.thecvf.com/content/CVPR2023/papers/Yang_Language_in_a_Bottle_Language_Model_Guided_Concept_Bottlenecks_for_CVPR_2023_paper.pdf)![Scholar citations](https://img.shields.io/badge/Citations-116-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/YueYANG1996/LaBo)![GitHub stars](https://img.shields.io/github/stars/YueYANG1996/LaBo.svg?logo=github&label=Stars)| +|ICLR 2023|Label-free Concept Bottleneck Models|UCSD| [[paper]](https://openreview.net/pdf?id=FlCg47MNvBA)![Scholar citations](https://img.shields.io/badge/Citations-84-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/Trustworthy-ML-Lab/Label-free-CBM)![GitHub stars](https://img.shields.io/github/stars/Trustworthy-ML-Lab/Label-free-CBM.svg?logo=github&label=Stars)| |AAAI 2023|Interactive Concept Bottleneck Models|Google| [[paper]](https://arxiv.org/pdf/2212.07430.pdf)![Scholar citations](https://img.shields.io/badge/Citations-36-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/google-research/google-research/tree/master/interactive_cbms)| |NIPS 2022|Addressing Leakage in Concept Bottleneck Models|Harvard University| [[paper]](https://finale.seas.harvard.edu/sites/scholar.harvard.edu/files/finale/files/10494_addressing_leakage_in_concept_.pdf)![Scholar citations](https://img.shields.io/badge/Citations-41-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/dtak/addressing-leakage)![GitHub stars](https://img.shields.io/github/stars/dtak/addressing-leakage.svg?logo=github&label=Stars)| |ICML 2021|Promises and Pitfalls of Black-Box Concept Learning Models|Harvard University| [[paper]](https://arxiv.org/pdf/2106.13314.pdf)![Scholar citations](https://img.shields.io/badge/Citations-74-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)|| @@ -53,6 +53,6 @@ A list of awesome resources related to constraint learning ### Model Editing | Venue | Title | Affiliation |       Link       |   Source   | | :---: | :---: | :---------: | :---: | :----: | -|ECCV 2020|Rewriting a Deep Generative Model|MIT| [[paper]](https://arxiv.org/pdf/2007.15646.pdf)![Scholar citations](https://img.shields.io/badge/Citations-119-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/davidbau/rewriting)![GitHub stars](https://img.shields.io/github/stars/davidbau/rewriting.svg?logo=github&label=Stars)| +|ECCV 2020|Rewriting a Deep Generative Model|MIT| [[paper]](https://arxiv.org/pdf/2007.15646.pdf)![Scholar citations](https://img.shields.io/badge/Citations-118-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/davidbau/rewriting)![GitHub stars](https://img.shields.io/github/stars/davidbau/rewriting.svg?logo=github&label=Stars)| |NIPS 2021|Editing a classifier by rewriting its prediction rules|MIT| [[paper]](https://proceedings.neurips.cc/paper/2021/file/c46489a2d5a9a9ecfc53b17610926ddd-Paper.pdf)![Scholar citations](https://img.shields.io/badge/Citations-67-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/MadryLab/EditingClassifiers)![GitHub stars](https://img.shields.io/github/stars/MadryLab/EditingClassifiers.svg?logo=github&label=Stars)| |arxiv 2024|DeepEdit: Knowledge Editing as Decoding with Constraints|UCLA| [[paper]](https://arxiv.org/pdf/2401.10471.pdf)![Scholar citations](https://img.shields.io/badge/Citations-9-_.svg?logo=google-scholar&labelColor=4f4f4f&color=3388ee)| [[code]](https://github.com/wangywUST/DeepEdit)![GitHub stars](https://img.shields.io/github/stars/wangywUST/DeepEdit.svg?logo=github&label=Stars)|