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] | |
TKDE 2019 | Informed machine learning-a taxonomy and survey of integrating prior knowledge into learning systems | Fraunhofer IAIS | [paper] | |
Scientific Reports 2022 | A review of some techniques for inclusion of domain-knowledge into deep neural networks | [paper] |
Venue | Title | Affiliation | Link | Source |
---|---|---|---|---|
EMNLP 2020 | CommonGen: A Constrained Text Generation Challenge for Generative Commonsense Reasoning | USC | [paper] | [code] |
Medical image analysis 2021 | VerSe: A Vertebrae Labelling and Segmentation Benchmark for Multi-detector CT Images | Technical University of Munich | [paper] | [code] |
Venue | Title | Affiliation | Link | Source |
---|---|---|---|---|
CVPR 2015 | Hyper-class Augmented and Regularized Deep Learning for Fine-grained Image Classification | UCSD | [paper] |
Venue | Title | Affiliation | Link | Source |
---|---|---|---|---|
NIPS 2023 | A Pseudo-Semantic Loss for Autoregressive Models with Logical Constraints | UCLA | [paper] | [code] |
ACL 2016 | Harnessing Deep Neural Networks with Logic Rules | CMU | [paper] | [code] |
Applied Intelligence 1999 | The Connectionist Inductive Learning and Logic Programming System | IC | [paper] | |
ICML 2018 | A Semantic Loss Function for Deep Learning with Symbolic Knowledge | UCLA | [paper] | [code] |
NAACL 2021 | Neurologic decoding:(un) supervised neural text generation with predicate logic constraints | UW | [paper] | [code] |
EMNLP 2017 | Guided Open Vocabulary Image Captioning with Constrained Beam Search | The Australian National University | [paper] | [code] |
NIPS 2022 | Don't Pour Cereal into Coffee: Differentiable Temporal Logic for Temporal Action Segmentation | NUS | [paper] | [code] |
AAAI 2021 | MultiplexNet: Towards Fully Satisfied Logical Constraints in Neural Networks | University of Edinburgh | [paper] | [code] |
NIPS 2018 | DeepProbLog: Neural Probabilistic Logic Programming | KU Leuven | [paper] | [code] |
ICML 2022 | Injecting Logical Constraints into Neural Networks via Straight-Through Estimators | Arizona State University | [paper] | [code] |
Venue | Title | Affiliation | Link | Source |
---|---|---|---|---|
ICML 2020 | Concept Bottleneck Models | Standard University | [paper] | [code] |
ICLR 2023 | POST-HOC CONCEPT BOTTLENECK MODELS | Standard University | [paper] | [code] |
ICML 2023 | Dividing and Conquering a BlackBox to a Mixture of Interpretable Models: Route, Interpret, Repeat | BU | [paper] | [code] |
CVPR 2023 | Language in a Bottle: Language Model Guided Concept Bottlenecks for Interpretable Image Classification | UPENN | [paper] | [code] |
ICLR 2023 | Label-free Concept Bottleneck Models | UCSD | [paper] | [code] |
AAAI 2023 | Interactive Concept Bottleneck Models | [paper] | [code] | |
NIPS 2022 | Addressing Leakage in Concept Bottleneck Models | Harvard University | [paper] | [code] |
ICML 2021 | Promises and Pitfalls of Black-Box Concept Learning Models | Harvard University | [paper] | |
ICML 2018 | Interpretability Beyond Feature Attribution:Quantitative Testing with Concept Activation Vectors (TCAV) | [paper] | [code] |
Venue | Title | Affiliation | Link | Source |
---|---|---|---|---|
ECCV 2020 | Rewriting a Deep Generative Model | MIT | [paper] | [code] |
NIPS 2021 | Editing a classifier by rewriting its prediction rules | MIT | [paper] | [code] |
arxiv 2024 | DeepEdit: Knowledge Editing as Decoding with Constraints | UCLA | [paper] | [code] |