{"list":"other","total":87,"rows":[{"id":"1610.02391","title":"Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization","created":"2016-10-07","cited_by_count":6733},{"id":"1301.3781","title":"Efficient Estimation of Word Representations in Vector Space","created":"2013-01-16","cited_by_count":4375},{"id":"1703.01365","title":"Axiomatic Attribution for Deep Networks","created":"2017-03-04","cited_by_count":3384},{"id":"1711.11279","title":"Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)","created":"2017-11-30","cited_by_count":1319},{"id":"1807.10221","title":"Unified Perceptual Parsing for Scene Understanding","created":"2018-07-26","cited_by_count":1271},{"id":"2007.04612","title":"Concept Bottleneck Models","created":"2020-07-09","cited_by_count":1032},{"id":"2006.15055","title":"Object-Centric Learning with Slot Attention","created":"2020-06-26","cited_by_count":876},{"id":"1806.10574","title":"This Looks Like That: Deep Learning for Interpretable Image Recognition","created":"2018-06-27","cited_by_count":737},{"id":"1712.01815","title":"Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm","created":"2017-12-05","cited_by_count":734},{"id":"1704.05796","title":"Network Dissection: Quantifying Interpretability of Deep Visual Representations","created":"2017-04-19","cited_by_count":708},{"id":"1806.07538","title":"Towards Robust Interpretability with Self-Explaining Neural Networks","created":"2018-06-20","cited_by_count":505},{"id":"2005.09635","title":"InterFaceGAN: Interpreting the Disentangled Face Representation Learned by GANs","created":"2020-05-18","cited_by_count":394},{"id":"1904.12584","title":"The Neuro-Symbolic Concept Learner: Interpreting Scenes, Words, and Sentences From Natural Supervision","created":"2019-04-26","cited_by_count":372},{"id":"1810.02338","title":"Neural-Symbolic VQA: Disentangling Reasoning from Vision and Language Understanding","created":"2018-10-04","cited_by_count":304},{"id":"1703.03717","title":"Right for the Right Reasons: Training Differentiable Models by Constraining their Explanations","created":"2017-03-10","cited_by_count":294},{"id":"2012.14261","title":"A Survey on Neural Network Interpretability","created":"2020-12-28","cited_by_count":291},{"id":"1910.07969","title":"On Completeness-aware Concept-Based Explanations in Deep Neural Networks","created":"2019-10-17","cited_by_count":239},{"id":"1808.00033","title":"Techniques for Interpretable Machine Learning","created":"2018-07-31","cited_by_count":238},{"id":"1710.04806","title":"Deep Learning for Case-Based Reasoning through Prototypes: A Neural Network that Explains Its Predictions","created":"2017-10-13","cited_by_count":234},{"id":"2002.01650","title":"Concept Whitening for Interpretable Image Recognition","created":"2020-02-05","cited_by_count":225},{"id":"2001.05371","title":"Making deep neural networks right for the right scientific reasons by interacting with their explanations","created":"2020-01-15","cited_by_count":142},{"id":"1907.07165","title":"Explaining Classifiers with Causal Concept Effect (CaCE)","created":"2019-07-16","cited_by_count":135},{"id":"2106.13314","title":"Promises and Pitfalls of Black-Box Concept Learning Models","created":"2021-06-24","cited_by_count":131},{"id":"1801.03454","title":"Net2Vec: Quantifying and Explaining how Concepts are Encoded by Filters in Deep Neural Networks","created":"2018-01-10","cited_by_count":129},{"id":"2111.09259","title":"Acquisition of Chess Knowledge in AlphaZero","created":"2021-11-17","cited_by_count":123},{"id":"2104.13369","title":"Explaining in Style: Training a GAN to explain a classifier in StyleSpace","created":"2021-04-27","cited_by_count":122},{"id":"2006.15417","title":"Invertible Concept-based Explanations for CNN Models with Non-negative Concept Activation Vectors","created":"2020-06-27","cited_by_count":120},{"id":"2105.04289","title":"Do Concept Bottleneck Models Learn as Intended?","created":"2021-05-10","cited_by_count":116},{"id":"2210.11841","title":"Diffusion Visual Counterfactual Explanations","created":"2022-10-21","cited_by_count":96},{"id":"2011.12854","title":"Right for the Right Concept: Revising Neuro-Symbolic Concepts by Interacting with their Explanations","created":"2020-11-25","cited_by_count":94},{"id":"1910.09772","title":"Weakly Supervised Disentanglement with Guarantees","created":"2019-10-22","cited_by_count":82},{"id":"2202.01459","title":"Concept Bottleneck Model with Additional Unsupervised Concepts","created":"2022-02-03","cited_by_count":79},{"id":"2102.02201","title":"When Can Models Learn From Explanations? A Formal Framework for Understanding the Roles of Explanation Data","created":"2021-02-03","cited_by_count":75},{"id":"2103.10663","title":"XProtoNet: Diagnosis in Chest Radiography with Global and Local Explanations","created":"2021-03-19","cited_by_count":73},{"id":"2205.15612","title":"GlanceNets: Interpretabile, Leak-proof Concept-based Models","created":"2022-05-31","cited_by_count":73},{"id":"2208.10431","title":"ProtoPFormer: Concentrating on Prototypical Parts in Vision Transformers for Interpretable Image Recognition","created":"2022-08-22","cited_by_count":63},{"id":"2212.04629","title":"Post hoc Explanations may be Ineffective for Detecting Unknown Spurious Correlation","created":"2022-12-09","cited_by_count":59},{"id":"2207.14526","title":"Leveraging Explanations in Interactive Machine Learning: An Overview","created":"2022-07-29","cited_by_count":53},{"id":"2205.15769","title":"Concept-level Debugging of Part-Prototype Networks","created":"2022-05-31","cited_by_count":52},{"id":"2010.13233","title":"Now You See Me (CME): Concept-based Model Extraction","created":"2020-10-25","cited_by_count":51},{"id":"2301.11911","title":"Multi-dimensional concept discovery (MCD): A unifying framework with completeness guarantees","created":"2023-01-27","cited_by_count":50},{"id":"2203.08008","title":"Beyond Explaining: Opportunities and Challenges of XAI-Based Model Improvement","created":"2022-03-15","cited_by_count":48},{"id":"2108.11761","title":"A Framework for Learning Ante-hoc Explainable Models via Concepts","created":"2021-08-25","cited_by_count":45},{"id":"2310.16410","title":"Bridging the Human-AI Knowledge Gap: Concept Discovery and Transfer in AlphaZero","created":"2023-10-25","cited_by_count":42},{"id":"2006.13913","title":"Generative causal explanations of black-box classifiers","created":"2020-06-24","cited_by_count":41},{"id":"2309.16928","title":"Learning to Receive Help: Intervention-Aware Concept Embedding Models","created":"2023-09-29","cited_by_count":41},{"id":"2303.12872","title":"Human Uncertainty in Concept-Based AI Systems","created":"2023-03-22","cited_by_count":39},{"id":"2301.04011","title":"Learning Support and Trivial Prototypes for Interpretable Image Classification","created":"2023-01-08","cited_by_count":38},{"id":"2105.15164","title":"DISSECT: Disentangled Simultaneous Explanations via Concept Traversals","created":"2021-05-31","cited_by_count":37},{"id":"2212.03954","title":"Going Beyond XAI: A Systematic Survey for Explanation-Guided Learning","created":"2022-12-07","cited_by_count":36},{"id":"2301.12276","title":"ProtoSeg: Interpretable Semantic Segmentation with Prototypical Parts","created":"2023-01-28","cited_by_count":33},{"id":"2302.01242","title":"Neuro-Symbolic Continual Learning: Knowledge, Reasoning Shortcuts and Concept Rehearsal","created":"2023-02-02","cited_by_count":33},{"id":"2007.11500","title":"Debiasing Concept-based Explanations with Causal Analysis","created":"2020-07-22","cited_by_count":32},{"id":"2301.10367","title":"Towards Robust Metrics for Concept Representation Evaluation","created":"2023-01-25","cited_by_count":29},{"id":"2112.02290","title":"Interactive Disentanglement: Learning Concepts by Interacting with their Prototype Representations","created":"2021-12-04","cited_by_count":24},{"id":"2202.12451","title":"Human-Centered Concept Explanations for Neural Networks","created":"2022-02-25","cited_by_count":23},{"id":"2204.11830","title":"Proto2Proto: Can you recognize the car, the way I do?","created":"2022-04-25","cited_by_count":23},{"id":"2106.08641","title":"Best of both worlds: local and global explanations with human-understandable concepts","created":"2021-06-16","cited_by_count":22},{"id":"2108.10612","title":"ProtoMIL: Multiple Instance Learning with Prototypical Parts for Whole-Slide Image Classification","created":"2021-08-24","cited_by_count":22},{"id":"2206.13413","title":"RES: A Robust Framework for Guiding Visual Explanation","created":"2022-06-27","cited_by_count":22},{"id":"2211.16080","title":"Understanding and Enhancing Robustness of Concept-based Models","created":"2022-11-29","cited_by_count":22},{"id":"2208.14966","title":"Concept Gradient: Concept-based Interpretation Without Linear Assumption","created":"2022-08-31","cited_by_count":20},{"id":"2012.02898","title":"Learning Interpretable Concept-Based Models with Human Feedback","created":"2020-12-04","cited_by_count":17},{"id":"2104.06917","title":"Is Disentanglement all you need? Comparing Concept-based & Disentanglement Approaches","created":"2021-04-14","cited_by_count":17},{"id":"2309.12482","title":"State2Explanation: Concept-Based Explanations to Benefit Agent Learning and User Understanding","created":"2023-09-21","cited_by_count":17},{"id":"2105.10172","title":"Explainable Machine Learning with Prior Knowledge: An Overview","created":"2021-05-21","cited_by_count":15},{"id":"2002.03549","title":"Adversarial TCAV -- Robust and Effective Interpretation of Intermediate Layers in Neural Networks","created":"2020-02-10","cited_by_count":12},{"id":"2108.13828","title":"PACE: Posthoc Architecture-Agnostic Concept Extractor for Explaining CNNs","created":"2021-08-31","cited_by_count":12},{"id":"2212.11254","title":"Adapting to Latent Subgroup Shifts via Concepts and Proxies","created":"2022-12-21","cited_by_count":12},{"id":"2004.03623","title":"PatchVAE: Learning Local Latent Codes for Recognition","created":"2020-04-07","cited_by_count":10},{"id":"2311.15303","title":"Concept Distillation: Leveraging Human-Centered Explanations for Model Improvement","created":"2023-11-26","cited_by_count":10},{"id":"2206.09575","title":"C-SENN: Contrastive Self-Explaining Neural Network","created":"2022-06-20","cited_by_count":9},{"id":"2002.01660","title":"CHAIN: Concept-harmonized Hierarchical Inference Interpretation of Deep Convolutional Neural Networks","created":"2020-02-05","cited_by_count":8},{"id":"2109.04518","title":"Unsupervised Causal Binary Concepts Discovery with VAE for Black-box Model Explanation","created":"2021-09-09","cited_by_count":8},{"id":"1806.01756","title":"Concept-Oriented Deep Learning","created":"2018-06-05","cited_by_count":5},{"id":"2012.06523","title":"Dependency Decomposition and a Reject Option for Explainable Models","created":"2020-12-11","cited_by_count":4},{"id":"2109.11160","title":"Toward a Unified Framework for Debugging Concept-based Models","created":"2021-09-23","cited_by_count":4},{"id":"2206.05257","title":"Explaining Image Classifiers Using Contrastive Counterfactuals in Generative Latent Spaces","created":"2022-06-10","cited_by_count":4},{"id":"2206.05981","title":"Efficient Human-in-the-loop System for Guiding DNNs Attention","created":"2022-06-13","cited_by_count":4},{"id":"2301.06324","title":"Img2Tab: Automatic Class Relevant Concept Discovery from StyleGAN Features for Explainable Image Classification","created":"2023-01-16","cited_by_count":4},{"id":"2301.13445","title":"A Survey of Explainable AI in Deep Visual Modeling: Methods and Metrics","created":"2023-01-31","cited_by_count":4},{"id":"2207.01916","title":"Hierarchical Symbolic Reasoning in Hyperbolic Space for Deep Discriminative Models","created":"2022-07-05","cited_by_count":3},{"id":"1909.01839","title":"Improving Disentangled Representation Learning with the Beta Bernoulli Process","created":"2019-09-03","cited_by_count":2},{"id":"2106.10947","title":"Leveraging Conditional Generative Models in a General Explanation Framework of Classifier Decisions","created":"2021-06-21","cited_by_count":2},{"id":"2202.00391","title":"Right for the Right Latent Factors: Debiasing Generative Models via Disentanglement","created":"2022-02-01","cited_by_count":2},{"id":"2203.10690","title":"CNN Attention Guidance for Improved Orthopedics Radiographic Fracture Classification","created":"2022-03-21","cited_by_count":2},{"id":"2211.10807","title":"Concept-based Explanations using Non-negative Concept Activation Vectors and Decision Tree for CNN Models","created":"2022-11-19","cited_by_count":2}],"next_cursor":null}