{"list":"other","total":163,"rows":[{"id":"1706.03762","title":"Attention Is All You Need","created":"2017-06-12","cited_by_count":60361},{"id":"2006.11239","title":"Denoising Diffusion Probabilistic Models","created":"2020-06-19","cited_by_count":21076},{"id":"1609.02907","title":"Semi-Supervised Classification with Graph Convolutional Networks","created":"2016-09-09","cited_by_count":11361},{"id":"1705.07874","title":"A Unified Approach to Interpreting Model Predictions","created":"2017-05-22","cited_by_count":6555},{"id":"1409.0473","title":"Neural Machine Translation by Jointly Learning to Align and Translate","created":"2014-09-01","cited_by_count":4548},{"id":"1704.01212","title":"Neural Message Passing for Quantum Chemistry","created":"2017-04-04","cited_by_count":3487},{"id":"1703.01365","title":"Axiomatic Attribution for Deep Networks","created":"2017-03-04","cited_by_count":3384},{"id":"1910.10045","title":"Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI","created":"2019-10-22","cited_by_count":1552},{"id":"1702.08608","title":"Towards A Rigorous Science of Interpretable Machine Learning","created":"2017-02-28","cited_by_count":1416},{"id":"1706.07269","title":"Explanation in Artificial Intelligence: Insights from the Social Sciences","created":"2017-06-22","cited_by_count":1206},{"id":"2408.06292","title":"The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery","created":"2024-08-12","cited_by_count":1071},{"id":"1802.01933","title":"A Survey Of Methods For Explaining Black Box Models","created":"2018-02-06","cited_by_count":973},{"id":"1910.09457","title":"Aleatoric and Epistemic Uncertainty in Machine Learning: An Introduction to Concepts and Methods","created":"2019-10-21","cited_by_count":913},{"id":"1903.03894","title":"GNNExplainer: Generating Explanations for Graph Neural Networks","created":"2019-03-10","cited_by_count":737},{"id":"1906.01563","title":"Hamiltonian Neural Networks","created":"2019-06-04","cited_by_count":712},{"id":"1802.05983","title":"Disentangling by Factorising","created":"2018-02-16","cited_by_count":616},{"id":"1905.11481","title":"AI Feynman: a Physics-Inspired Method for Symbolic Regression","created":"2019-05-27","cited_by_count":548},{"id":"1806.00069","title":"Explaining Explanations: An Overview of Interpretability of Machine Learning","created":"2018-05-31","cited_by_count":508},{"id":"1806.07538","title":"Towards Robust Interpretability with Self-Explaining Neural Networks","created":"2018-06-20","cited_by_count":505},{"id":"1712.09707","title":"Deep learning for universal linear embeddings of nonlinear dynamics","created":"2017-12-27","cited_by_count":494},{"id":"1611.05397","title":"Reinforcement Learning with Unsupervised Auxiliary Tasks","created":"2016-11-16","cited_by_count":404},{"id":"2003.04630","title":"Lagrangian Neural Networks","created":"2020-03-10","cited_by_count":398},{"id":"2103.11251","title":"Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges","created":"2021-03-20","cited_by_count":367},{"id":"1706.07979","title":"Methods for Interpreting and Understanding Deep Neural Networks","created":"2017-06-24","cited_by_count":345},{"id":"2006.11287","title":"Discovering Symbolic Models from Deep Learning with Inductive Biases","created":"2020-06-19","cited_by_count":344},{"id":"2008.08605","title":"The effect of data encoding on the expressive power of variational quantum machine learning models","created":"2020-08-19","cited_by_count":333},{"id":"1912.04871","title":"Deep symbolic regression: Recovering mathematical expressions from data via risk-seeking policy gradients","created":"2019-12-10","cited_by_count":309},{"id":"1901.04592","title":"Interpretable machine learning: definitions, methods, and applications","created":"2019-01-14","cited_by_count":305},{"id":"1801.06637","title":"Deep Hidden Physics Models: Deep Learning of Nonlinear Partial Differential Equations","created":"2018-01-20","cited_by_count":235},{"id":"1810.01075","title":"Implicit Self-Regularization in Deep Neural Networks: Evidence from Random Matrix Theory and Implications for Learning","created":"2018-10-02","cited_by_count":215},{"id":"1708.00588","title":"Hidden Physics Models: Machine Learning of Nonlinear Partial Differential Equations","created":"2017-08-02","cited_by_count":208},{"id":"2204.10532","title":"End-to-end symbolic regression with transformers","created":"2022-04-22","cited_by_count":203},{"id":"1905.08883","title":"Explainable Machine Learning for Scientific Insights and Discoveries","created":"2019-05-21","cited_by_count":166},{"id":"2006.10782","title":"AI Feynman 2.0: Pareto-optimal symbolic regression exploiting graph modularity","created":"2020-06-18","cited_by_count":144},{"id":"2005.06247","title":"Explainable Reinforcement Learning: A Survey","created":"2020-05-13","cited_by_count":141},{"id":"2009.00329","title":"Learning explanations that are hard to vary","created":"2020-09-01","cited_by_count":132},{"id":"1802.04181","title":"State Representation Learning for Control: An Overview","created":"2018-02-12","cited_by_count":127},{"id":"1807.10300","title":"Discovering physical concepts with neural networks","created":"2018-07-26","cited_by_count":117},{"id":"1610.02995","title":"Extrapolation and learning equations","created":"2016-10-10","cited_by_count":110},{"id":"2211.10873","title":"Interpretable Scientific Discovery with Symbolic Regression: A Review","created":"2022-11-20","cited_by_count":105},{"id":"2111.00053","title":"Symbolic Regression via Neural-Guided Genetic Programming Population Seeding","created":"2021-10-29","cited_by_count":101},{"id":"1912.04825","title":"Integration of Neural Network-Based Symbolic Regression in Deep Learning for Scientific Discovery","created":"2019-12-10","cited_by_count":93},{"id":"2011.04698","title":"AI Poincar\\'e: Machine Learning Conservation Laws from Trajectories","created":"2020-11-09","cited_by_count":77},{"id":"1710.00794","title":"What Does Explainable AI Really Mean? A New Conceptualization of Perspectives","created":"2017-10-02","cited_by_count":72},{"id":"2202.02306","title":"Rediscovering orbital mechanics with machine learning","created":"2022-02-04","cited_by_count":71},{"id":"2204.01467","title":"On scientific understanding with artificial intelligence","created":"2022-04-04","cited_by_count":70},{"id":"1905.01205","title":"Learning in Modal Space: Solving Time-Dependent Stochastic PDEs Using Physics-Informed Neural Networks","created":"2019-05-03","cited_by_count":64},{"id":"2210.08566","title":"Theory for Equivariant Quantum Neural Networks","created":"2022-10-16","cited_by_count":62},{"id":"2004.12157","title":"A Bayesian machine scientist to aid in the solution of challenging scientific problems","created":"2020-04-25","cited_by_count":61},{"id":"2002.09595","title":"The Pragmatic Turn in Explainable Artificial Intelligence (XAI)","created":"2020-02-22","cited_by_count":54},{"id":"1909.05862","title":"Learning Symbolic Physics with Graph Networks","created":"2019-09-12","cited_by_count":53},{"id":"2201.10295","title":"Post-Hoc Explanations Fail to Achieve their Purpose in Adversarial Contexts","created":"2022-01-25","cited_by_count":53},{"id":"1606.00318","title":"Discovering Phase Transitions with Unsupervised Learning","created":"2016-06-01","cited_by_count":49},{"id":"2306.13461","title":"Understanding quantum machine learning also requires rethinking generalization","created":"2023-06-23","cited_by_count":49},{"id":"1703.02435","title":"Unsupervised learning of phase transitions: from principal component analysis to variational autoencoders","created":"2017-03-07","cited_by_count":47},{"id":"1909.04607","title":"Discovery of Useful Questions as Auxiliary Tasks","created":"2019-09-10","cited_by_count":45},{"id":"1706.00868","title":"Active learning machine learns to create new quantum experiments","created":"2017-06-02","cited_by_count":41},{"id":"2106.09004","title":"Learning effective stochastic differential equations from microscopic simulations: linking stochastic numerics to deep learning","created":"2021-06-10","cited_by_count":39},{"id":"2209.13083","title":"Why neural networks find simple solutions: the many regularizers of geometric complexity","created":"2022-09-27","cited_by_count":39},{"id":"1809.04506","title":"Combined Reinforcement Learning via Abstract Representations","created":"2018-09-12","cited_by_count":37},{"id":"2212.12611","title":"Your diffusion model secretly knows the dimension of the data manifold","created":"2022-12-23","cited_by_count":36},{"id":"2003.13679","title":"Detecting Symmetries with Neural Networks","created":"2020-03-30","cited_by_count":34},{"id":"2003.04299","title":"Discovering Symmetry Invariants and Conserved Quantities by Interpreting Siamese Neural Networks","created":"2020-03-09","cited_by_count":33},{"id":"2109.09721","title":"Machine-learning hidden symmetries","created":"2021-09-20","cited_by_count":31},{"id":"2110.15355","title":"Explaining Latent Representations with a Corpus of Examples","created":"2021-10-28","cited_by_count":30},{"id":"2310.00105","title":"Latent Space Symmetry Discovery","created":"2023-09-29","cited_by_count":29},{"id":"1701.04831","title":"Equivalence of restricted Boltzmann machines and tensor network states","created":"2017-01-17","cited_by_count":27},{"id":"2111.01037","title":"Interpretable and Explainable Machine Learning for Materials Science and Chemistry","created":"2021-11-01","cited_by_count":26},{"id":"2112.03321","title":"Noether Networks: Meta-Learning Useful Conserved Quantities","created":"2021-12-06","cited_by_count":26},{"id":"2107.10879","title":"Discovering Sparse Interpretable Dynamics from Partial Observations","created":"2021-07-22","cited_by_count":25},{"id":"2307.16506","title":"Explainable Equivariant Neural Networks for Particle Physics: PELICAN","created":"2023-07-31","cited_by_count":24},{"id":"1907.06011","title":"Extracting Interpretable Physical Parameters from Spatiotemporal Systems using Unsupervised Learning","created":"2019-07-13","cited_by_count":23},{"id":"2301.05638","title":"Deep Learning Symmetries and Their Lie Groups, Algebras, and Subalgebras from First Principles","created":"2023-01-13","cited_by_count":22},{"id":"2306.04292","title":"Dear XAI Community, We Need to Talk! Fundamental Misconceptions in Current XAI Research","created":"2023-06-07","cited_by_count":22},{"id":"2309.07742","title":"Interpretability is in the Mind of the Beholder: A Causal Framework for Human-interpretable Representation Learning","created":"2023-09-14","cited_by_count":22},{"id":"2311.00212","title":"A Unified Framework to Enforce, Discover, and Promote Symmetry in Machine Learning","created":"2023-11-01","cited_by_count":22},{"id":"2206.05487","title":"Scientific Inference With Interpretable Machine Learning: Analyzing Models to Learn About Real-World Phenomena","created":"2022-06-11","cited_by_count":21},{"id":"2203.12610","title":"AI Poincar\\'{e} 2.0: Machine Learning Conservation Laws from Differential Equations","created":"2022-03-23","cited_by_count":20},{"id":"1708.09401","title":"Machine Learning Topological Invariants with Neural Networks","created":"2017-08-30","cited_by_count":19},{"id":"2312.15124","title":"On fundamental aspects of quantum extreme learning machines","created":"2023-12-23","cited_by_count":18},{"id":"2111.10009","title":"ExoMiner: A Highly Accurate and Explainable Deep Learning Classifier that Validates 301 New Exoplanets","created":"2021-11-19","cited_by_count":17},{"id":"2301.09138","title":"Explaining Quantum Circuits with Shapley Values: Towards Explainable Quantum Machine Learning","created":"2023-01-22","cited_by_count":17},{"id":"2311.11258","title":"Tensor networks for interpretable and efficient quantum-inspired machine learning","created":"2023-11-19","cited_by_count":17},{"id":"1802.09484","title":"Disentangling the independently controllable factors of variation by interacting with the world","created":"2018-02-26","cited_by_count":16},{"id":"2001.00593","title":"Operationally meaningful representations of physical systems in neural networks","created":"2020-01-02","cited_by_count":16},{"id":"2108.05379","title":"On the Relation of Trust and Explainability: Why to Engineer for Trustworthiness","created":"2021-08-11","cited_by_count":16},{"id":"1801.07172","title":"Scale-invariant Feature Extraction of Neural Network and Renormalization Group Flow","created":"2018-01-22","cited_by_count":15},{"id":"1811.00961","title":"Discovering conservation laws from data for control","created":"2018-11-02","cited_by_count":14},{"id":"2007.10784","title":"OccamNet: A Fast Neural Model for Symbolic Regression at Scale","created":"2020-07-16","cited_by_count":14},{"id":"2011.13466","title":"Explainable AI for ML jet taggers using expert variables and layerwise relevance propagation","created":"2020-11-26","cited_by_count":14},{"id":"2105.06677","title":"XAI Handbook: Towards a Unified Framework for Explainable AI","created":"2021-05-14","cited_by_count":14},{"id":"2011.03474","title":"Correlator Convolutional Neural Networks: An Interpretable Architecture for Image-like Quantum Matter Data","created":"2020-11-06","cited_by_count":13},{"id":"2112.10789","title":"Machine learning discovery of new phases in programmable quantum simulator snapshots","created":"2021-12-20","cited_by_count":13},{"id":"2308.11098","title":"On the Interpretability of Quantum Neural Networks","created":"2023-08-22","cited_by_count":13},{"id":"2003.01504","title":"Towards Novel Insights in Lattice Field Theory with Explainable Machine Learning","created":"2020-03-03","cited_by_count":12},{"id":"2109.02490","title":"Learning Interpretable Representations of Entanglement in Quantum Optics Experiments using Deep Generative Models","created":"2021-09-06","cited_by_count":12},{"id":"2111.12840","title":"Explaining machine-learned particle-flow reconstruction","created":"2021-11-24","cited_by_count":12},{"id":"2202.04925","title":"Decomposing neural networks as mappings of correlation functions","created":"2022-02-10","cited_by_count":12},{"id":"2208.14995","title":"Discovering Conservation Laws using Optimal Transport and Manifold Learning","created":"2022-08-31","cited_by_count":12},{"id":"1703.07718","title":"Independently Controllable Features","created":"2017-03-22","cited_by_count":11}],"next_cursor":"100"}