Human-level concept learning through probabilistic program induction

Lake, Brenden M., Salakhutdinov, Ruslan, Tenenbaum, Joshua B.·2015·Science

Cited by (1,005)

Items 201–300 of 1,005

  1. Human-machine Collaborative Decision-making: An Evolutionary Roadmap Based on Cognitive Intelligence
    Minglun Ren, Nengying Chen, Hui Qiu · 2023 · International Journal of Social Robotics
  2. Finding strong lenses in CFHTLS using convolutional neural networks
    Jacobs, C., Glazebrook, K., Collett, T., More, A., McCarthy, C. · 2017 · Monthly Notices of the Royal Astronomical Society
  3. Survey on Artificial Intelligence for Vehicles
    Jun Li, Hong Cheng, Hongliang Guo, Shaobo Qiu · 2018 · Automotive Innovation
  4. Backpropagation through the Void: Optimizing control variates for black-box gradient estimation
    Will Grathwohl, Dami Choi, Yuhuai Wu, Geoffrey Roeder, David Duvenaud · 2017 · arXiv (Cornell University)
  5. Zero-Shot Learning via Class-Conditioned Deep Generative Models
    Wenlin Wang, Yunchen Pu, Vinay Kumar Verma, Kai Fan, Yizhe Zhang, Changyou Chen, Piyush Rai, Lawrence Carin · 2018 · Proceedings of the AAAI Conference on Artificial Intelligence
  6. JAX, M.D. A framework for differentiable physics*
    Samuel S. Schoenholz, Ekin D. Cubuk · 2021 · Journal of Statistical Mechanics Theory and Experiment
  7. Learning to cluster in order to transfer across domains and tasks
    Yen-Chang Hsu, Zhaoyang Lv, Zsolt Kira · 2017 · arXiv (Cornell University)
  8. An intelligent fault diagnosis model based on deep neural network for few-shot fault diagnosis
    Cunjun Wang, Zili Xu · 2021 · Neurocomputing
  9. INPUT COMPLEXITY AND OUT-OF-DISTRIBUTION DETECTION WITH LIKELIHOOD-BASED GENERATIVE MODELS
    Joan Serrà, David Álvarez, Vicenç Gómez, Olga Slizovskaia, José F. Núñez, Jordi Luque · 2019 · arXiv (Cornell University)
  10. Dynamic Signature Verification System Based on One Real Signature
    Moises Díaz, Andreas Fischer, Miguel A. Ferrer, Réjean Plamondon B · 2016 · IEEE Transactions on Cybernetics
  11. Bayesian learning for rapid prediction of lithium-ion battery-cycling protocols
    Benben Jiang, William E. Gent, Fabian Mohr, Supratim Das, Marc D. Berliner, Michael A. Forsuelo, Hongbo Zhao, Peter M. Attia, Aditya Grover, Patrick K. Herring et al. · 2021 · Joule
  12. Between the Devil and the Deep Blue Sea: Tensions Between Scientific Judgement and Statistical Model Selection
    Danielle Navarro · 2018 · Computational Brain & Behavior
  13. Doing more with less: meta-reasoning and meta-learning in humans and machines
    Thomas L. Griffiths, Frederick Callaway, Michael B. Chang, Erin Grant, Paul M. Krueger, Falk Lieder · 2019 · Current Opinion in Behavioral Sciences
  14. Word meaning in minds and machines.
    Lake, Brenden M., Murphy, Gregory L. · 2023 · Psychological Review
  15. Cutting Down on Prompts and Parameters: Simple Few-Shot Learning with Language Models
    Logan IV, Robert, Balazevic, Ivana, Wallace, Eric, Petroni, Fabio, Singh, Sameer, Riedel, Sebastian · 2022 · Findings of the Association for Computational Linguistics: ACL 2022
  16. Efficient coding explains the universal law of generalization in human perception
    Chris R. Sims · 2018 · Science
  17. Metahuman systems = humans + machines that learn
    Kalle Lyytinen, Jeffrey V. Nickerson, John Leslie King · 2020 · Journal of Information Technology
  18. Cross-domain Few-shot Learning with Task-specific Adapters
    Weihong Li, Xialei Liu, Hakan Bilen · 2022 · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings
  19. Abstraction and analogy‐making in artificial intelligence
    Mitchell, Melanie · 2021 · Annals of the New York Academy of Sciences
  20. Theoretical perspectives on active sensing
    Scott Cheng‐Hsin Yang, Daniel M. Wolpert, Máté Lengyel · 2016 · Current Opinion in Behavioral Sciences
  21. Learning Compositional Representations for Few-Shot Recognition
    Pavel Tokmakov, Yu-Xiong Wang, Martial Hebert · 2019 · IEEE/CVF International Conference on Computer Vision (ICCV)
  22. Meta-Learning Representations for Continual Learning
    Khurram Javed, Martha White · 2019 · arXiv (Cornell University)
  23. A Few Shot Classification Methods Based on Multiscale Relational Networks
    Wenfeng Zheng, Tian Xia, Bo Yang, Shan Liu, Yueming Ding, Jiawei Tian, Lirong Yin · 2022 · Applied Sciences
  24. Heterogeneous Domain Generalization Via Domain Mixup
    Yufei Wang, Haoliang Li, Alex Chichung Kot · 2020 · IEEE International Conference on Acoustics Speech and Signal Processing
  25. Brain-inspired global-local learning incorporated with neuromorphic computing
    Yujie Wu, Rong Juan Zhao, Jun Zhu, Feng Chen, Mingkun Xu, Guoqi Li, Sen Song, Lei Deng, Guanrui Wang, Hao Zheng et al. · 2022 · Nature Communications
  26. JAX-Fluids: A fully-differentiable high-order computational fluid dynamics solver for compressible two-phase flows
    Deniz A. Bezgin, Aaron B. Buhendwa, Nikolaus A. Adams · 2022 · Computer Physics Communications
  27. Born to learn: The inspiration, progress, and future of evolved plastic artificial neural networks
    Andrea Soltoggio, Kenneth Owen Stanley, Sebastian Risi · 2018 · Neural Networks
  28. Multi-attention Meta Learning for Few-shot Fine-grained Image Recognition
    Yaohui Zhu, Chenlong Liu, Shuqiang Jiang · 2020 · International Joint Conference on Artificial Intelligence
  29. Sensitivity to geometric shape regularity in humans and baboons: A putative signature of human singularity
    Mathias Sablé-Meyer, Joël Fagot, Serge Caparos, Timo van Kerkoerle, Marie Amalric, Stanislas Dehaene · 2021 · Proceedings of the National Academy of Sciences
  30. Toward an integration of deep learning and neuroscience
    Marblestone, Adam H., Wayne, Greg, Kording, Konrad P. · 2016 · Frontiers in Computational Neuroscience
  31. Selecting Relevant Features from a Multi-domain Representation for Few-Shot Classification
    Nikita Dvornik, Cordelia Schmid, Julien Mairal · 2020 · Lecture notes in computer science
  32. Soliton, breather, and rogue wave solutions for solving the nonlinear Schrödinger equation using a deep learning method with physical constraints*
    Juncai Pu, Jun Li, Yong Chen · 2021 · Chinese Physics B
  33. Structure learning and the posterior parietal cortex
    Summerfield, Christopher, Luyckx, Fabrice, Sheahan, Hannah · 2020 · Progress in Neurobiology
  34. Examples-Rules Guided Deep Neural Network for Makeup Recommendation
    Taleb Alashkar, Songyao Jiang, Shuyang Wang, Yun Fu · 2017 · Proceedings of the AAAI Conference on Artificial Intelligence
  35. A Learned Representation for Scalable Vector Graphics
    Raphael Gontijo Lopes, David Ha, Douglas Eck, Jonathon Shlens · 2019 · IEEE/CVF International Conference on Computer Vision (ICCV)
  36. Two-Way Concept-Cognitive Learning Method: A Fuzzy-Based Progressive Learning
    Weihua Xu, Doudou Guo, Yuhua Qian, Weiping Ding · 2022 · IEEE Transactions on Fuzzy Systems
  37. Learning Algorithms for Active Learning
    Philip Bachman, A. Sordoni, Adam Trischler · 2017 · International Conference on Machine Learning
  38. Evolutionary architecture search for deep multitask networks
    Jason Liang, Elliot Meyerson, Risto P Miikkulainen · 2018 · Genetic and Evolutionary Computation Conference (GECCO)
  39. Centroid-based Deep Metric Learning for Speaker Recognition
    Jixuan Wang, Kuan-Chieh Jackson Wang, Marc T. Law, Frank Rudzicz, Michael Brudno · 2019 · IEEE International Conference on Acoustics Speech and Signal Processing
  40. Guided Variational Autoencoder for Disentanglement Learning
    Zheng Ding, Yifan Xu, Weijian Xu, Gaurav Parmar, Yang Yang, Max Welling, Zhuowen Tu · 2020 · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings
  41. Learning to Learn Adaptive Classifier–Predictor for Few-Shot Learning
    Nan Lai, Meina Kan, Chunrui Han, Xingguang Song, Shiguang Shan · 2020 · IEEE Transactions on Neural Networks and Learning Systems
  42. All-fiber high-speed image detection enabled by deep learning
    Zhoutian Liu, Lele Wang, Yuan Meng, Tiantian He, Sifeng He, Yousi Yang, Liuyue Wang, Jiading Tian, Dan Li, Ping Yan et al. · 2022 · Nature Communications
  43. The Omniglot challenge: a 3-year progress report
    Joshua B. Tenenbaum · 2019 · DSpace@MIT (Massachusetts Institute of Technology)
  44. Nanomaterials Discovery and Design through Machine Learning
    Ming Wang, Ting Wang, Pingqiang Cai, Xiaodong Chen · 2019 · Small Methods
  45. Robust high-dimensional memory-augmented neural networks
    Geethan Karunaratne, Manuel Schmuck, Manuel Le Gallo, Giovanni Cherubini, Luca Benini C, Abu Sebastian, Abbas Rahimi · 2021 · Nature Communications
  46. Algorithmic fairness datasets: the story so far
    Alessandro Fabris, Stefano Messina, Gianmaria Silvello, Gian Antonio Susto · 2022 · Data Mining and Knowledge Discovery
  47. One-Shot Generalization in Deep Generative Models
    Danilo Jimenez Rezende, Shakir Mohamed, Ivo Danihelka, Karol Gregor, Daan Wierstra · 2016 · arXiv (Cornell University)
  48. FoodAI
    Doyen Sahoo, Hao Wang, Shu Ke, Wu Xiongwei, Hung Le, Palakorn Achananuparp, Ee‐Peng Lim, Steven C. H. Hoi · 2019 · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)
  49. A historical survey of algorithms and hardware architectures for neural-inspired and neuromorphic computing applications
    Conrad D. James, James B. Aimone, Nadine E. Miner, Craig Michael Vineyard, Fredrick H. Rothganger, Kristofor D. Carlson, Samuel A. Mulder, Timothy J. Draelos, Aleksandra Faust, Matthew J. Marinella et al. · 2017 · Biologically Inspired Cognitive Architectures
  50. Aspect based sentiment analysis by a linguistically regularized CNN with gated mechanism
    Dao-Jian Zeng, Yuan Dai, Feng Li, Jin Wang, A. Sangaiah · 2019 · Journal of Intelligent & Fuzzy Systems
  51. Deep Learning for Biospectroscopy and Biospectral Imaging: State-of-the-Art and Perspectives
    Hao He, Sen Yan, Danya Lyu, Mengxi Xu, Ruiqian Ye, Peng Zheng, Xinyu Lu, Lei Wang, Bin W. Ren · 2021 · Analytical Chemistry
  52. Three-way concept learning based on cognitive operators: An information fusion viewpoint
    Chenchen Huang, Jinhai Li, Changlin Mei, Wei-Zhi Wu · 2017 · International Journal of Approximate Reasoning
  53. Seeing social interactions
    Emalie McMahon, Leyla Işık · 2023 · Trends in Cognitive Sciences
  54. Domain generalization through meta-learning: a survey
    Arsham Gholamzadeh Khoee, Yinan Yu, ROBERT H. FELDT · 2024 · Artificial Intelligence Review
  55. A foundation model to predict and capture human cognition
    Binz, Marcel, Akata, Elif, Bethge, Matthias, Brändle, Franziska, Callaway, Fred, Coda-Forno, Julian, Dayan, Peter, Demircan, Can, Eckstein, Maria K., Éltető, Noémi et al. · 2025 · Nature
  56. Toward the neural implementation of structure learning
    Tervo, D Gowanlock R, Tenenbaum, Joshua B, Gershman, Samuel J · 2016 · Current Opinion in Neurobiology
  57. The Predictive Processing Paradigm Has Roots in Kant
    Link Ray Swanson · 2016 · Frontiers in Systems Neuroscience
  58. Lifelong generative modeling
    Jason Ramapuram, Magda Gregorová, Alexandros Kalousis · 2020 · Neurocomputing
  59. Holistic Reinforcement Learning: The Role of Structure and Attention
    Angela Radulescu, Yael Niv, Ian C. Ballard · 2019 · Trends in Cognitive Sciences
  60. MetaSense
    Taesik Gong, Yeonsu Kim, Jinwoo Shin, Sung-Ju Lee · 2019 · ACM Conference on Embedded Networked Sensor Systems (SenSys)
  61. Domain Knowledge Powered Two-Stream Deep Network for Few-Shot SAR Vehicle Recognition
    Linbin Zhang, Xiangguang Leng, Sijia Feng, Xiaojie Ma, Kefeng Ji, Gangyao Kuang, Li Liu · 2021 · IEEE Transactions on Geoscience and Remote Sensing
  62. Neural Networks for Modeling and Control of Particle Accelerators
    Auralee L. Edelen, S.G. Biedroń, Brian E. Chase, Dean Edstrom, S.V. Milton, Pierpaolo Stabile · 2016 · IEEE Transactions on Nuclear Science
  63. A New Model for Stock Price Movements Prediction Using Deep Neural Network
    Huy Duc Huynh, L. Minh Dang, Duc Minh Duong · 2017 · International Symposium on Information and Communication Technology
  64. Few-shot Learning: A Survey
    Yaqing Wang, Quanming Yao · 2019 · arXiv (Cornell University)
  65. Bayesian Decision Models: A Primer
    Wei Ji Ma · 2019 · Neuron
  66. Trace Norm Regularised Deep Multi-Task Learning
    Yongxin Yang, Timothy M. Hospedales · 2016 · arXiv (Cornell University)
  67. Not-So-CLEVR: learning same–different relations strains feedforward neural networks
    Junkyung Kim, Matthew R. Ricci, Thomas Serre · 2018 · Interface Focus
  68. DreamCoder: bootstrapping inductive program synthesis with wake-sleep library learning
    Ellis, Kevin, Wong, Catherine, Nye, Maxwell, Sablé-Meyer, Mathias, Morales, Lucas, Hewitt, Luke, Cary, Luc, Solar-Lezama, Armando, Tenenbaum, Joshua B. · 2021 · Proceedings of the 42nd ACM SIGPLAN International Conference on Programming Language Design and Implementation
  69. Differentiable plasticity: training plastic neural networks with backpropagation
    Thomas Miconi, Jeff Clune, Kenneth Owen Stanley · 2018 · arXiv (Cornell University)
  70. Automatically Discovering and Learning New Visual Categories with Ranking Statistics
    Kai Han, Sylvestre-Alvise Rebuffi, Sébastien Ehrhardt, Andrea Vedaldi, Andrew Zisserman · 2020 · arXiv (Cornell University)
  71. Adaptive Augmentation of Medical Data Using Independently Conditional Variational Auto-Encoders
    Mehran Pesteie, Purang Abolmaesumi, Robert N. Rohling · 2019 · IEEE Transactions on Medical Imaging
  72. PEDLA: predicting enhancers with a deep learning-based algorithmic framework
    Liu, Feng, Li, Hao, Ren, Chao, Bo, Xiaochen, Shu, Wenjie · 2016 · Scientific Reports
  73. Learning to Write Stylized Chinese Characters by Reading a Handful of Examples
    Danyang Sun, Tongzheng Ren, Chongxuan Li, Hang Su, Jun Zhu · 2018 · International Joint Conference on Artificial Intelligence
  74. A User’s Guide to Machine Learning for Polymeric Biomaterials
    Travis A. Meyer, Cesar Ramirez, Matthew Tamasi, Adam J. Gormley · 2022 · ACS Polymers Au
  75. Artificial-intelligence-led revolution of construction materials: From molecules to Industry 4.0
    Xing Quan Wang, Pengguang Chen, Cheuk Lun Chow, Denvid Lau · 2023 · Matter
  76. M-FCCL: Memory-based concept-cognitive learning for dynamic fuzzy data classification and knowledge fusion
    Doudou Guo, Weihua Xu, Yuhua Qian, Weiping Ding · 2023 · Information Fusion
  77. Generative replay underlies compositional inference in the hippocampal-prefrontal circuit
    Philipp Schwartenbeck, Alon Boaz Baram, Yunzhe Liu, Shirley Mark, Timothy Müller, Raymond J. Dolan, Matthew Botvinick, Zeb L. Kurth-Nelson, Timothy E.J. Behrens · 2023 · Cell
  78. Investigating Meta-Learning Algorithms for Low-Resource Natural Language Understanding Tasks
    Zi-Yi Dou, Keyi Yu, Antonios Anastasopoulos · 2019 · Conference on Empirical Methods in Natural Language Processing (EMNLP)
  79. Universal Representation Learning from Multiple Domains for Few-shot Classification
    Weihong Li, Xialei Liu, Hakan Bilen · 2021 · IEEE/CVF International Conference on Computer Vision (ICCV)
  80. Contextual inference in learning and memory
    James B. Heald, Máté Lengyel, Daniel M. Wolpert · 2022 · Trends in Cognitive Sciences
  81. Drawing as a versatile cognitive tool
    Judith E. Fan, Wilma Alice Bainbridge, Rebecca Chamberlain, Jeffrey D. Wammes · 2023 · Nature Reviews Psychology
  82. The Generalized Reparameterization Gradient
    Francisco J. R. Ruiz, Michalis K. Titsias, David M. Blei · 2016 · arXiv (Cornell University)
  83. An Active Inference Approach to Modeling Structure Learning: Concept Learning as an Example Case
    Ryan S. Smith, Philipp Schwartenbeck, Thomas Parr, Karl John Friston · 2020 · Frontiers in Computational Neuroscience
  84. Fuzzy-Granular Concept-Cognitive Learning via Three-Way Decision: Performance Evaluation on Dynamic Knowledge Discovery
    Doudou Guo, Weihua Xu, Yuhua Qian, Weiping Ding · 2023 · IEEE Transactions on Fuzzy Systems
  85. Cognitive Science: An Introduction to the Science of the Mind
    José Luis Bermúdez · 2020
  86. Evolving Rule-Based Explainable Artificial Intelligence for Unmanned Aerial Vehicles
    Blen Keneni, Devinder Kaur, Ali S. Al Bataineh, Vijaya Kumar Devabhaktuni, Ahmad Y. Javaid, Jack D. Zaientz, Robert Marinier · 2019 · IEEE Access
  87. Artistic glyph image synthesis via one-stage few-shot learning
    Yue Gao, Yuan Guo, Zhouhui Lian, Yingmin Tang, Jianguo Xiao · 2019 · ACM Transactions on Graphics
  88. Patients’ perceptions of using artificial intelligence (AI)-based technology to comprehend radiology imaging data
    Zhan Zhang, Daniel Citardi, Dakuo Wang, Yegin Genc, Juan Shan, Xiangmin Fan · 2021 · Health Informatics Journal
  89. A survey on machine learning from few samples
    Lu, Jiang, Gong, Pinghua, Ye, Jieping, Zhang, Jianwei, Zhang, Changshui · 2023 · Pattern Recognition
  90. Deep Meta-Learning: Learning to Learn in the Concept Space
    Fengwei Zhou, Bin Wu, Zhenguo Li · 2018 · arXiv (Cornell University)
  91. Few-Shot Segmentation Propagation with Guided Networks
    Kate Rakelly, Evan Shelhamer, Trevor Darrell, Alexei A. Efros, Sergey Levine · 2018 · arXiv (Cornell University)
  92. Attentive Recurrent Comparators
    Pranav Shyam, Shubham Gupta, Ambedkar Dukkipati · 2017 · NOT FOUND REPOSITORY (Indian Institute of Science Bangalore)
  93. Ways of applying artificial intelligence in software engineering
    ROBERT H. FELDT, Francisco Gomes de Oliveira Neto, Richard Torkar · 2018 · International Workshop on Realizing Artificial Intelligence Synergies in Software Engineering (RAISE)
  94. Soft-Label Dataset Distillation and Text Dataset Distillation
    Ilia Sucholutsky, Matthias Schonlau · 2021 · International Joint Conference on Neural Networks (IJCNN)
  95. Two-Way Concept-Cognitive Learning via Concept Movement Viewpoint
    Weihua Xu, Doudou Guo, Jusheng Mi, Yuhua Qian, Keyin Zheng, Weiping Ding · 2023 · IEEE Transactions on Neural Networks and Learning Systems
  96. Off-line handwritten signature verification using compositional synthetic generation of signatures and Siamese Neural Networks
    Victoria Ruiz-Parrado, Ismael Linares, Ángel Sánchez, José F. Vélez · 2019 · Neurocomputing
  97. Multi-class Classification without Multi-class Labels
    Yen-Chang Hsu, Zhaoyang Lv, Joel Schlosser, Phillip Odom, Zsolt Kira · 2019 · arXiv (Cornell University)
  98. Mechanisms for Enhanced State Retention and Stability in Redox‐Gated Organic Neuromorphic Devices
    Scott Tom Keene, Armantas Melianas, Yoeri van de Burgt, Alberto Salleo · 2018 · Advanced Electronic Materials
  99. Compositional Inductive Biases in Function Learning
    Eric Schulz, Joshua B. Tenenbaum, David Duvenaud, Maarten Speekenbrink, Samuel Joseph Gershman · 2016 · Cognitive Psychology
  100. Integrative Few-Shot Learning for Classification and Segmentation
    Dahyun Kang, Minsu Cho · 2022 · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings