mlsanityData to 7 Oct 2026

Building machines that adapt and compute like brains

arXiv 1711.04203 ↗First posted 11 Nov 2017cs.AI q-bio.NCcited by 99

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Abstract (arXiv)

Building machines that learn and think like humans is essential not only for cognitive science, but also for computational neuroscience, whose ultimate goal is to understand how cognition is implemented in biological brains. A new cognitive computational neuroscience should build cognitive-level and neural- level models, understand their relationships, and test both types of models with both brain and behavioral data.

Key facts. Cited by 99 papers in the map (data through 7 Oct 2026). First posted on arXiv 11 Nov 2017.

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Citing papers per year: 2019: 1; 2020: 0; 2021: 51; 2022: 37; 2023: 10.

99 papers.

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  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
  2. 2021Self-Supervised Learning with Data Augmentations Provably Isolates Content from Stylecited by 308
  3. 2021Deep Stable Learning for Out-Of-Distribution Generalizationcited by 153
  4. 2021Pretraining Representations for Data-Efficient Reinforcement Learningcited by 105
  5. 2021Counterfactual Zero-Shot and Open-Set Visual Recognitioncited by 95
  6. 2022Compositional Semantic Parsing with Large Language Modelscited by 95
  7. 2022Tutorial on amortized optimizationcited by 83
  8. 2021Generalization and Robustness Implications in Object-Centric Learningcited by 81
  9. 2021Detecting Human-Object Interaction via Fabricated Compositional Learningcited by 79
  10. 2023Strategic Reasoning with Language Modelscited by 64
  11. 2021Counterfactuals and Causability in Explainable Artificial Intelligence: Theory, Algorithms, and Applicationscited by 59
  12. 2022Compositional Temporal Grounding with Structured Variational Cross-Graph Correspondence Learningcited by 59
38 more
  1. 2021Hybrid Intelligencecited by 55
  2. 2021Vector Quantized Models for Planningcited by 47
  3. 2023Provably Learning Object-Centric Representationscited by 45
  4. 2021Leveraging Language to Learn Program Abstractions and Search Heuristicscited by 44
  5. 2021Human-Level Reinforcement Learning through Theory-Based Modeling, Exploration, and Planningcited by 43
  6. 2022Compositional Generalization in Unsupervised Compositional Representation Learning: A Study on Disentanglement and Emergent Languagecited by 42
  7. 2021Pyramid Adversarial Training Improves ViT Performancecited by 39
  8. 2022Improving Human-AI Partnerships in Child Welfare: Understanding Worker Practices, Challenges, and Desires for Algorithmic Decision Supportcited by 37
  9. 2021From Machine Learning to Robotics: Challenges and Opportunities for Embodied Intelligencecited by 35
  10. 2022A Review of Emerging Research Directions in Abstract Visual Reasoningcited by 33
  11. 2021Attention Approximates Sparse Distributed Memorycited by 29
  12. 2022Probabilistic Compositional Embeddings for Multimodal Image Retrievalcited by 29
  13. 2021Towards a Collective Agenda on AI for Earth Science Data Analysiscited by 27
  14. 2021Which priors matter? Benchmarking models for learning latent dynamicscited by 26
  15. 2021Understanding the Dynamics of DNNs Using Graph Modularitycited by 26
  16. 2022Symmetry-Based Representations for Artificial and Biological General Intelligencecited by 26
  17. 2021COVR: A test-bed for Visually Grounded Compositional Generalization with real imagescited by 25
  18. 2021Separating Skills and Concepts for Novel Visual Question Answeringcited by 24
  19. 2021SKID RAW: Skill Discovery from Raw Trajectoriescited by 22
  20. 2022Equi-Tuning: Group Equivariant Fine-Tuning of Pretrained Modelscited by 22
  21. 2021Capturing the objects of vision with neural networkscited by 21
  22. 2021Composing Partial Differential Equations with Physics-Aware Neural Networkscited by 21
  23. 2021Inducing Transformer's Compositional Generalization Ability via Auxiliary Sequence Prediction Taskscited by 19
  24. 2022Abstraction for Deep Reinforcement Learningcited by 19
  25. 2022Offline Reinforcement Learning with Causal Structured World Modelscited by 19
  26. 2022Toward Supporting Perceptual Complementarity in Human-AI Collaboration via Reflection on Unobservablescited by 19
  27. 2021Dissonance Between Human and Machine Understandingcited by 18
  28. 2021Understanding the World Through Actioncited by 17
  29. 2021Inspecting the concept knowledge graph encoded by modern language modelscited by 15
  30. 2022Recent Advances in Neural Text Generation: A Task-Agnostic Surveycited by 14
  31. 2021Benchmarks, Algorithms, and Metrics for Hierarchical Disentanglementcited by 12
  32. 2021Mitigating belief projection in explainable artificial intelligence via Bayesian Teachingcited by 10
  33. 2021X-GGM: Graph Generative Modeling for Out-of-Distribution Generalization in Visual Question Answeringcited by 10
  34. 2021Quantifying Local Specialization in Deep Neural Networkscited by 10
  35. 2019From explanation to synthesis: Compositional program induction for learning from demonstrationcited by 9
  36. 2021Robot Skill Adaptation via Soft Actor-Critic Gaussian Mixture Modelscited by 9
  37. 2022Meta-learning Spiking Neural Networks with Surrogate Gradient Descentcited by 9
  38. 2022The developmental trajectory of object recognition robustness: children are like small adults but unlike big deep neural networkscited by 9

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