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REVIEW 3 major objections 5 minor 40 references

Neuro-Symbolic Concepts

T0 review · 3 major / 5 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read The paper argues that agents should be built from a vocabulary of typed neuro-symbolic concepts, each represented as a triple of parameters, a program, and neural-network grounding, and that this design yields data efficiency…

desk verdict A clean, honest position paper that unifies the authors' prior neuro-symbolic work under one notation; the evidence is self-sourced and mostly synthetic, and the fixed-DSL caveat is real but openly acknowledged. read the letter →

arxiv 2505.06191 v1 pith:3FJIH6QZ submitted 2025-05-09 cs.AI cs.CLcs.CVcs.LGcs.RO

Jiayuan Mao , Joshua B. Tenenbaum , Jiajun Wu This is my paper · ORCID
classification cs.AIcs.CLcs.CVcs.LGcs.RO
keywords neuro-symbolicconceptsconceptlearningcompositionalgeneralizationcontinualzero-shottransfervisualquestionansweringroboticmanipulationprogramexecution
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper argues that intelligent agents should be built around a vocabulary of neuro-symbolic concepts—object, relation, and action concepts, each stored as a typed triple of parameters, a symbolic program, and a neural-network grounding. The claim is that this representation gives agents four properties that end-to-end neural networks struggle to combine: data efficiency, compositional generalization, continual learning, and zero-shot transfer. That claim matters because embodied tasks are costly to annotate, so a system that can learn concepts from a few examples and recombine them across scenes, modalities, and goals is a practical path toward generalist agents. The concrete demonstration is the Neuro-Symbolic Concept Learner (NS-CL), which learns visual concepts from images and question-answer pairs and executes compositional programs over them.

What carries the argument

The central object is the neuro-symbolic concept triple. The program component is a typed symbolic function in a DSL, such as a filter, a relation, or an action with pre- and post-conditions, while the neural component is a learned embedding that grounds the concept symbol in perception. The argument is carried by executing these programs differentiably over probabilistic object masks, which lets concept embeddings be learned from question-answer pairs and lets novel concepts be formed by composing existing programs.

What would settle it

Train the NS-CL system on a visual question-answering suite whose test questions require a relation among four or more objects or a scene-level concept absent from the predefined DSL; if accuracy falls to chance while a baseline with an extended DSL succeeds, the claim that the fixed vocabulary supports flexible reasoning fails for that class.

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Extended reading notes

Core claim

The paper's central claim is that representing each concept as a triple of parameters, a program, and neural networks is enough to support generalist learning. The program is a typed function in a domain-specific language: for example, an object concept is a filter, a relation is a relate operation, and an action includes preconditions, postconditions, and a controller. The neural part is an embedding that grounds the symbol in sensory input or actuation output. Reasoning is program execution over probabilistic attention masks, so gradients flow back to concept embeddings and concepts can be learned from natural supervision. The authors report that NS-CL reaches 98.9% accuracy on CLEVR when trained on only 10% of the data, surpasses baselines by 14 percentage points, generalizes to scenes and questions more complex than those in training, and that follow-up systems extend the scheme to continual learning, video reasoning, 3D scenes, and robotic manipulation.

Load-bearing premise

The framework assumes that a fixed, hand-defined set of DSL primitives—filter, relate, count, and action controllers—is expressive enough to cover the concepts a real task domain needs, a limitation the paper itself acknowledges in Section 5.

Editorial extensions

If this is right

  • Agents can be markedly data-efficient: NS-CL reaches 98.9% accuracy on CLEVR when trained on 10% of the training data.
  • The same learned concepts generalize compositionally to scenes with more objects and to questions with more complex structure than seen in training.
  • New concepts can be added continually: FALCON acquires a novel visual concept from one or a few examples by combining image evidence, captions, and conceptual relations.
  • Learned concepts transfer across tasks and domains, for example from visual question answering to image retrieval and from image concepts to robotic manipulation instructions.
  • Disentangling grounding from reasoning—neural embeddings for concepts plus deterministic DSL operations for composition—is what the paper identifies as the source of these benefits.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • A reader could test the boundary of the thesis by extending the DSL with loops or recursion: if the same concept-triple representation can handle algorithmic tasks, the framework becomes a route from perceptual concepts to full program induction, but gradient flow through long execution traces will likely need curriculum or auxiliary supervision.
  • The cross-domain concept library the paper calls for would make concepts like 'close to' a single shared abstract notion grounded separately in each modality, offering a modular alternative to monolithic vision-language models.
  • One could quantify the claimed advantage by comparing a concept-centric agent with an end-to-end model on a suite of tasks whose test concepts are deliberately held out of the DSL; the thesis predicts the performance gap widens as task complexity grows.
  • If high-arity and scene-level concepts turn out not to be expressible with the primitive vocabulary, the practical route is either grammar-based lexicon learning or LLM-generated programs with freshly initialized concept embeddings, both of which the paper mentions as open directions.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 5 minor

Summary. The paper proposes a concept-centric paradigm for building agents that can learn continually and reason flexibly, in which knowledge is represented as typed neuro-symbolic concepts of the form ⟨parameter, program, neural-nets⟩. It presents the Neuro-Symbolic Concept Learner (NS-CL) as a concrete instantiation, argues that this decomposition yields data efficiency, compositional generalization, continual learning, and zero-shot transfer, and surveys extensions to 3D scenes, video, human motion, and robotic manipulation. The paper contains no new experiments; its evidence is drawn primarily from the authors' prior peer-reviewed publications. Section 5 acknowledges a key limitation: the reliance on a predefined domain-specific language (DSL) and hand-authored concept vocabulary.

Significance. If the paradigm delivers the claimed properties, it could be an important step toward generalist embodied agents that learn from limited data and compose knowledge across tasks. The paper's strengths include a clear formalization of concepts as tuples of symbolic programs and neural embeddings, a lucid explanation of how modular structure aids data efficiency (e.g., NS-CL's 98.9% CLEVR accuracy with 10% data), and a broad survey of applications. However, the evidence is concentrated in synthetic or controlled settings, and the central claim is contingent on the expressiveness of the hand-authored DSL, a premise that the paper itself states in Section 5 is an open problem. As a perspective article, it succeeds in synthesizing a body of work, but it does not establish the general validity of the paradigm beyond DSL-covered tasks.

major comments (3)
  1. [Abstract and Section 1 vs. Section 5] The abstract and Section 1 state that the concept-centric framework 'offers' data efficiency, compositional generalization, continual learning, and zero-shot transfer. Section 5 concedes that many neuro-symbolic concept learning works rely on a predefined DSL and that automatic vocabulary expansion is an open direction. All cited demonstrations (NS-CL, FALCON, NS3D, and the robotics applications) presuppose a hand-authored DSL with concept symbols such as ORANGE and PUT-LEFT. The claim as stated is therefore not established for concepts that fall outside the predefined vocabulary. The paper should add an explicit qualification in the abstract and introduction that these properties are demonstrated for tasks whose required concepts are within the DSL, and it should discuss whether the proposed expansion routes (grammar-based lexicon learning, LLM-generated symbols, theory composition) preserve the advertised properties.
  2. [Section 3 (Continual learning and Transfer learning)] The paper presents continual learning and zero-shot transfer as intrinsic advantages of the paradigm. The concrete evidence in Section 3 consists of FALCON, which operates in a class-incremental setting, and a synthetic image retrieval experiment using simple captions; the paper acknowledges in Section 5 that full curriculum learning and unsupervised concept learning remain unsolved. The presentation in Section 3 should be tempered to reflect these limitations, or should incorporate details from the more substantial robotics transfer results cited in Section 4 (Wang et al. [37], Kalithasan et al. [18]) to strengthen the support for the transfer claim.
  3. [Section 2] The formal definition of a concept as ⟨parameter, program, neural-nets⟩ is not fully consistent across the examples. For object and relation concepts, the 'program' is a single function (filter or relate), but for action concepts the program is a triple of preconditions, postconditions, and a controller; the typing and composition rules for action concepts are not specified with the same precision. The paper should either formalize the action concept representation more rigorously or explicitly label the notation as illustrative.
minor comments (5)
  1. [Section 2, Fig. 2a] In Fig. 2a, the caption contains a typo, showing '#ilter' instead of 'filter'; please correct it.
  2. [Section 5] In Section 5, the sentence 'This DSL encompasses primitive operators such as filter and relate, as well as concept symbols such as orange and place' uses 'place' inconsistently with the earlier examples that use 'put-left-of'; please align the concept-symbol terminology.
  3. [References] Reference [26] (Morton 1980) is an unusual citation for the theory-theory of concepts; a standard reference such as Gopnik and Meltzoff, or Murphy and Medin, would be more appropriate for this widely discussed position.
  4. [Section 4] The statement in Section 4 that the framework can 'integrate with external knowledge bases' cites references [4, 34], but reference [4] (Barbiero et al.) is about interpretable neuro-symbolic reasoning rather than external knowledge bases; please verify the citation.
  5. [Figure 7a] Figure 7a would benefit from showing the exact percentages next to the bars, as the text mentions specific numbers but the figure axis labels are not legible.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the paradigm claims are architectural and supported by external benchmark evaluations; the predefined-DSL limitation is explicitly acknowledged, not a circular reduction.

full rationale

This manuscript is a position and survey article rather than a derivation with fitted parameters. Its central object, the neuro-symbolic concept tuple c = <parameter, program, neural-nets>, is a definition, and the claimed benefits (data efficiency, compositional generalization, continual learning, zero-shot transfer) are argued from that architecture and then illustrated on external benchmarks such as CLEVR (Fig. 7), compositional generalization splits, and few-shot continual-learning datasets. The supporting systems (NS-CL, FALCON, the manipulation frameworks) are prior works by the same authors, but the cited evaluations are held-out benchmark results that are externally falsifiable; thus the self-citations serve as evidence rather than as circular justification. No equation in the paper is defined in terms of the result it is used to support, and no fitted parameter is renamed as a prediction. The genuinely load-bearing assumption is the hand-defined DSL, and the paper itself flags this in Section 5: 'many works on neuro-symbolic concept learning have the limitation of relying on a predefined DSL.' It also lists grammar-based lexicon learning, LLM-generated symbols, and theory composition as future directions rather than tested components. That is a scope limitation and a correctness risk for the general framing, not a circular reduction of the derivation chain. Accordingly, no specific circular step is identified.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

The paper is a review and position piece, so the ledger is minimal. It introduces no free parameters and no invented entities. Its load-bearing assumptions are domain assumptions about compositionality and the sufficiency of a predefined DSL, both explicitly acknowledged.

assumptions (3)
  • domain assumption Concepts are compositional: they can be combined via symbolic functional composition to form new concepts (Section 2).
    The entire framework relies on compositionality as a design principle; no proof is offered that all useful concepts are compositional in this way.
  • domain assumption A predefined domain-specific language with primitives such as filter, relate, count, and action controllers is sufficient for the target tasks (Section 5, limitation).
    The authors acknowledge this limitation: 'relying on a predefined set of concepts can be too restrictive in many real-world applications.'
  • domain assumption Neural embeddings can be grounded to object properties and relations by cosine similarity to concept embeddings (Section 2).
    The concrete implementations use embedding similarity as the grounding mechanism, which is an empirical modeling choice, not a theorem.

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Cite this review

Pith. "Pith review of Neuro-Symbolic Concepts." pith.science (2026). https://pith.science/paper/3FJIH6QZ

@misc{pith2026250506191,
  author       = {Pith},
  title        = {Pith review of: Neuro-Symbolic Concepts},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/3FJIH6QZ}},
  note         = {Machine review of arXiv:2505.06191}
}
read the original abstract

This article presents a concept-centric paradigm for building agents that can learn continually and reason flexibly. The concept-centric agent utilizes a vocabulary of neuro-symbolic concepts. These concepts, such as object, relation, and action concepts, are grounded on sensory inputs and actuation outputs. They are also compositional, allowing for the creation of novel concepts through their structural combination. To facilitate learning and reasoning, the concepts are typed and represented using a combination of symbolic programs and neural network representations. Leveraging such neuro-symbolic concepts, the agent can efficiently learn and recombine them to solve various tasks across different domains, ranging from 2D images, videos, 3D scenes, and robotic manipulation tasks. This concept-centric framework offers several advantages, including data efficiency, compositional generalization, continual learning, and zero-shot transfer.

Figures

Figures reproduced from arXiv: 2505.06191 by the authors.

Figure 1
Figure 1. Our framework for building intelligent agents by internalizing a vocabulary of “concepts,” which are [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. In a neuro-symbolic concept-centric framework, different concepts such as object categories, properties, [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Three challenges in (a): Compositional generalization. (b): Continual learning of concepts for reasoning. [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figures from the paper (4 more)
Figure 4
Figure 4. Figure 4: Humans learn visual concepts, words, and semantic parsing jointly and incrementally. [PITH_FULL_IMAGE:figures/full_fig_p007_4.png]
Figure 5
Figure 5. Figure 5: NS-CL uses neural symbolic reasoning to bridge the learning of visual concepts, words, and semantic [PITH_FULL_IMAGE:figures/full_fig_p007_5.png]
Figure 6
Figure 6. Figure 6: The neuro-symbolic execution procedure of a program based on the visual representation and concept [PITH_FULL_IMAGE:figures/full_fig_p007_6.png]
Figure 7
Figure 7. Figure 7: Data efficiency test and compositional generalization test for the Neuro-Symbolic Concept Learner [PITH_FULL_IMAGE:figures/full_fig_p008_7.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

40 extracted references · 40 canonical work pages

  1. [37]

    Renhao Wang, Jiayuan Mao, Joy Hsu, Hang Zhao, Jiajun Wu, and Yang Gao. 2023. Programmatically Grounded, Compositionally Generalizable Robotic Manipulation. InICLR

    work page 2023

  2. [18]

    Namasivayam Kalithasan, Himanshu Singh, Vishal Bindal, Arnav Tuli, Vishwajeet Agrawal, Rahul Jain, Parag Singla, and Rohan Paul. 2023. Learning Neuro-Symbolic Programs for Language Guided Robot Manipulation. InICRA

    work page 2023

  3. [1]

    Omri Abend, Tom Kwiatkowski, Nathaniel J Smith, Sharon Goldwater, and Mark Steedman. 2017. Bootstrapping Language Acquisition.Cognition164 (2017), 116–143

    work page 2017

  4. [2]

    Saeed Amizadeh, Hamid Palangi, Oleksandr Polozov, Yichen Huang, and Kazuhito Koishida. 2020. Neuro-Symbolic Visual Reasoning: Disentangling ‘Visual’ from ‘Reasoning’. InICML

    work page 2020

  5. [3]

    Jacob Andreas, Marcus Rohrbach, Trevor Darrell, and Dan Klein. 2016. Neural Module Networks. InCVPR

    work page 2016

  6. [4]

    Pietro Barbiero, Gabriele Ciravegna, Francesco Giannini, Mateo Espinosa Zarlenga, Lucie Charlotte Magister, Alberto Tonda, Pietro Lió, Frederic Precioso, Mateja Jamnik, and Giuseppe Marra. 2023. Interpretable Neural-Symbolic Concept Reasoning. InICML

    work page 2023

  7. [5]

    Tenenbaum, and Chuang Gan

    Zhenfang Chen, Jiayuan Mao, Jiajun Wu, Kwan-Yee K Wong, Joshua B. Tenenbaum, and Chuang Gan. 2021. Grounding Physical Concepts of Objects and Events Through Dynamic Visual Reasoning. InICLR

    work page 2021

  8. [6]

    Mayukh Das, Nandini Ramanan, Janardhan Rao Doppa, and Sriraam Natarajan. 2020. Few-Shot Induction of Generalized Logical Concepts via Human Guidance.Frontiers in Robotics and AI7 (2020), 122

    work page 2020

Show all 40 references
  1. [7]

    Thomas Demeester, Tim Rocktäschel, and Sebastian Riedel. 2016. Lifted Rule Injection for Relation Embeddings. In EMNLP

    2016

  2. [8]

    Surís Dídac, Sachit Menon, and Carl Vondrick. 2023. ViperGPT: Visual Inference via Python Execution for Reasoning. InICCV

    2023

  3. [9]

    Tenenbaum, and Chuang Gan

    Mingyu Ding, Zhenfang Chen, Tao Du, Ping Luo, Joshua B. Tenenbaum, and Chuang Gan. 2021. Dynamic Visual Reasoning by Learning Differentiable Physics Models from Video and Language. InNeurIPS

    2021

  4. [10]

    Kevin Ellis, Lionel Wong, Maxwell Nye, Mathias Sable-Meyer, Luc Cary, Lore Anaya Pozo, Luke Hewitt, Armando Solar-Lezama, and Joshua B Tenenbaum. 2023. DreamCoder: Growing Generalizable, Interpretable Knowledge with Wake–Sleep Bayesian Program Learning.Philosophical Transactio...

    2023

  5. [11]

    Mark Endo, Joy Hsu, Jiaman Li, and Jiajun Wu. 2023. Motion Question Answering via Modular Motion Programs. In ICML

    2023

  6. [12]

    Afsaneh Fazly, Afra Alishahi, and Suzanne Stevenson. 2010. A Probabilistic Computational Model of Cross-Situational Word Learning.Cognit. Sci.34, 6 (2010), 1017–1063

    2010

  7. [13]

    Nir Friedman, Lise Getoor, Daphne Koller, and Avi Pfeffer. 1999. Learning Probabilistic Relational Models. InIJCAI

    1999

  8. [14]

    Tenenbaum, and Jiajun Wu

    Chi Han, Jiayuan Mao, Chuang Gan, Joshua B. Tenenbaum, and Jiajun Wu. 2019. Visual Concept Metaconcept Learning. InNeurIPS

    2019

  9. [15]

    Joy Hsu, Jiayuan Mao, Joshua B Tenenbaum, Noah D Goodman, and Jiajun Wu. 2025. What Makes a Maze Look Like a Maze?. InICLR

    2025

  10. [16]

    Joy Hsu, Jiayuan Mao, and Jiajun Wu. 2023. NS3D: Neuro-Symbolic Grounding of 3D Objects and Relations. InCVPR

    2023

  11. [17]

    Justin Johnson, Bharath Hariharan, Laurens van der Maaten, Li Fei-Fei, C Lawrence Zitnick, and Ross Girshick. 2017. CLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoning. InCVPR. Communications of The ACM 14 Mao, Tenenbaum, and Wu

    2017

  12. [19]

    Qing Li, Siyuan Huang, Yining Hong, and Song-Chun Zhu. 2020. A Competence-Aware Curriculum for Visual Concepts Learning via Question Answering. InECCV

    2020

  13. [20]

    Robin Manhaeve, Sebastijan Dumancic, Angelika Kimmig, Thomas Demeester, and Luc De Raedt. 2018. Deepproblog: Neural Probabilistic Logic Programming. InNeurIPS

    2018

  14. [21]

    Tenenbaum, and Jiajun Wu

    Jiayuan Mao, Chuang Gan, Pushmeet Kohli, Joshua B. Tenenbaum, and Jiajun Wu. 2019. The Neuro-Symbolic Concept Learner: Interpreting Scenes, Words, and Sentences From Natural Supervision. InICLR

    2019

  15. [22]

    Tenenbaum, and Leslie Kaelbling

    Jiayuan Mao, Tomás Lozano-Pérez, Joshua B. Tenenbaum, and Leslie Kaelbling. 2022. PDSketch: Integrated Domain Programming, Learning, and Planning. InNeurIPS

    2022

  16. [23]

    Levy, and Joshua B

    Jiayuan Mao, Haoyue Shi, Jiajun Wu, Roger P. Levy, and Joshua B. Tenenbaum. 2021. Grammar-based Grounded Language Learning. InNeurIPS

    2021

  17. [24]

    1999.Concepts: Core Readings

    Eric Margolis and Stephen Laurence. 1999.Concepts: Core Readings. The MIT Press

    1999

  18. [25]

    Lingjie Mei, Jiayuan Mao, Ziqi Wang, Chuang Gan, and Joshua B Tenenbaum. 2022. FALCON: Fast Visual Concept Learning by Integrating Images, Linguistic Descriptions, and Conceptual Relations. InICLR

    2022

  19. [26]

    1980.Frames of Mind: Constraints on the Common-Sense Conception of the Mental

    Adam Morton. 1980.Frames of Mind: Constraints on the Common-Sense Conception of the Mental. Oxford University Press

    1980

  20. [27]

    Stephen Muggleton and Luc De Raedt. 1994. Inductive Logic Programming: Theory and Methods.The Journal of Logic Programming19 (1994), 629–679

    1994

  21. [28]

    Felix Petersen, Christian Borgelt, Hilde Kuehne, and Oliver Deussen. 2021. Learning with Algorithmic Supervision via Continuous Relaxations. InNeurIPS

    2021

  22. [29]

    Mihir Prabhudesai, Shamit Lal, Darshan Patil, Hsiao-Yu Tung, Adam W Harley, and Katerina Fragkiadaki. 2021. Disentangling 3D Prototypical Networks for Few-Shot Concept Learning. InICLR

    2021

  23. [30]

    Tim Rocktäschel, Sameer Singh, and Sebastian Riedel. 2015. Injecting Logical Background Knowledge into Embeddings for Relation Extraction. InNAACL-HLT

    2015

  24. [31]

    Jingyuan Sha, Hikaru Shindo, Kristian Kersting, and Devendra Singh Dhami. 2024. Neuro-Symbolic Predicate Invention: Learning Relational Concepts from Visual Scenes.Neurosymbolic Artificial Intelligence(2024), 1–26

    2024

  25. [32]

    Hikaru Shindo, Viktor Pfanschilling, Devendra Singh Dhami, and Kristian Kersting. 2023. 𝛼 ILP: Thinking Visual Scenes as Differentiable Logic Programs.Machine Learning112, 5 (2023), 1465–1497

    2023

  26. [33]

    Hikaru Shindo, Viktor Pfanschilling, Devendra Singh Dhami, and Kristian Kersting. 2023. Learning Differentiable Logic Programs for Abstract Visual Reasoning.arXiv:2307.00928(2023)

    2023 arXiv

  27. [34]

    Arseny Skryagin, Daniel Ochs, Devendra Singh Dhami, and Kristian Kersting. 2023. Scalable Neural-Probabilistic Answer Set Programming.JAIR78 (2023), 579–617

    2023

  28. [35]

    Gido M Van de Ven, Tinne Tuytelaars, and Andreas S Tolias. 2022. Three Types of Incremental Learning.Nature Machine Intelligence4, 12 (2022), 1185–1197

    2022

  29. [36]

    Po-Wei Wang, Priya Donti, Bryan Wilder, and Zico Kolter. 2019. SATNet: Bridging Deep Learning and Logical Reasoning Using a Differentiable Satisfiability Solverr. InICML. PMLR

    2019

  30. [38]

    Hao Wu, Jiayuan Mao, Yufeng Zhang, Yuning Jiang, Lei Li, Weiwei Sun, and Wei-Ying Ma. 2019. Unified Visual-Semantic Embeddings: Bridging Vision and Language With Structured Meaning Representations. InCVPR

    2019

  31. [39]

    Wen-Chi Yang, Giuseppe Marra, Gavin Rens, and Luc De Raedt. 2023. Safe Reinforcement Learning via Probabilistic Logic Shields. InIJCAI

    2023

  32. [40]

    Zhun Yang, Adam Ishay, and Joohyung Lee. 2020. NeurASP: Embracing Neural Networks into Answer Set Programming. InIJCAI. Author Information Jiayuan Mao is a PhD student at the Massachusetts Institute of Technology, Cambridge, Mas- sachusetts, USA. Joshua B. Tenenbaum is a profe...

    2020

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