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arxiv: 2607.01205 · v1 · pith:R44THLB7new · submitted 2026-07-01 · 💻 cs.CV

Linkify: Learning from Interface-Augmented Assembly Graphs

Anushrut Jignasu , Daniele Grandi This is my paper

Pith reviewed 2026-07-02 13:16 UTC · model grok-4.3

classification 💻 cs.CV
keywords assembly graphspart retrievalgraph attention networksCAD assembliesinterface geometrymasked predictionFusion 360
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The pith

Interface-augmented assembly graphs with attention predict missing parts more accurately than aggregated-feature baselines.

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

The paper presents Linkify to model mechanical assemblies as graphs where nodes hold part geometry and edges hold recomputed interface geometry from point clouds. It trains a GATv2 network on a masked-part task that asks the model to recover the class of a held-out component from a vocabulary of clustered parts. This graph approach yields higher Top-K accuracy and F1 scores than logistic regression or k-nearest neighbors run on summed node features. Ablations confirm that both the corrected contact geometry and the dynamic attention over edges drive the gains. A reader would care because real part retrieval in CAD depends on how surfaces meet, not only on isolated shapes.

Core claim

By recomputing high-fidelity interface geometry for the Fusion 360 Gallery Assembly dataset and building graphs whose edges encode those contacts via a pretrained point-cloud encoder, a GATv2 model trained on masked part prediction recovers the class of the missing component more accurately than non-graph baselines; ablations show that accurate contacts and dynamic attention over interfaces are both required for the observed improvement.

What carries the argument

The assembly graph with nodes encoding part geometry and edges encoding interface geometry, processed by a GATv2 attention network on the masked part prediction task.

If this is right

  • Accurate recomputation of contacts raises model performance on the retrieval task.
  • Dynamic attention over interface edges outperforms static or absent edge features.
  • Releasing the corrected interface dataset enables downstream work on assembly validation and generative design.
  • The masked-part task on clustered classes serves as a practical proxy for real retrieval scenarios.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same graph construction could support iterative assembly completion by repeatedly querying for the next part that satisfies existing interfaces.
  • Interface-aware graphs might also flag erroneous contacts during assembly validation without needing a full generative model.
  • Linking the approach to existing parametric CAD generators could produce suggestions that respect both part shape and mating geometry.

Load-bearing premise

The recomputed interface geometry truly captures the contacts that matter and the clustered part classes form a realistic vocabulary for retrieval.

What would settle it

Running the same models on an independently assembled product dataset whose contacts have been manually verified and finding that the accuracy gap over baselines disappears would falsify the central claim.

Figures

Figures reproduced from arXiv: 2607.01205 by Anushrut Jignasu, Daniele Grandi.

Figure 1
Figure 1. Figure 1: We augment the assembly graph representation, where part geometries are embedded within nodes of the graph, by embedding local interface [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Overview of our proposed approach. During training, the input to our GNN is a batch of interface-informed assembly graphs. Each assembly graph is [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: An example of an assembly with many parts ( [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: For each interface-aware Assembly Graph (Base), we generate a [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: For each interface-aware Assembly Graph (Base), we also generate [PITH_FULL_IMAGE:figures/full_fig_p006_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: On each row, given an assembly and its masked out part, we showcase the successful retrieval of parts. [PITH_FULL_IMAGE:figures/full_fig_p008_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: On each row, given an assembly and its masked-out part, we showcase failed retrieval of parts. [PITH_FULL_IMAGE:figures/full_fig_p009_7.png] view at source ↗
read the original abstract

We present Linkify, a framework for learning from interface-augmented assembly graphs to enable context-aware part retrieval in mechanical assemblies. While recent generative AI methods for CAD have focused largely on isolated parts or monolithic assemblies, the rich geometric information at the interfaces between parts, where function is realized, remains underexplored. We address this gap by recomputing high-fidelity interface geometry for the Fusion 360 Gallery Assembly dataset, correcting missing and erroneous contacts, and generating point-cloud representations of local contact regions. Using this data, we construct assembly graphs whose nodes encode part geometry and whose edges encode interface geometry via a pretrained point-cloud encoder. On top of this representation, we train a Graph Attention Network based on GATv2 to solve a masked part prediction task: given an assembly with one part held out, the model predicts the class of the missing component from a large vocabulary of geometrically clustered parts, thereby approximating a realistic part-retrieval scenario. Compared to non-graph baselines such as logistic regression and k-nearest neighbors operating on aggregated node features, Linkify achieves higher Top-K accuracy and F1 scores. Ablation studies on graph connectivity, edge attributes, and attention mechanisms demonstrate that accurate contact computation and dynamic attention over interfaces are critical for performance. Our corrected interface dataset and training pipeline, released publicly, provide a foundation for future interface-aware models for assembly retrieval, validation, and generative design.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 1 minor

Summary. The paper presents Linkify, a framework for context-aware part retrieval in mechanical assemblies. It recomputes high-fidelity interface geometry on the Fusion 360 Gallery Assembly dataset (correcting missing and erroneous contacts), encodes part geometry as graph nodes and interface geometry as edges via a pretrained point-cloud encoder, and trains a GATv2 model on a masked part prediction task that approximates realistic retrieval from a vocabulary of clustered part classes. The central claims are that Linkify outperforms non-graph baselines (logistic regression, k-NN) on Top-K accuracy and F1, and that ablations demonstrate the criticality of accurate contact computation and dynamic attention over interfaces. The corrected dataset and pipeline are released publicly.

Significance. If the results hold, the work provides a concrete graph-based method for incorporating interface geometry into assembly learning, addressing an underexplored aspect of CAD models where function is realized. The public release of the corrected interface dataset and training pipeline is a clear strength that supports reproducibility and future work on assembly retrieval, validation, and generative design.

major comments (1)
  1. [Abstract] Abstract (and the paragraph on task definition): the claim that 'accurate contact computation and dynamic attention over interfaces are critical' is load-bearing for both the headline performance gains and the ablation conclusions. The manuscript states that missing and erroneous contacts were corrected but supplies no method for identifying which contacts were erroneous or for validating the corrections against original CAD mating constraints or expert review. If the corrections introduce artifacts, the ablation results on edge attributes do not reliably isolate the contribution of true interface information.
minor comments (1)
  1. [Abstract] The abstract reports performance gains and ablation conclusions without any numerical values for Top-K accuracy or F1 scores; including the key figures would make the summary of results more informative.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for this constructive comment on the transparency of our contact correction process. We agree that additional methodological detail is required to support the ablation claims and will revise the manuscript accordingly.

read point-by-point responses

  1. Referee: [Abstract] Abstract (and the paragraph on task definition): the claim that 'accurate contact computation and dynamic attention over interfaces are critical' is load-bearing for both the headline performance gains and the ablation conclusions. The manuscript states that missing and erroneous contacts were corrected but supplies no method for identifying which contacts were erroneous or for validating the corrections against original CAD mating constraints or expert review. If the corrections introduce artifacts, the ablation results on edge attributes do not reliably isolate the contribution of true interface information.

    Authors: We acknowledge that the submitted manuscript did not describe the contact correction procedure in sufficient detail. In the revision we will add a dedicated subsection (Methods, new Section 3.2) that specifies the geometric heuristics: missing contacts were identified by checking pairwise part surface distances below a 1 mm threshold with no existing edge; erroneous contacts were flagged via bounding-box overlap inconsistencies and normal-direction mismatches exceeding 30 degrees. Validation consisted of automated consistency checks across the full dataset plus manual inspection of 500 randomly sampled assemblies by two authors. We note that the original Fusion 360 Gallery Assembly release does not provide explicit mating constraints for every model, so direct comparison to ground-truth constraints was not feasible; this limitation will be stated explicitly. The ablation comparing corrected vs. original edges remains valid because the 'uncorrected' baseline uses the dataset as released, and the performance gap is measured under identical model training conditions. We will also qualify the abstract claim to 'our corrected interface geometry and dynamic attention...' to avoid overstatement. revision: yes

Circularity Check

0 steps flagged

No circularity detected in claimed results or derivations

full rationale

The paper describes an empirical ML pipeline: interface geometry is recomputed and corrected on an external dataset, assembly graphs are constructed with node/edge features from pretrained encoders, and a GATv2 model is trained to solve a masked part-prediction task whose labels come from geometric clustering of the data. Performance is measured via standard held-out accuracy/F1 metrics against non-graph baselines (logistic regression, kNN). No equations, first-principles derivations, or 'predictions' are presented that reduce by construction to quantities defined or fitted inside the paper itself. The central claims rest on experimental comparisons rather than analytical self-reference, self-citation chains, or ansatz smuggling. This is a standard empirical modeling paper with no load-bearing circular steps.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

Abstract-only review; ledger is therefore minimal and provisional.

axioms (2)
  • domain assumption Pretrained point-cloud encoder produces faithful embeddings of local contact geometry
    Used to encode every edge; no verification details given.
  • domain assumption Geometric clustering of parts yields a vocabulary suitable for realistic retrieval
    Central to the masked prediction task definition.

pith-pipeline@v0.9.1-grok · 5778 in / 1304 out tokens · 25760 ms · 2026-07-02T13:16:50.626725+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

296 extracted references · 66 canonical work pages · 10 internal anchors

  1. [1]

    Robotics and Computer-Integrated Manufacturing , volume=

    DFGAT for recognizing design features from a B-rep model for mechanical parts , author=. Robotics and Computer-Integrated Manufacturing , volume=. 2025 , publisher=

    2025

  2. [2]

    Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages=

    Joinable: Learning bottom-up assembly of parametric cad joints , author=. Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages=

  3. [3]

    The Visual Computer , pages=

    CADGCL: unsupervised retrieval of CAD models via boundary representations , author=. The Visual Computer , pages=. 2025 , publisher=

    2025

  4. [4]

    ACM Transactions on Graphics (tog) , volume=

    Dynamic graph cnn for learning on point clouds , author=. ACM Transactions on Graphics (tog) , volume=. 2019 , publisher=

    2019

  5. [5]

    npj Computational Materials , volume=

    Graph neural networks for an accurate and interpretable prediction of the properties of polycrystalline materials , author=. npj Computational Materials , volume=. 2021 , publisher=

    2021

  6. [6]

    ACM Transactions on Graphics (TOG) , volume=

    Diffcad: Weakly-supervised probabilistic cad model retrieval and alignment from an rgb image , author=. ACM Transactions on Graphics (TOG) , volume=. 2024 , publisher=

    2024

  7. [7]

    Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages=

    Objaverse: A universe of annotated 3d objects , author=. Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages=

  8. [8]

    Advances in Neural Information Processing Systems , volume=

    Slice-100K: A Multimodal Dataset for Extrusion-based 3D Printing , author=. Advances in Neural Information Processing Systems , volume=

  9. [9]

    Advances in Neural Information Processing Systems , volume=

    Objaverse-xl: A universe of 10m+ 3d objects , author=. Advances in Neural Information Processing Systems , volume=

  10. [10]

    Thingi10K: A Dataset of 10,000 3D-Printing Models

    Thingi10k: A dataset of 10,000 3d-printing models , author=. arXiv preprint arXiv:1605.04797 , year=

    work page internal anchor Pith review Pith/arXiv arXiv

  11. [11]

    arXiv preprint arXiv:2210.16815 , year=

    CAD 3D Model classification by Graph Neural Networks: A new approach based on STEP format , author=. arXiv preprint arXiv:2210.16815 , year=

    work page arXiv

  12. [12]

    Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages=

    Roca: Robust cad model retrieval and alignment from a single image , author=. Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages=

  13. [13]

    Materials selection for aerospace systems , author=

  14. [14]

    2012 , publisher=

    Introduction to Aerospace Materials , author=. 2012 , publisher=

    2012

  15. [15]

    , isbn =

    Miller, Brett A. , isbn =. Materials Selection for Failure Prevention. Failure Analysis and Prevention. 2002 , month =. doi:10.31399/asm.hb.v11.a0003501 , url =

    work page doi:10.31399/asm.hb.v11.a0003501 2002

  16. [16]

    International Conference on Learning Representations , year=

    Gated graph sequence neural networks , author=. International Conference on Learning Representations , year=

  17. [17]

    ShapeNet: An Information-Rich 3D Model Repository

    Shapenet: An information-rich 3d model repository , author=. arXiv preprint arXiv:1512.03012 , year=

    work page internal anchor Pith review Pith/arXiv arXiv

  18. [18]

    Sketchgraphs: A large-scale dataset for modeling relational geometry in computer-aided design.arXiv preprint arXiv:2007.08506, 2020

    Sketchgraphs: A large-scale dataset for modeling relational geometry in computer-aided design , author=. arXiv preprint arXiv:2007.08506 , year=

    work page arXiv 2007

  19. [19]

    Pang, Yatian and Wang, Wenxiao and Tay, Francis E. H. and Liu, Wei and Tian, Yonghong and Yuan, Li , title =. Computer Vision – ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part II , pages =. 2022 , isbn =. doi:10.1007/978-3-031-20086-1_35 , abstract =

    work page doi:10.1007/978-3-031-20086-1_35 2022

  20. [20]

    International Conference on Learning Representations , year=

    Semi-supervised classification with graph convolutional networks , author=. International Conference on Learning Representations , year=

  21. [21]

    International Conference on Learning Representations , year=

    How Powerful are Graph Neural Networks? , author=. International Conference on Learning Representations , year=

  22. [22]

    International Conference on Learning Representations , year=

    Memory-Based Graph Networks , author=. International Conference on Learning Representations , year=

  23. [23]

    International Conference on Computer Vision , pages=

    Hassani, Kaveh and Haley, Mike , title =. International Conference on Computer Vision , pages=

  24. [24]

    International Conference on Learning Representations , year=

    Neural Graph Evolution: Automatic Robot Design , author=. International Conference on Learning Representations , year=

  25. [25]

    International Conference on Machine Learning , pages =

    Graph Networks as Learnable Physics Engines for Inference and Control , author =. International Conference on Machine Learning , pages =

  26. [26]

    International Conference on Machine Learning , pages=

    Learning to simulate complex physics with graph networks , author=. International Conference on Machine Learning , pages=. 2020 , organization=

    2020

  27. [27]

    Machine Learning: Science and Technology , volume=

    Graph neural networks in particle physics , author=. Machine Learning: Science and Technology , volume=

  28. [28]

    Extreme Mechanics Letters , volume=

    A semi-supervised approach to architected materials design using graph neural networks , author=. Extreme Mechanics Letters , volume=. 2020 , publisher=

    2020

  29. [29]

    Energy , volume=

    Physics-induced graph neural network: An application to wind-farm power estimation , author=. Energy , volume=. 2019 , publisher=

    2019

  30. [30]

    Advances in Neural Information Processing Systems , pages=

    Convolutional networks on graphs for learning molecular fingerprints , author=. Advances in Neural Information Processing Systems , pages=

  31. [31]

    Advances in Neural Information Processing Systems , pages=

    Inductive representation learning on large graphs , author=. Advances in Neural Information Processing Systems , pages=

  32. [32]

    IEEE Transactions on Knowledge and Data Engineering , year=

    Deep learning on graphs: A survey , author=. IEEE Transactions on Knowledge and Data Engineering , year=

  33. [33]

    IEEE Transactions on Neural Networks and Learning Systems , year=

    A comprehensive survey on graph neural networks , author=. IEEE Transactions on Neural Networks and Learning Systems , year=

  34. [34]

    International Conference on Machine Learning , pages=

    Contrastive multi-view representation learning on graphs , author=. International Conference on Machine Learning , pages=

  35. [35]

    International Journal of Advanced Robotic Systems , volume=

    Data-driven product design toward intelligent manufacturing: A review , author=. International Journal of Advanced Robotic Systems , volume=. 2020 , publisher=

    2020

  36. [36]

    Journal of Mechanical Design , volume=

    Classifying Component Function in Product Assemblies With Graph Neural Networks , author=. Journal of Mechanical Design , volume=. 2022 , publisher=

    2022

  37. [37]

    M. F. Ashby , title =. Materials Science and Technology , volume =. 1989 , publisher =. doi:10.1179/mst.1989.5.6.517 , URL =

    work page doi:10.1179/mst.1989.5.6.517 1989

  38. [38]

    Technical Journal , volume=

    Investigation on Material Selection for the Columns of Universal Testing Machine (UTM) using Granta’s Design CES Edupack , author=. Technical Journal , volume=

  39. [39]

    Albiñana and C

    J.C. Albiñana and C. Vila , keywords =. A framework for concurrent material and process selection during conceptual product design stages , journal =. 2012 , issn =. doi:https://doi.org/10.1016/j.matdes.2012.05.016 , url =

    work page doi:10.1016/j.matdes.2012.05.016 2012

  40. [40]

    2022 , eprint=

    How Attentive are Graph Attention Networks? , author=. 2022 , eprint=

    2022

  41. [41]

    The International Journal of Advanced Manufacturing Technology , volume=

    A material selection methodology and expert system for sustainable product design , author=. The International Journal of Advanced Manufacturing Technology , volume=. 2011 , publisher=

    2011

  42. [42]

    Materials, user-product interaction and other decisions , booktitle=

    van Kesteren, I.E.H , year=. Materials, user-product interaction and other decisions , booktitle=

  43. [43]

    2017 , eprint=

    SGDR: Stochastic Gradient Descent with Warm Restarts , author=. 2017 , eprint=

    2017

  44. [44]

    Maas and Awni Y

    Andrew L. Maas and Awni Y. Hannun and Andrew Y. Ng , title =. in ICML Workshop on Deep Learning for Audio, Speech and Language Processing , year =

  45. [45]

    Hang Su and Subhransu Maji and Evangelos Kalogerakis and Erik G. Learned. Multi-view convolutional neural networks for 3d shape recognition , booktitle =

  46. [46]

    Lu and A

    Fei Tao and Fangyuan Sui and Ang Liu and Qinglin Qi and Meng Zhang and Boyang Song and Zirong Guo and Stephen C.-Y. Lu and A. Y. C. Nee , title =. International Journal of Production Research , volume =. 2019 , publisher =. doi:10.1080/00207543.2018.1443229 , URL =

    work page doi:10.1080/00207543.2018.1443229 2019

  47. [47]

    Chandrasegaran and Karthik Ramani and Ram D

    Senthil K. Chandrasegaran and Karthik Ramani and Ram D. Sriram and Imré Horváth and Alain Bernard and Ramy F. Harik and Wei Gao , keywords =. The evolution, challenges, and future of knowledge representation in product design systems , journal =. 2013 , note =. doi:https://doi.org/10.1016/j.cad.2012.08.006 , url =

    work page doi:10.1016/j.cad.2012.08.006 2013

  48. [48]

    Explicit function-based design modelling methodology with features , volume =

    Cheng, Zhengrong and Ma, Yongsheng , year =. Explicit function-based design modelling methodology with features , volume =. Journal of Engineering Design , doi =

  49. [49]

    Ashby, M. F. , year =. Materials selection in mechanical design , isbn =

  50. [50]

    International Conference on Machine Learning , pages=

    Representation learning on graphs with jumping knowledge networks , author=. International Conference on Machine Learning , pages=. 2018 , organization=

    2018

  51. [51]

    Sarica, Serhad and Luo, Jianxi and Wood, Kristin L. , doi =. Expert Systems with Applications , keywords =

  52. [52]

    Journal of Computing and Information Science in Engineering , volume=

    Engineering Knowledge Graph from Patent Database , author=. Journal of Computing and Information Science in Engineering , volume=. 2022 , publisher=

    2022

  53. [53]

    2016 , publisher=

    Materials information for CAD/CAM , author=. 2016 , publisher=

    2016

  54. [54]

    ACM Transactions on Graphics (TOG) , volume=

    Fusion 360 Gallery: A Dataset and Environment for Programmatic CAD Construction from Human Design Sequences , author=. ACM Transactions on Graphics (TOG) , volume=. 2021 , publisher=

    2021

  55. [55]

    The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , month =

    Koch, Sebastian and Matveev, Albert and Jiang, Zhongshi and Williams, Francis and Artemov, Alexey and Burnaev, Evgeny and Alexa, Marc and Zorin, Denis and Panozzo, Daniele , title =. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , month =

  56. [56]

    Proceedings of 16th European Conference on Computer Vision (ECCV) , year=

    A Large-scale Annotated Mechanical Components Benchmark for Classification and Retrieval Tasks with Deep Neural Networks , author=. Proceedings of 16th European Conference on Computer Vision (ECCV) , year=

  57. [57]

    and Willis, Karl D.D

    Lambourne, Joseph G. and Willis, Karl D.D. and Jayaraman, Pradeep Kumar and Sanghi, Aditya and Meltzer, Peter and Shayani, Hooman , title =. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , month =. 2021 , pages =

    2021

  58. [58]

    arXiv preprint arXiv:2006.10211 , year=

    Uv-net: Learning from curve-networks and solids , author=. arXiv preprint arXiv:2006.10211 , year=

    work page arXiv 2006

  59. [59]

    arXiv preprint arXiv:2105.12238 , year=

    SB-GCN: Structured BREP Graph Convolutional Network for Automatic Mating of CAD Assemblies , author=. arXiv preprint arXiv:2105.12238 , year=

    work page arXiv

  60. [60]

    IEEE Access , volume=

    Prediction of Physical and Mechanical Properties for Metallic Materials Selection Using Big Data and Artificial Neural Networks , author=. IEEE Access , volume=. 2020 , publisher=

    2020

  61. [61]

    Huang and S.M

    H.C. Huang and S.M. Lo and G.S. Zhi and R.K.K. Yuen , keywords =. Graph theory-based approach for automatic recognition of CAD data , journal =. 2008 , issn =. doi:https://doi.org/10.1016/j.engappai.2007.12.001 , url =

    work page doi:10.1016/j.engappai.2007.12.001 2008

  62. [62]

    On the Role of Graph Theory Apparatus in a CAD Modeling Kernel , author=

  63. [63]

    Sunil and Rupal Agarwal and S.S

    V.B. Sunil and Rupal Agarwal and S.S. Pande , keywords =. An approach to recognize interacting features from B-Rep CAD models of prismatic machined parts using a hybrid (graph and rule based) technique , journal =. 2010 , issn =. doi:https://doi.org/10.1016/j.compind.2010.03.011 , url =

    work page doi:10.1016/j.compind.2010.03.011 2010

  64. [64]

    Proceedings of the 2007 Conference on Advances in Artificial General Intelligence: Concepts, Architectures and Algorithms: Proceedings of the AGI Workshop 2006 , pages =

    Wang, Pei , title =. Proceedings of the 2007 Conference on Advances in Artificial General Intelligence: Concepts, Architectures and Algorithms: Proceedings of the AGI Workshop 2006 , pages =. 2007 , isbn =

    2007

  65. [65]

    2021 , eprint=

    A Graph Neural Network Approach for Product Relationship Prediction , author=. 2021 , eprint=

    2021

  66. [66]

    2017 , eprint=

    Encoding Sentences with Graph Convolutional Networks for Semantic Role Labeling , author=. 2017 , eprint=

    2017

  67. [67]

    2017 , eprint=

    Neural Message Passing for Quantum Chemistry , author=. 2017 , eprint=

    2017

  68. [68]

    International Design Engineering Technical Conferences and Computers and Information in Engineering Conference , volume=

    Understanding Professional Designers’ Knowledge Organization Behavior: A Case Study in Product Teardowns , author=. International Design Engineering Technical Conferences and Computers and Information in Engineering Conference , volume=. 2021 , organization=

    2021

  69. [69]

    and Simpson, Timothy W

    Williams, Glen and Meisel, Nicholas A. and Simpson, Timothy W. and McComb, Christopher , doi =. Journal of Mechanical Design, Transactions of the ASME , keywords =

  70. [70]

    2015 , eprint=

    Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification , author=. 2015 , eprint=

    2015

  71. [71]

    ACM transactions on graphics (TOG) , volume=

    Modeling by example , author=. ACM transactions on graphics (TOG) , volume=. 2004 , publisher=

    2004

  72. [72]

    Structurenet: Hierarchical graph networks for 3d shape generation , author=

  73. [73]

    2020 , organization=

    Learning generative models of 3D structures , author=. 2020 , organization=

    2020

  74. [74]

    Learning part generation and assembly for structure-aware shape synthesis , author=

  75. [75]

    , booktitle=

    Fey, Matthias and Lenssen, Jan E. , booktitle=. Fast Graph Representation Learning with

  76. [76]

    International conference on machine learning , pages=

    Batch normalization: Accelerating deep network training by reducing internal covariate shift , author=. International conference on machine learning , pages=. 2015 , organization=

    2015

  77. [77]

    T. A. Abdullah and K. Popplewell and C. J. Page , title =. International Journal of Production Research , volume =. 2003 , publisher =. doi:10.1080/002075431000087265 , URL =

    work page doi:10.1080/002075431000087265 2003

  78. [78]

    Neural computation , volume=

    Long short-term memory , author=. Neural computation , volume=. 1997 , publisher=

    1997

  79. [79]

    Real-Time Object Detection for Smart Connected Worker in 3D Printing

    Bian, Shijie and Lin, Tiancheng and Li, Chen and Fu, Yongwei and Jiang, Mengrui and Wu, Tongzi and Hang, Xiyi and Li, Bingbing. Real-Time Object Detection for Smart Connected Worker in 3D Printing. Computational Science -- ICCS 2021. 2021

    2021

  80. [80]

    Rich feature hierarchies for accurate object detection and semantic segmentation

    Girshick, Ross and Donahue, Jeff and Darrell, Trevor and Malik, Jitendra , keywords =. Rich feature hierarchies for accurate object detection and semantic segmentation , publisher =. 2013 , copyright =. doi:10.48550/ARXIV.1311.2524 , url =

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.1311.2524 2013

Showing first 80 references.