Efficient Mixed Precision Quantization in Graph Neural Networks | Paper Citation Record

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This paper cites Warden and D.

Efficient Mixed Precision Quantization in Graph Neural Networks Warden and D

Reference 1

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This paper cites On-device training under 256kb memory,.

Efficient Mixed Precision Quantization in Graph Neural Networks On-device training under 256kb memory,

Reference 2

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This paper cites A study of lora: Long range and low power networks for the internet of things,.

Efficient Mixed Precision Quantization in Graph Neural Networks A study of lora: Long range and low power networks for the internet of things,

Reference 3

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This paper cites Appearance vs Disappearance of broad absorption line troughs in quasars.

Efficient Mixed Precision Quantization in Graph Neural Networks Appearance vs Disappearance of broad absorption line troughs in quasars

Reference 4

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This paper cites Ll-gnn: Low-latency graph neural net- works on fpgas for high-energy physics,.

Efficient Mixed Precision Quantization in Graph Neural Networks Ll-gnn: Low-latency graph neural net- works on fpgas for high-energy physics,

Reference 5

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This paper cites Eta prediction with graph neural networks in google maps,.

Efficient Mixed Precision Quantization in Graph Neural Networks Eta prediction with graph neural networks in google maps,

Reference 6

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This paper cites Point-gnn: Graph neural net- work for 3d object detection in a point cloud,.

Efficient Mixed Precision Quantization in Graph Neural Networks Point-gnn: Graph neural net- work for 3d object detection in a point cloud,

Reference 7

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This paper cites Degree-quant: Quantization-aware training for graph neu- ral networks,.

Efficient Mixed Precision Quantization in Graph Neural Networks Degree-quant: Quantization-aware training for graph neu- ral networks,

Reference 8

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This paper cites Goodfellow, Y.

Efficient Mixed Precision Quantization in Graph Neural Networks Goodfellow, Y

Reference 9

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This paper cites Mea- suring and relieving the over-smoothing problem for graph neural networks from the topological view,.

Efficient Mixed Precision Quantization in Graph Neural Networks Mea- suring and relieving the over-smoothing problem for graph neural networks from the topological view,

Reference 10

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This paper cites Understanding over-squashing and bottlenecks on graphs via curvature,.

Efficient Mixed Precision Quantization in Graph Neural Networks Understanding over-squashing and bottlenecks on graphs via curvature,

Reference 11

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This paper cites Beyond over-smoothing: Uncovering the trainability challenges in deep graph neural networks,.

Efficient Mixed Precision Quantization in Graph Neural Networks Beyond over-smoothing: Uncovering the trainability challenges in deep graph neural networks,

Reference 12

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This paper cites Opti- mization of graph neural networks: Implicit acceleration by skip connections and more depth,.

Efficient Mixed Precision Quantization in Graph Neural Networks Opti- mization of graph neural networks: Implicit acceleration by skip connections and more depth,

Reference 13

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This paper cites an unresolved cited work.

Efficient Mixed Precision Quantization in Graph Neural Networks Unresolved cited work

Reference 14

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This paper cites Quantization of deep neural networks for accurate edge computing,.

Efficient Mixed Precision Quantization in Graph Neural Networks Quantization of deep neural networks for accurate edge computing,

Reference 15

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This paper cites Aggregation-aware quantization for graph neu- ral networks,.

Efficient Mixed Precision Quantization in Graph Neural Networks Aggregation-aware quantization for graph neu- ral networks,

Reference 16

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This paper cites Semi-supervised classification with graph convolutional networks,.

Efficient Mixed Precision Quantization in Graph Neural Networks Semi-supervised classification with graph convolutional networks,

Reference 17

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This paper cites Graph attention networks,.

Efficient Mixed Precision Quantization in Graph Neural Networks Graph attention networks,

Reference 18

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Observation 979d41fa-3417-4251-942a-4f36e6960a17 · outbound

This paper cites How powerful are graph neural networks?,.

Efficient Mixed Precision Quantization in Graph Neural Networks How powerful are graph neural networks?,

Reference 19

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This paper cites Masked label prediction: Unified message passing model for semi-supervised classification,.

Efficient Mixed Precision Quantization in Graph Neural Networks Masked label prediction: Unified message passing model for semi-supervised classification,

Reference 20

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This paper cites Topology Adaptive Graph Convolutional Networks.

Efficient Mixed Precision Quantization in Graph Neural Networks Topology Adaptive Graph Convolutional Networks

Reference 21

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This paper cites How to find your friendly neighbor- hood: Graph attention design with self-supervision,.

Efficient Mixed Precision Quantization in Graph Neural Networks How to find your friendly neighbor- hood: Graph attention design with self-supervision,

Reference 22

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This paper cites NVIDIA Hopper Architec- ture In-Depth,.

Efficient Mixed Precision Quantization in Graph Neural Networks NVIDIA Hopper Architec- ture In-Depth,

Reference 23

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This paper cites Ladder: Enabling e fficient low-precision deep learning computing through hardware-aware tensor transformation,.

Efficient Mixed Precision Quantization in Graph Neural Networks Ladder: Enabling e fficient low-precision deep learning computing through hardware-aware tensor transformation,

Reference 24

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This paper cites NVIDIA Blackwell Architecture Technical Brief,.

Efficient Mixed Precision Quantization in Graph Neural Networks NVIDIA Blackwell Architecture Technical Brief,

Reference 25

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This paper cites Extension of accu- rate numerical algorithms for matrix multiplication based on error-free transformation,.

Efficient Mixed Precision Quantization in Graph Neural Networks Extension of accu- rate numerical algorithms for matrix multiplication based on error-free transformation,

Reference 26

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This paper cites Neural message passing for quantum chem- istry,.

Efficient Mixed Precision Quantization in Graph Neural Networks Neural message passing for quantum chem- istry,

Reference 27

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This paper cites Inductive representation learning on large graphs,.

Efficient Mixed Precision Quantization in Graph Neural Networks Inductive representation learning on large graphs,

Reference 28

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This paper cites Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation.

Efficient Mixed Precision Quantization in Graph Neural Networks Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation

Reference 29

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This paper cites Quantization and train- ing of neural networks for efficient integer-arithmetic-only inference,.

Efficient Mixed Precision Quantization in Graph Neural Networks Quantization and train- ing of neural networks for efficient integer-arithmetic-only inference,

Reference 30

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This paper cites Vertex-centric visual programming for graph neural networks,.

Efficient Mixed Precision Quantization in Graph Neural Networks Vertex-centric visual programming for graph neural networks,

Reference 31

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This paper cites Regraphx: NoC-enabled 3d heteroge- neous ReRAM architecture for training graph neural net- works,.

Efficient Mixed Precision Quantization in Graph Neural Networks Regraphx: NoC-enabled 3d heteroge- neous ReRAM architecture for training graph neural net- works,

Reference 32

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This paper cites Graphite: Optimizing graph neural net- works on CPUs through cooperative software-hardware techniques,.

Efficient Mixed Precision Quantization in Graph Neural Networks Graphite: Optimizing graph neural net- works on CPUs through cooperative software-hardware techniques,

Reference 33

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This paper cites Graphiler: Optimizing graph neural networks with message passing data flow graph,.

Efficient Mixed Precision Quantization in Graph Neural Networks Graphiler: Optimizing graph neural networks with message passing data flow graph,

Reference 34

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Observation 04a78ad3-f416-4d0f-8303-f497fc4dba4c · outbound

This paper cites Rubik: A hierarchical architecture for efficient graph neural network training,.

Efficient Mixed Precision Quantization in Graph Neural Networks Rubik: A hierarchical architecture for efficient graph neural network training,

Reference 35

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This paper cites A unified lottery ticket hypothesis for graph neural networks,.

Efficient Mixed Precision Quantization in Graph Neural Networks A unified lottery ticket hypothesis for graph neural networks,

Reference 36

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This paper cites Comprehensive graph gradual prun- ing for sparse training in graph neural networks,.

Efficient Mixed Precision Quantization in Graph Neural Networks Comprehensive graph gradual prun- ing for sparse training in graph neural networks,

Reference 37

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This paper cites GraphSAINT: Graph sampling based induc- tive learning method,.

Efficient Mixed Precision Quantization in Graph Neural Networks GraphSAINT: Graph sampling based induc- tive learning method,

Reference 38

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This paper cites GNNAutoScale: Scalable and expressive graph neural networks via historical embeddings,.

Efficient Mixed Precision Quantization in Graph Neural Networks GNNAutoScale: Scalable and expressive graph neural networks via historical embeddings,

Reference 39

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This paper cites Vq-gnn: A universal framework to scale up graph neural networks using vector quantization,.

Efficient Mixed Precision Quantization in Graph Neural Networks Vq-gnn: A universal framework to scale up graph neural networks using vector quantization,

Reference 40

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This paper cites Epquant: A graph neural network compression approach based on product quantization,.

Efficient Mixed Precision Quantization in Graph Neural Networks Epquant: A graph neural network compression approach based on product quantization,

Reference 41

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Observation 192b0909-e7e4-4ae4-b314-4b2b00e7c92a · outbound

This paper cites Sgquant: Squeezing the last bit on graph neural networks with specialized quantization,.

Efficient Mixed Precision Quantization in Graph Neural Networks Sgquant: Squeezing the last bit on graph neural networks with specialized quantization,

Reference 42

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This paper cites Graph neural architecture search,.

Efficient Mixed Precision Quantization in Graph Neural Networks Graph neural architecture search,

Reference 43

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This paper cites Graphnas++: Distributed architecture search for graph neural networks,.

Efficient Mixed Precision Quantization in Graph Neural Networks Graphnas++: Distributed architecture search for graph neural networks,

Reference 44

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This paper cites Distilling knowledge from graph convolutional networks,.

Efficient Mixed Precision Quantization in Graph Neural Networks Distilling knowledge from graph convolutional networks,

Reference 45

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This paper cites Graph-less neural networks: Teaching old MLPs new tricks via distillation,.

Efficient Mixed Precision Quantization in Graph Neural Networks Graph-less neural networks: Teaching old MLPs new tricks via distillation,

Reference 46

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This paper cites Exponentially improving the complexity of simulating the weisfeiler- lehman test with graph neural networks,.

Efficient Mixed Precision Quantization in Graph Neural Networks Exponentially improving the complexity of simulating the weisfeiler- lehman test with graph neural networks,

Reference 47

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This paper cites Meta- aggregator: Learning to aggregate for 1-bit graph neural networks,.

Efficient Mixed Precision Quantization in Graph Neural Networks Meta- aggregator: Learning to aggregate for 1-bit graph neural networks,

Reference 48

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This paper cites Binarized graph neural network,.

Efficient Mixed Precision Quantization in Graph Neural Networks Binarized graph neural network,

Reference 49

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This paper cites Binary graph neural networks,.

Efficient Mixed Precision Quantization in Graph Neural Networks Binary graph neural networks,

Reference 50

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Observation 9d07c869-971b-416f-aadd-14e3e8fd6b65 · outbound

This paper cites MEGA: A memory-efficient GNN accelerator exploiting degree-aware mixed-precision quantization,.

Efficient Mixed Precision Quantization in Graph Neural Networks MEGA: A memory-efficient GNN accelerator exploiting degree-aware mixed-precision quantization,

Reference 51

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This paper cites DARTS: Differen- tiable architecture search,.

Efficient Mixed Precision Quantization in Graph Neural Networks DARTS: Differen- tiable architecture search,

Reference 52

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Observation f73e0ff6-208b-4608-b2d5-8caa69e07334 · outbound

This paper cites Rethinking di fferentiable search for mixed-precision neural networks,.

Efficient Mixed Precision Quantization in Graph Neural Networks Rethinking di fferentiable search for mixed-precision neural networks,

Reference 53

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Observation bebfb25f-a39e-4933-b46c-96996680bb5b · outbound

This paper cites One-shot model for mixed-precision quan- tization,.

Efficient Mixed Precision Quantization in Graph Neural Networks One-shot model for mixed-precision quan- tization,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:43:09.394561Z

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Observation 05f2ea4b-26c8-4f09-9d24-3b2000a25cc1 · outbound

This paper cites Sparse GPU kernels for deep learning,.

Efficient Mixed Precision Quantization in Graph Neural Networks Sparse GPU kernels for deep learning,

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:43:09.382595Z

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Observation 285b712a-2e9f-4906-bac1-ca96183e34d1 · outbound

This paper cites Efficient quantized sparse matrix operations on tensor cores,.

Efficient Mixed Precision Quantization in Graph Neural Networks Efficient quantized sparse matrix operations on tensor cores,

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:43:09.371997Z

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Observation e83c27f4-a076-451b-855e-90d977d99fb0 · outbound

This paper cites QGTC: Accelerating quantized graph neural networks via GPU tensor core,.

Efficient Mixed Precision Quantization in Graph Neural Networks QGTC: Accelerating quantized graph neural networks via GPU tensor core,

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:43:09.361330Z

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Observation 0fe50ee8-1b0f-48d8-a5aa-c8ca37d56a5c · outbound

This paper cites Pytorch 2: Faster ma- chine learning through dynamic Python bytecode transfor- mation and graph compilation,.

Efficient Mixed Precision Quantization in Graph Neural Networks Pytorch 2: Faster ma- chine learning through dynamic Python bytecode transfor- mation and graph compilation,

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:43:09.346551Z

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Observation fa9ce984-3f0e-48cb-afc6-70cb88b0c122 · outbound

This paper cites SDQ: Stochastic di fferen- tiable quantization with mixed precision,.

Efficient Mixed Precision Quantization in Graph Neural Networks SDQ: Stochastic di fferen- tiable quantization with mixed precision,

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:43:09.332574Z

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Observation cea8694f-4757-4820-bb17-347d946b1022 · outbound

This paper cites Bayesian bits: Unifying quantization and pruning,.

Efficient Mixed Precision Quantization in Graph Neural Networks Bayesian bits: Unifying quantization and pruning,

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:43:09.316846Z

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Observation bf102160-9d37-4a72-aa6c-89d45a055d29 · outbound

This paper cites Searching for low-bit weights in quantized neural networks,.

Efficient Mixed Precision Quantization in Graph Neural Networks Searching for low-bit weights in quantized neural networks,

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:43:09.304282Z

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Observation 868d856c-65d9-4029-99ac-7257df6ee7c5 · outbound

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Efficient Mixed Precision Quantization in Graph Neural Networks AMD EPYC ™ 9534 Processor,

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:43:09.294064Z

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Observation 26924b88-ac87-4a3f-8022-aff2e5e9f7ab · outbound

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Efficient Mixed Precision Quantization in Graph Neural Networks Unresolved cited work

Reference 63

Resolution: unresolved; raw_fallback, observed 2026-08-15T21:43:09.283017Z

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Observation bfe93d46-03b4-45c6-98c6-7cde1e115dd1 · outbound

This paper cites Bisong, Google Colaboratory.

Efficient Mixed Precision Quantization in Graph Neural Networks Bisong, Google Colaboratory

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:43:09.270648Z

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Observation 15c93136-3ad4-49a6-b2bd-92642ab4de95 · outbound

This paper cites Revisiting semi-supervised learning with graph embeddings,.

Efficient Mixed Precision Quantization in Graph Neural Networks Revisiting semi-supervised learning with graph embeddings,

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:43:09.259399Z

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Observation 9034e383-39a9-41c8-bda4-0c74b9975094 · outbound

This paper cites Open graph benchmark: Datasets for machine learning on graphs,.

Efficient Mixed Precision Quantization in Graph Neural Networks Open graph benchmark: Datasets for machine learning on graphs,

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:43:09.247114Z

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This paper cites Igb: Addressing the gaps in labeling, features, heterogeneity, and size of public graph datasets for deep learning research,.

Efficient Mixed Precision Quantization in Graph Neural Networks Igb: Addressing the gaps in labeling, features, heterogeneity, and size of public graph datasets for deep learning research,

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:43:09.234821Z

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Observation 8fa56c69-7cb2-4861-8956-bb54718b641d · outbound

This paper cites Rela- tional pooling for graph representations,.

Efficient Mixed Precision Quantization in Graph Neural Networks Rela- tional pooling for graph representations,

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:43:09.222783Z

Source-reported events for the cited work

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Observation 34df04a5-a9f7-43b3-a75b-883c888faa7a · outbound

This paper cites TUDataset: A collection of benchmark datasets for learning with graphs,.

Efficient Mixed Precision Quantization in Graph Neural Networks TUDataset: A collection of benchmark datasets for learning with graphs,

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:43:09.210379Z

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Observation 25419509-c1cd-437d-aadf-4a2537862664 · outbound

This paper cites Low-bit quantization for deep graph neural networks with smoothness-aware message propagation,.

Efficient Mixed Precision Quantization in Graph Neural Networks Low-bit quantization for deep graph neural networks with smoothness-aware message propagation,

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:43:09.195028Z

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Observation 489d9048-b17c-4a16-8d23-db117600388e · outbound

This paper cites Benchmarking graph neural networks,.

Efficient Mixed Precision Quantization in Graph Neural Networks Benchmarking graph neural networks,

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:43:09.182185Z

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