RETENTION: Resource-Efficient Tree-Based Ensemble Model Acceleration with Content-Addressable Memory | Paper Citation Record

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This paper cites Tabular data: Deep learning is not all you need,.

RETENTION: Resource-Efficient Tree-Based Ensemble Model Acceleration with Content-Addressable Memory Tabular data: Deep learning is not all you need,

Reference 1

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This paper cites Why do tree-based models still outperform deep learning on typical tabular data?.

RETENTION: Resource-Efficient Tree-Based Ensemble Model Acceleration with Content-Addressable Memory Why do tree-based models still outperform deep learning on typical tabular data?

Reference 2

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This paper cites From local explanations to global understanding with explainable ai for trees,.

RETENTION: Resource-Efficient Tree-Based Ensemble Model Acceleration with Content-Addressable Memory From local explanations to global understanding with explainable ai for trees,

Reference 3

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This paper cites Explainable artificial intelligence (xai),.

RETENTION: Resource-Efficient Tree-Based Ensemble Model Acceleration with Content-Addressable Memory Explainable artificial intelligence (xai),

Reference 4

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This paper cites Large language models for scientific discovery in molecular property prediction,.

RETENTION: Resource-Efficient Tree-Based Ensemble Model Acceleration with Content-Addressable Memory Large language models for scientific discovery in molecular property prediction,

Reference 5

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This paper cites A supervised machine learning algorithm for detecting and predicting fraud in credit card transactions,.

RETENTION: Resource-Efficient Tree-Based Ensemble Model Acceleration with Content-Addressable Memory A supervised machine learning algorithm for detecting and predicting fraud in credit card transactions,

Reference 6

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This paper cites Application of xgboost algorithm in bearing fault diagnosis,.

RETENTION: Resource-Efficient Tree-Based Ensemble Model Acceleration with Content-Addressable Memory Application of xgboost algorithm in bearing fault diagnosis,

Reference 7

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This paper cites Kaggle machine learning & data science survey 2021,.

RETENTION: Resource-Efficient Tree-Based Ensemble Model Acceleration with Content-Addressable Memory Kaggle machine learning & data science survey 2021,

Reference 8

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This paper cites Xgboost: A scalable tree boosting system,.

RETENTION: Resource-Efficient Tree-Based Ensemble Model Acceleration with Content-Addressable Memory Xgboost: A scalable tree boosting system,

Reference 9

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Observation 8661aa87-9eb5-43be-a8b8-f00c77f4f6d1 · outbound

This paper cites Dynamic decision tree ensembles for energy-efficient inference on iot edge nodes,.

RETENTION: Resource-Efficient Tree-Based Ensemble Model Acceleration with Content-Addressable Memory Dynamic decision tree ensembles for energy-efficient inference on iot edge nodes,

Reference 10

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This paper cites Tahoe: tree structure-aware high performance inference engine for decision tree ensemble on gpu,.

RETENTION: Resource-Efficient Tree-Based Ensemble Model Acceleration with Content-Addressable Memory Tahoe: tree structure-aware high performance inference engine for decision tree ensemble on gpu,

Reference 11

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This paper cites Fpga accelerator for gradient boosting decision trees,.

RETENTION: Resource-Efficient Tree-Based Ensemble Model Acceleration with Content-Addressable Memory Fpga accelerator for gradient boosting decision trees,

Reference 12

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This paper cites Accelerating a random forest classifier: Multi-core, gp-gpu, or fpga?.

RETENTION: Resource-Efficient Tree-Based Ensemble Model Acceleration with Content-Addressable Memory Accelerating a random forest classifier: Multi-core, gp-gpu, or fpga?

Reference 13

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This paper cites Content-addressable memory (cam) circuits and architectures: a tutorial and survey,.

RETENTION: Resource-Efficient Tree-Based Ensemble Model Acceleration with Content-Addressable Memory Content-addressable memory (cam) circuits and architectures: a tutorial and survey,

Reference 14

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This paper cites Isaac: a convolutional neural network accelerator with in-situ analog arithmetic in crossbars,.

RETENTION: Resource-Efficient Tree-Based Ensemble Model Acceleration with Content-Addressable Memory Isaac: a convolutional neural network accelerator with in-situ analog arithmetic in crossbars,

Reference 15

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This paper cites Prime: A novel processing-in-memory architecture for neural network computation in reram-based main memory,.

RETENTION: Resource-Efficient Tree-Based Ensemble Model Acceleration with Content-Addressable Memory Prime: A novel processing-in-memory architecture for neural network computation in reram-based main memory,

Reference 16

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This paper cites An overview of computing-in-memory circuits with dram and nvm,.

RETENTION: Resource-Efficient Tree-Based Ensemble Model Acceleration with Content-Addressable Memory An overview of computing-in-memory circuits with dram and nvm,

Reference 17

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This paper cites Rram-based tcams for pattern search,.

RETENTION: Resource-Efficient Tree-Based Ensemble Model Acceleration with Content-Addressable Memory Rram-based tcams for pattern search,

Reference 18

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Observation ce6a4683-e1e1-4bae-8672-9d27b60f6f63 · outbound

This paper cites The trend of emerging non-volatile tcam for parallel search and ai applications,.

RETENTION: Resource-Efficient Tree-Based Ensemble Model Acceleration with Content-Addressable Memory The trend of emerging non-volatile tcam for parallel search and ai applications,

Reference 19

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This paper cites 17.5 a 3t1r nonvolatile tcam using mlc reram with sub-1ns search time,.

RETENTION: Resource-Efficient Tree-Based Ensemble Model Acceleration with Content-Addressable Memory 17.5 a 3t1r nonvolatile tcam using mlc reram with sub-1ns search time,

Reference 20

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This paper cites API design for machine learning software: experiences from the scikit-learn project,.

RETENTION: Resource-Efficient Tree-Based Ensemble Model Acceleration with Content-Addressable Memory API design for machine learning software: experiences from the scikit-learn project,

Reference 21

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RETENTION: Resource-Efficient Tree-Based Ensemble Model Acceleration with Content-Addressable Memory Random forests,

Reference 22

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This paper cites Dt2cam: A decision tree to content addressable memory framework,.

RETENTION: Resource-Efficient Tree-Based Ensemble Model Acceleration with Content-Addressable Memory Dt2cam: A decision tree to content addressable memory framework,

Reference 23

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This paper cites Tree-based machine learning performed in-memory with memristive analog cam,.

RETENTION: Resource-Efficient Tree-Based Ensemble Model Acceleration with Content-Addressable Memory Tree-based machine learning performed in-memory with memristive analog cam,

Reference 24

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RETENTION: Resource-Efficient Tree-Based Ensemble Model Acceleration with Content-Addressable Memory X-time: Accelerating large tree ensem- bles inference for tabular data with analog cams,

Reference 25

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This paper cites A digital 3d tcam accelerator for the inference phase of random forest,.

RETENTION: Resource-Efficient Tree-Based Ensemble Model Acceleration with Content-Addressable Memory A digital 3d tcam accelerator for the inference phase of random forest,

Reference 26

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This paper cites Deep random forest with ferroelectric analog content addressable memory,.

RETENTION: Resource-Efficient Tree-Based Ensemble Model Acceleration with Content-Addressable Memory Deep random forest with ferroelectric analog content addressable memory,

Reference 27

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RETENTION: Resource-Efficient Tree-Based Ensemble Model Acceleration with Content-Addressable Memory Out-of-bag estimation,

Reference 28

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RETENTION: Resource-Efficient Tree-Based Ensemble Model Acceleration with Content-Addressable Memory Lightgbm: A highly efficient gradient boosting decision tree,

Reference 29

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RETENTION: Resource-Efficient Tree-Based Ensemble Model Acceleration with Content-Addressable Memory Catboost: unbiased boosting with categorical features,

Reference 30

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This paper cites Breiman, J.

RETENTION: Resource-Efficient Tree-Based Ensemble Model Acceleration with Content-Addressable Memory Breiman, J

Reference 31

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RETENTION: Resource-Efficient Tree-Based Ensemble Model Acceleration with Content-Addressable Memory Bagging predictors,

Reference 32

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RETENTION: Resource-Efficient Tree-Based Ensemble Model Acceleration with Content-Addressable Memory A brief introduction to boosting,

Reference 33

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This paper cites Emerging trends in design and applications of memory-based computing and content-addressable memories,.

RETENTION: Resource-Efficient Tree-Based Ensemble Model Acceleration with Content-Addressable Memory Emerging trends in design and applications of memory-based computing and content-addressable memories,

Reference 34

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This paper cites Advancements in content-addressable memory (cam) circuits: State-of-the-art, applica- tions, and future directions in the ai domain,.

RETENTION: Resource-Efficient Tree-Based Ensemble Model Acceleration with Content-Addressable Memory Advancements in content-addressable memory (cam) circuits: State-of-the-art, applica- tions, and future directions in the ai domain,

Reference 35

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This paper cites A ternary content- addressable memory (tcam) based on 4t static storage and including a current-race sensing scheme,.

RETENTION: Resource-Efficient Tree-Based Ensemble Model Acceleration with Content-Addressable Memory A ternary content- addressable memory (tcam) based on 4t static storage and including a current-race sensing scheme,

Reference 36

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RETENTION: Resource-Efficient Tree-Based Ensemble Model Acceleration with Content-Addressable Memory Becker and R

Reference 37

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RETENTION: Resource-Efficient Tree-Based Ensemble Model Acceleration with Content-Addressable Memory Credit Approval,

Reference 38

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RETENTION: Resource-Efficient Tree-Based Ensemble Model Acceleration with Content-Addressable Memory Dry Bean,

Reference 39

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RETENTION: Resource-Efficient Tree-Based Ensemble Model Acceleration with Content-Addressable Memory Letter Recognition,

Reference 40

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RETENTION: Resource-Efficient Tree-Based Ensemble Model Acceleration with Content-Addressable Memory Wine quality,

Reference 41

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This paper cites Analog content-addressable memories with memristors,.

RETENTION: Resource-Efficient Tree-Based Ensemble Model Acceleration with Content-Addressable Memory Analog content-addressable memories with memristors,

Reference 42

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This paper cites Noise injection adaption: End-to-end reram crossbar non-ideal effect adaption for neural network mapping,.

RETENTION: Resource-Efficient Tree-Based Ensemble Model Acceleration with Content-Addressable Memory Noise injection adaption: End-to-end reram crossbar non-ideal effect adaption for neural network mapping,

Reference 43

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This paper cites On minimizing analog variation errors to resolve the scalability issue of reram-based crossbar accelerators,.

RETENTION: Resource-Efficient Tree-Based Ensemble Model Acceleration with Content-Addressable Memory On minimizing analog variation errors to resolve the scalability issue of reram-based crossbar accelerators,

Reference 44

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This paper cites Margin optimization based pruning for random forest,.

RETENTION: Resource-Efficient Tree-Based Ensemble Model Acceleration with Content-Addressable Memory Margin optimization based pruning for random forest,

Reference 45

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This paper cites Search for the smallest random forest,.

RETENTION: Resource-Efficient Tree-Based Ensemble Model Acceleration with Content-Addressable Memory Search for the smallest random forest,

Reference 46

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This paper cites Smart sensors: Analysis of different types of iot sensors,.

RETENTION: Resource-Efficient Tree-Based Ensemble Model Acceleration with Content-Addressable Memory Smart sensors: Analysis of different types of iot sensors,

Reference 47

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This paper cites A survey of research and practices of network-on-chip,.

RETENTION: Resource-Efficient Tree-Based Ensemble Model Acceleration with Content-Addressable Memory A survey of research and practices of network-on-chip,

Reference 48

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This paper cites The uci machine learning repository,.

RETENTION: Resource-Efficient Tree-Based Ensemble Model Acceleration with Content-Addressable Memory The uci machine learning repository,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:09:37.040308Z

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Observation dedeb78e-16da-4a87-a111-f4a54fb305cb · outbound

This paper cites ranger: A fast implementation of random forests for high dimensional data in c++ and r,.

RETENTION: Resource-Efficient Tree-Based Ensemble Model Acceleration with Content-Addressable Memory ranger: A fast implementation of random forests for high dimensional data in c++ and r,

Reference 50

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Observation b42d28dc-9c04-47e6-aab3-abccfb8c5979 · outbound

This paper cites 17.5 a 3t1r nonvolatile tcam using mlc reram with sub-1ns search time,.

RETENTION: Resource-Efficient Tree-Based Ensemble Model Acceleration with Content-Addressable Memory 17.5 a 3t1r nonvolatile tcam using mlc reram with sub-1ns search time,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:09:36.631555Z

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Observation 2cca7ba0-77f1-4277-9aa6-9cf32031a7d3 · outbound

This paper cites Available: https://doi.org/10.1145/1132952.1132953.

RETENTION: Resource-Efficient Tree-Based Ensemble Model Acceleration with Content-Addressable Memory Available: https://doi.org/10.1145/1132952.1132953

Reference 2006

Resolution: metadata mismatch; raw_fallback, observed 2026-08-07T06:09:36.257267Z

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