Paper Citation Record · LEDGER

Optimising TinyML with Quantization and Distillation of Transformer and Mamba Models for Indoor Localisation on Edge Devices

As of 19 August 2026, this Paper Citation Record lists 42 of 42 outbound references and 0 inbound Pith citation observations for arXiv:2412.09289.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2412.09289 v1

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measured 42 of 42 reference resolution

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Pith citing papers itemized under the disclosed page cap.

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42 of 42 outbound references displayed

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Outbound references

Observation 6c787473-2b06-4077-a430-be8680e3836c · outbound

This paper cites Tinyml applications and use cases for healthcare,.

Optimising TinyML with Quantization and Distillation of Transformer and Mamba Models for Indoor Localisation on Edge Devices Tinyml applications and use cases for healthcare,

Reference 1

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This paper cites Activity monitoring and location sensory system for people with mild cognitive impairments,.

Optimising TinyML with Quantization and Distillation of Transformer and Mamba Models for Indoor Localisation on Edge Devices Activity monitoring and location sensory system for people with mild cognitive impairments,

Reference 2

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This paper cites Survey of indoor location technologies and wayfinding systems for users with cognitive disabilities in emergencies,.

Optimising TinyML with Quantization and Distillation of Transformer and Mamba Models for Indoor Localisation on Edge Devices Survey of indoor location technologies and wayfinding systems for users with cognitive disabilities in emergencies,

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Observation a995dbfd-4b08-4519-8a61-37bd40da9381 · outbound

This paper cites A tinyml deep learning approach for indoor tracking of assets,.

Optimising TinyML with Quantization and Distillation of Transformer and Mamba Models for Indoor Localisation on Edge Devices A tinyml deep learning approach for indoor tracking of assets,

Reference 4

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This paper cites Detecting Signatures of Early-stage Dementia with Behavioural Models Derived from Sensor Data.

Optimising TinyML with Quantization and Distillation of Transformer and Mamba Models for Indoor Localisation on Edge Devices Detecting Signatures of Early-stage Dementia with Behavioural Models Derived from Sensor Data

Reference 5

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This paper cites Vesta: A digital health analytics platform for a smart home in a box,.

Optimising TinyML with Quantization and Distillation of Transformer and Mamba Models for Indoor Localisation on Edge Devices Vesta: A digital health analytics platform for a smart home in a box,

Reference 6

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This paper cites Tinyml using neural networks for resource-constrained devices,.

Optimising TinyML with Quantization and Distillation of Transformer and Mamba Models for Indoor Localisation on Edge Devices Tinyml using neural networks for resource-constrained devices,

Reference 7

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This paper cites A survey of quantiza- tion methods for efficient neural network inference,.

Optimising TinyML with Quantization and Distillation of Transformer and Mamba Models for Indoor Localisation on Edge Devices A survey of quantiza- tion methods for efficient neural network inference,

Reference 8

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Observation c14611aa-cd3a-4b47-8ea6-03a13a282392 · outbound

This paper cites Distilling the Knowledge in a Neural Network.

Optimising TinyML with Quantization and Distillation of Transformer and Mamba Models for Indoor Localisation on Edge Devices Distilling the Knowledge in a Neural Network

Reference 9

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Observation 28603afd-fea2-46fb-ab82-5d732c9613b1 · outbound

This paper cites Machine learning techniques for indoor localization on edge devices: Integrating ai with embedded devices for indoor localization purposes,.

Optimising TinyML with Quantization and Distillation of Transformer and Mamba Models for Indoor Localisation on Edge Devices Machine learning techniques for indoor localization on edge devices: Integrating ai with embedded devices for indoor localization purposes,

Reference 10

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Observation 00bcf6e4-54fc-4a65-8a27-19a147db203e · outbound

This paper cites Multimodal indoor localisation in parkinson’s disease for detecting medication use: Observational pilot study in a free- living setting,.

Optimising TinyML with Quantization and Distillation of Transformer and Mamba Models for Indoor Localisation on Edge Devices Multimodal indoor localisation in parkinson’s disease for detecting medication use: Observational pilot study in a free- living setting,

Reference 11

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This paper cites A review on tinyml: State-of-the-art and prospects,.

Optimising TinyML with Quantization and Distillation of Transformer and Mamba Models for Indoor Localisation on Edge Devices A review on tinyml: State-of-the-art and prospects,

Reference 12

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Observation 6a211aa6-add2-47e1-8f38-02915db78fdd · outbound

This paper cites To prune, or not to prune: exploring the efficacy of pruning for model compression.

Optimising TinyML with Quantization and Distillation of Transformer and Mamba Models for Indoor Localisation on Edge Devices To prune, or not to prune: exploring the efficacy of pruning for model compression

Reference 13

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This paper cites Low-rank matrix factorization for deep neural network training with high-dimensional output targets,.

Optimising TinyML with Quantization and Distillation of Transformer and Mamba Models for Indoor Localisation on Edge Devices Low-rank matrix factorization for deep neural network training with high-dimensional output targets,

Reference 14

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Observation d00de16b-0d91-44cb-bb27-e56bba3dc427 · outbound

This paper cites A compre- hensive survey on tinyml,.

Optimising TinyML with Quantization and Distillation of Transformer and Mamba Models for Indoor Localisation on Edge Devices A compre- hensive survey on tinyml,

Reference 15

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Observation 3ab34dfa-1c39-4fbb-8c9d-642c337c3e29 · outbound

This paper cites Model compression via distillation and quantization.

Optimising TinyML with Quantization and Distillation of Transformer and Mamba Models for Indoor Localisation on Edge Devices Model compression via distillation and quantization

Reference 16

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This paper cites Channel state information based device free wireless sensing for iot devices employing tinyml,.

Optimising TinyML with Quantization and Distillation of Transformer and Mamba Models for Indoor Localisation on Edge Devices Channel state information based device free wireless sensing for iot devices employing tinyml,

Reference 17

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This paper cites Design space exploration of a multi-model ai-based indoor localization system,.

Optimising TinyML with Quantization and Distillation of Transformer and Mamba Models for Indoor Localisation on Edge Devices Design space exploration of a multi-model ai-based indoor localization system,

Reference 18

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This paper cites A tinyml-approach to detect the proximity of people based on bluetooth low energy beacons,.

Optimising TinyML with Quantization and Distillation of Transformer and Mamba Models for Indoor Localisation on Edge Devices A tinyml-approach to detect the proximity of people based on bluetooth low energy beacons,

Reference 19

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Observation 55946fee-3510-4adf-9578-7a7a09bf5d3a · outbound

This paper cites Tiny but mighty: Embedded machine learning for indoor wireless localization,.

Optimising TinyML with Quantization and Distillation of Transformer and Mamba Models for Indoor Localisation on Edge Devices Tiny but mighty: Embedded machine learning for indoor wireless localization,

Reference 20

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Observation 6fccee1a-8684-4f55-8099-3d4577b43aaf · outbound

This paper cites A fast indoor positioning using a knowledge-distilled convolutional neural network (kd-cnn),.

Optimising TinyML with Quantization and Distillation of Transformer and Mamba Models for Indoor Localisation on Edge Devices A fast indoor positioning using a knowledge-distilled convolutional neural network (kd-cnn),

Reference 21

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This paper cites Teacher-assistant knowledge distillation based indoor positioning system,.

Optimising TinyML with Quantization and Distillation of Transformer and Mamba Models for Indoor Localisation on Edge Devices Teacher-assistant knowledge distillation based indoor positioning system,

Reference 22

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Optimising TinyML with Quantization and Distillation of Transformer and Mamba Models for Indoor Localisation on Edge Devices Knowledge distillation for a lightweight deep learning-based indoor positioning system on edge environments,

Reference 23

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Optimising TinyML with Quantization and Distillation of Transformer and Mamba Models for Indoor Localisation on Edge Devices Knowledge distillation based deep learning model for user equipment positioning in massive mimo systems using flying reconfigurable intelligent surfaces,

Reference 24

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Optimising TinyML with Quantization and Distillation of Transformer and Mamba Models for Indoor Localisation on Edge Devices Attention is all you need,

Reference 25

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Optimising TinyML with Quantization and Distillation of Transformer and Mamba Models for Indoor Localisation on Edge Devices A survey of transformers,

Reference 26

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Observation 680f7f04-8b9f-4923-b046-cf8ca6633701 · outbound

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Optimising TinyML with Quantization and Distillation of Transformer and Mamba Models for Indoor Localisation on Edge Devices Mamba: Linear-Time Sequence Modeling with Selective State Spaces

Reference 27

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Optimising TinyML with Quantization and Distillation of Transformer and Mamba Models for Indoor Localisation on Edge Devices TimeMachine: A Time Series is Worth 4 Mambas for Long-term Forecasting

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Optimising TinyML with Quantization and Distillation of Transformer and Mamba Models for Indoor Localisation on Edge Devices Residential wearable rssi and accelerometer measurements with detailed location annotations,

Reference 29

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Observation b0fbbb0d-8b57-44e7-abba-455733b64817 · outbound

This paper cites Ujiindoorloc: A new multi-building and multi-floor database for wlan fingerprint-based indoor localization problems,.

Optimising TinyML with Quantization and Distillation of Transformer and Mamba Models for Indoor Localisation on Edge Devices Ujiindoorloc: A new multi-building and multi-floor database for wlan fingerprint-based indoor localization problems,

Reference 30

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This paper cites A survey on indoor positioning systems for iot-based applications,.

Optimising TinyML with Quantization and Distillation of Transformer and Mamba Models for Indoor Localisation on Edge Devices A survey on indoor positioning systems for iot-based applications,

Reference 31

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Optimising TinyML with Quantization and Distillation of Transformer and Mamba Models for Indoor Localisation on Edge Devices Gpt3. int8 (): 8-bit matrix multiplication for transformers at scale,

Reference 32

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Optimising TinyML with Quantization and Distillation of Transformer and Mamba Models for Indoor Localisation on Edge Devices A survey of techniques for optimizing transformer inference,

Reference 33

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Optimising TinyML with Quantization and Distillation of Transformer and Mamba Models for Indoor Localisation on Edge Devices Mamba in Speech: Towards an Alternative to Self-Attention

Reference 34

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Optimising TinyML with Quantization and Distillation of Transformer and Mamba Models for Indoor Localisation on Edge Devices Saturn: Sample-efficient Generative Molecular Design using Memory Manipulation

Reference 35

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Optimising TinyML with Quantization and Distillation of Transformer and Mamba Models for Indoor Localisation on Edge Devices Accurate post training quantiza- tion with small calibration sets,

Reference 36

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Optimising TinyML with Quantization and Distillation of Transformer and Mamba Models for Indoor Localisation on Edge Devices Quantization and training of neural networks for efficient integer-arithmetic-only inference,

Reference 37

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Optimising TinyML with Quantization and Distillation of Transformer and Mamba Models for Indoor Localisation on Edge Devices Up or down? adap- tive rounding for post-training quantization,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:08:50.358416Z

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This paper cites Less is more: Task-aware layer-wise distillation for language model compression,.

Optimising TinyML with Quantization and Distillation of Transformer and Mamba Models for Indoor Localisation on Edge Devices Less is more: Task-aware layer-wise distillation for language model compression,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:08:50.342226Z

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Observation a9dd328b-7232-4c1f-a7ee-a6398c7ab961 · outbound

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Optimising TinyML with Quantization and Distillation of Transformer and Mamba Models for Indoor Localisation on Edge Devices FitNets: Hints for Thin Deep Nets

Reference 40

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Observation da7629e7-c7c9-4aab-9478-ca644a6d196d · outbound

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Optimising TinyML with Quantization and Distillation of Transformer and Mamba Models for Indoor Localisation on Edge Devices Darkrank: Accelerating deep metric learning via cross sample similarities transfer,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:08:50.327582Z

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Observation 667d78de-cd1e-496c-9fbe-98d179653bc6 · outbound

This paper cites Categories of response-based, feature-based, and relation- based knowledge distillation,.

Optimising TinyML with Quantization and Distillation of Transformer and Mamba Models for Indoor Localisation on Edge Devices Categories of response-based, feature-based, and relation- based knowledge distillation,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:08:50.310106Z

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Pith citing papers

No inbound Pith citation observations are available.