Pith. sign in

REVIEW 6 major objections 5 minor 40 references

Conformal Prediction for Indoor Positioning with Correctness Coverage Guarantees

T0 review · 6 major / 5 minor · reviewed 2026-08-16 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read Under exchangeable calibration data, conformal prediction guarantees the true position is in the predicted set with probability at least $1-\alpha$.

desk verdict Split conformal prediction applied to indoor positioning: the coverage proof is standard and fine, but the risk-control losses don't match the stated FDR/FNR, and the experiments don't test the paper's own exchangeability caveat. read the letter →

arxiv 2505.01810 v1 pith:DRPIEKI6 submitted 2025-05-03 cs.LG

Zhiyi Zhou , Hexin Peng , Hongyu Long This is my paper · ORCID
classification cs.LG
keywords conformalpredictionindoorpositioninguncertaintyquantificationcoverageguaranteefingerprintlocalizationriskcontrolpathnavigationdeeplearning
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

This paper applies conformal prediction to deep-learning-based indoor positioning and claims a statistical guarantee: for any user-chosen error rate $\alpha$, the prediction set built from the model's non-conformity scores contains the true position with probability at least $1-\alpha$. The guarantee is distribution-free and works with any positioning model, provided calibration and test data are exchangeable. The paper extends the same idea to path navigation by controlling false discovery and false negative rates, and adds a conformal p-value scheme for flagging unreliable positioning points. Experiments on a Wi-Fi fingerprint dataset show that empirical coverage tracks the target coverage across five lightweight models, with the prediction set size serving as an interpretable uncertainty measure.

What carries the argument

The load-bearing object is the non-conformity score and its quantile threshold. The paper computes scores on calibration data, sets $\hat{q}$ as the empirical quantile of those scores, and builds prediction sets by thresholding the test score at $\hat{q}$. Exchangeability of calibration and test scores makes the rank of the test score uniform, and that uniformity is the entire source of the coverage guarantee. For risk control, the same quantile logic is applied to loss functions that are non-increasing in the threshold parameter $\lambda$, yielding bounds on expected loss for false discoveries and false negatives.

What would settle it

Collect calibration fingerprints at one time or with one device, then test on fingerprints taken later or with a different device; if the empirical coverage over many test points falls materially below $1-\alpha$, the exchangeability-based guarantee has failed.

Watch

Extended reading notes

Core claim

The paper's central claim is that uncertainty in indoor positioning can be made statistically accountable. Using split conformal prediction, the authors convert the model's positioning error into a non-conformity score, typically the Euclidean distance between predicted and true coordinates, $s_i = \|Y_i - \hat{Y}_i\|_2$. They then set a threshold $\hat{q}$ as the smallest calibration score such that the fraction of scores below it reaches $\lceil (n+1)(1-\alpha)\rceil/(n+1)$, and the prediction set for a test point is $\{y : s(x_{\text{test}}, y) \le \hat{q}\}$. Under exchangeability, this set contains the true position with probability between $1-\alpha$ and $1-\alpha + 1/(n+1)$. The same machinery, with monotone loss functions defined on path points, controls expected false discovery and false negative rates in navigation, and the conformal p-value bounds the chance of wrongly rejecting the true position.

Load-bearing premise

The guarantee collapses if calibration and test data are not exchangeable; the paper concedes that temporal signal drift, path-sequence correlation, and device heterogeneity can break exchangeability, and its fixes are heuristic rather than part of the proof.

Editorial extensions

If this is right

  • System designers can choose $\alpha$ and receive a prediction set that contains the true position with probability at least $1-\alpha$, independent of which deep model produced the scores.
  • Prediction set size becomes an interpretable uncertainty measure: at the same $\alpha$, EfficientNet gives smaller sets than VGG19, indicating higher confidence.
  • Path navigation can be operated under explicit false-discovery and false-negative budgets, so the rate of misleading instructions is bounded rather than merely hoped for.
  • The conformal p-value filter lets an application suppress positioning points whose reliability is low, keeping the proportion of position-error points below a specified significance level.

Reading between the lines

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

  • The paper's guarantee is conditional on exchangeability; in deployment, stale calibration data from signal drift or device change would likely push empirical coverage below $1-\alpha$, which can be tested by splitting the dataset by time or device.
  • The conformal p-value filter could double as an online distribution-shift detector: a sustained drop in accepted points would signal that the environment has changed and recalibration is due.
  • Because the coverage proof is distribution-free, the same framework should extend to other fingerprint modalities, such as geomagnetic or Bluetooth signals, with no change to the guarantee.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

6 major / 5 minor

Summary. The paper applies split conformal prediction (Split CP) to deep learning-based fingerprint indoor positioning. It claims a finite-sample coverage guarantee for prediction sets, proposes conformal risk control to manage FDR and FNR in path navigation, and introduces a conformal p-value framework for pointwise reliability assessment. Experiments on UJIIndoLoc with MobileNetV1, VGG19, MobileNetV2, ResNet50, and EfficientNet compare empirical coverage with target coverage and report average prediction set sizes. The basic coverage argument in Section 3.1 is a standard Split CP derivation, but the paper's extensions and experiments contain several load-bearing gaps: the exchangeability condition is acknowledged to be violated in the motivating settings but is not tested there, the FDR/FNR loss definitions do not match the quantities being controlled, the p-value proof is not rigorous with tied scores, and several quantitative claims in the abstract and contributions are not supported by the reported experiments.

Significance. If the stated guarantees held, applying conformal prediction to indoor positioning would be practically valuable, especially for safety-critical location-based services. The paper usefully demonstrates that a standard Split CP wrapper can be placed on top of off-the-shelf CNN classifiers and that empirical coverage tracks nominal coverage on a random split of UJIIndoLoc. However, the claimed contributions for path navigation and pointwise reliability are not established: the risk-control losses do not implement FDR/FNR as defined, the p-value proof is not valid with tied scores, and the experiments do not test the settings in which the paper itself says exchangeability fails. The manuscript therefore does not currently support its headline promise of correctness coverage guarantees in practical indoor deployments.

major comments (6)
  1. [§3.1.2, §4.1.1] The paper explicitly identifies temporal dynamics, path-sequence correlation, and device heterogeneity as factors violating exchangeability, and it concedes that the proposed mitigations 'don't eliminate exchangeability concerns.' Yet the guarantee in Eq. (1) is a finite-sample statement that holds only under exchangeability. The random 70/10/20 split in §4.1.1 preserves exchangeability by construction, so the experiments do not validate the guarantee in the multi-device, time-varying path-navigation settings that motivate the paper. The authors should either restrict the central claims to static exchangeable fingerprinting or provide quantitative tests under distribution shift (e.g., time-based splits, device-held-out splits) and a robustness analysis showing how coverage degrades when exchangeability fails.
  2. [§3.2.1, Eqs. (12)-(15)] The FDR/FNR instantiations do not satisfy the conformal risk control theorem as stated. The theorem controls E[L_{n+1}(λhat)] ≤ β for the chosen loss, but the FDR defined in Eq. (13) is an expectation of a ratio over path points, not a sum of individual losses. Eq. (12) uses squared error rather than the false-discovery indicator 1{||Y_i − Ŷ_i||² > λ and P_i = 1}, and it does not include the denominator used in Eq. (13). The same mismatch appears in Eqs. (14)-(15) for FNR. Consequently, the claimed guarantees FDR(λhat_FDR) ≤ β_FDR and FNR(λhat_FNR) ≤ β_FNR do not follow from Eq. (11). The loss functions and the target risks must be aligned, or the claims must be restricted to the quantity actually controlled.
  3. [§3.3, Eqs. (19)-(21)] The p-value proof is not valid as written. The equality P(1 + Σ_i 1{s_i ≥ s(x_test, y)} ≤ ⌊(n+1)α⌋) = ⌊(n+1)α⌋/(n+1) assumes that the rank of s(x_test, y) among the calibration scores is exactly uniform, which fails when scores are tied (e.g., duplicated softmax probabilities or identical Euclidean error distances). The standard argument requires care with the direction of the inequality or randomized tie-breaking. In addition, the calibration score definition s_i = max_{y_j∈[m]_i} s(x_i, y_j) does not match the score construction used elsewhere in the paper; for the null hypothesis y = Y_test, the comparison should use the calibration score s(x_i, Y_i).
  4. [§4.1.3, Table 1] Average prediction set sizes below 1 (e.g., 0.135 for EfficientNet at α = 1) are not interpretable with the given problem setup. If a prediction set is a subset of K discrete locations, its cardinality is integer-valued; if it is a region in continuous coordinates, the paper must define its 'size' (e.g., area, number of grid cells) and explain how candidate positions are enumerated for thresholding by Euclidean distance. As presented, Table 1 and Figure 6 do not provide a well-defined uncertainty-efficiency comparison.
  5. [Eq. (2), §3.1.1] The quantile formula in Eq. (2) is inconsistent with the proof and with standard split conformal prediction. It compares the empirical count over n calibration scores to a threshold with denominator n while the left-hand side has denominator n+1; the standard finite-sample corrected quantile is the ⌈(n+1)(1−α)⌉-th smallest calibration score, which is what the proof in Eq. (7) uses. The displayed formula should be corrected, otherwise the implementation and the theoretical derivation do not match.
  6. [§1, §4] The abstract and contribution list contain quantitative claims — 'up to 15% improvement in positioning reliability,' 'reducing misleading navigation instructions by 23%,' and 'outperforms traditional confidence scoring methods' — that are not supported by any experiment, baseline, or comparison in the paper. The experiments only compare empirical coverage to target coverage and report prediction set sizes; there is no evaluation against Bayesian neural networks, ensembles, MC-dropout, or heuristic confidence scores, despite Section 2.3 motivating the work through those baselines.
minor comments (5)
  1. [§4.1.2] The text 'VGG19p Simonyan and Zisserman' appears to contain a typo; it should read 'VGG19' followed by the citation.
  2. [§3.1, proof after Eq. (7)] The coverage proof states an exact equality P(Y_test ∈ C(X_test)) = ⌈(n+1)(1−α)⌉/(n+1). This equality, and the upper bound in Eq. (1), hold only under a continuity/no-ties condition; the lower bound is the robust statement and should be emphasized as such.
  3. [§3.3] The notation y_j∈[m]_i is undefined; the candidate label set for each calibration point should be formally defined before it is used in the score definition.
  4. [References] Foundational conformal prediction and conformal risk control results are cited primarily through a narrow set of recent papers by Wang et al.; the primary literature (e.g., Vovk et al., Shafer and Vovk, and Angelopoulos et al. for risk control) should be cited so that readers can verify the standard results.
  5. [§4.3] The architectural analysis in Section 4.2 is speculative and not supported by ablations; statements such as 'EfficientNet... allows the model to focus on the most discriminative features' should be presented as hypotheses rather than conclusions.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity found: the coverage guarantee follows from exchangeability via the paper's own proof, and the calibration-then-evaluation procedure is the standard split-conformal protocol.

full rationale

The paper's central claim, the coverage guarantee in Eq. (1), is not fitted or defined in terms of its own output. The proof in Section 3.1.2 derives the bound directly from exchangeability of the calibration and test scores, using the standard quantile argument: P(Y_test in C(X_test)) = P(s_test <= s_{ceil((n+1)(1-alpha))}) = ceil((n+1)(1-alpha))/(n+1), which is between 1-alpha and 1-alpha + 1/(n+1). The calibration threshold qhat is estimated from a held-out calibration set and then evaluated on a disjoint test set, which is exactly the canonical split-conformal procedure and not a case of fitting a parameter and calling it a prediction. The conformal risk control and conformal p-value results are likewise restatements of exchangeability-based lemmas, with proofs included in the text rather than imported as unexplained self-citations. The CP references cited in Sections 1 and 3.1 are to external prior work, and while some of those papers share overlapping author groups with each other, they do not overlap with the current paper's authors, and in any case the load-bearing theorem is proven in this manuscript. The paper also explicitly acknowledges that temporal dynamics, sequence correlation, and device heterogeneity can violate exchangeability, and it does not attempt to claim that the guarantee holds under those violations; this is a limitation, not a circularity. Therefore no step in the derivation reduces to its own inputs by construction.

Assumptions & free parameters 2 free parameters · 4 assumptions · 0 invented entities

The paper rests on standard conformal prediction machinery. The only assumptions are exchangeability, boundedness/monotonicity of losses, and a held-out calibration set. No new entities are introduced. The conformal quantile and risk threshold are calibrated from data but are algorithmically determined, not ad hoc hand-tuning.

free parameters (2)
  • Significance level alpha (user-specified) = 0.05 as example; swept from 0 to 1 in experiments
    Target error rate input to conformal prediction. Not fitted to data; it is a user-determined hyperparameter.
  • Conformal risk control threshold lambda = Estimated from calibration data via Equation (8)
    Calibrated to bound expected loss. It is algorithmically determined, not an ad hoc parameter, but it does depend on the calibration set.
assumptions (4)
  • domain assumption Exchangeability of calibration and test data
    Split conformal prediction provides coverage only under exchangeability. The paper acknowledges temporal drift, path correlation, and device heterogeneity may violate it (Section 3.1.2), and its mitigations are heuristic.
  • domain assumption Bounded, non-increasing, right-continuous loss functions for risk control
    The conformal risk control bound in Equation (8) requires these conditions. The FDR/FNR losses in Equations (12) and (14) are not verified against them, and the FDR/FNR ratio definitions do not match the loss definitions.
  • domain assumption Held-out calibration set disjoint from training
    The paper manually partitions UJIIndoLoc into 70% train, 10% calibration, 20% test (Section 4.1.1).
  • standard math Almost surely distinct non-conformity scores
    The coverage proof uses P(s_test <= s_k) = k/(n+1), which requires continuous scores without ties; the paper does not address ties.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Conformal Prediction for Indoor Positioning with Correctness Coverage Guarantees." pith.science (2026). https://pith.science/paper/DRPIEKI6

@misc{pith2026250501810,
  author       = {Pith},
  title        = {Pith review of: Conformal Prediction for Indoor Positioning with Correctness Coverage Guarantees},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/DRPIEKI6}},
  note         = {Machine review of arXiv:2505.01810}
}
read the original abstract

With the advancement of Internet of Things (IoT) technologies, high-precision indoor positioning has become essential for Location-Based Services (LBS) in complex indoor environments. Fingerprint-based localization is popular, but traditional algorithms and deep learning-based methods face challenges such as poor generalization, overfitting, and lack of interpretability. This paper applies conformal prediction (CP) to deep learning-based indoor positioning. CP transforms the uncertainty of the model into a non-conformity score, constructs prediction sets to ensure correctness coverage, and provides statistical guarantees. We also introduce conformal risk control for path navigation tasks to manage the false discovery rate (FDR) and the false negative rate (FNR).The model achieved an accuracy of approximately 100% on the training dataset and 85% on the testing dataset, effectively demonstrating its performance and generalization capability. Furthermore, we also develop a conformal p-value framework to control the proportion of position-error points. Experiments on the UJIIndoLoc dataset using lightweight models such as MobileNetV1, VGG19, MobileNetV2, ResNet50, and EfficientNet show that the conformal prediction technique can effectively approximate the target coverage, and different models have different performance in terms of prediction set size and uncertainty quantification.

Figures

Figures reproduced from arXiv: 2505.01810 by the authors.

Figure 1
Figure 1. RSSI feature classification. 4.1.3 Conformal Prediction Implementation We implemented the standard Split Conformal Prediction approach. The procedure consisted of these steps: 1. Train the model on the training dataset (70% of the data). 2. For each sample in the calibration set (10% of the data), compute the non-conformity score defined as the Euclidean distance between the predicted coordinates and the ground trut… view at source ↗
Figure 2
Figure 2. Path positioning. prediction technique effectively approximates the target coverage in these deep learning models. Despite the theoretical guarantee of SCP being rigorous, there can be minor fluctuations in practice due to finite-sample variability. In real-world tasks of indoor positioning, test points may deviate from the distribution of the calibration set, and at this point, there will be miscalibration issues o… view at source ↗
Figure 3
Figure 3. Training and testing loss [PITH_FULL_IMAGE:figures/full_fig_p012_3.png] view at source ↗
Figures from the paper (3 more)
Figure 4
Figure 4. Figure 4: Training and testing accuracy. In contrast, VGG19’s larger prediction sets reflect greater uncertainty in its positioning estimates, which can be attributed to several architectural limitations. First, its uniform architecture lacks modern bottleneck designs, following…
Figure 5
Figure 5. Figure 5: Empirical and target coverage [PITH_FULL_IMAGE:figures/full_fig_p013_5.png]
Figure 6
Figure 6. Figure 6: Average prediction set size. MobileNetV1 and MobileNetV2 both achieve a balance between parameter efficiency and prediction certainty, with MobileNetV2 showing slightly better performance due to its inverted residual structure. ResNet50, despite its depth, performs rea…

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

40 extracted references · 33 canonical work pages

  1. [1]

    A survey on indoor positioning systems for iot-based applications

    Pooyan Shams Farahsari, Amirhossein Farahzadi, Javad Rezazadeh, and Alireza Bagheri. A survey on indoor positioning systems for iot-based applications. IEEE Internet of Things Journal, 9 0 (10): 0 7680--7699, 2022

    work page 2022

  2. [2]

    Indoor positioning system: A review

    Nur Haliza Abdul Wahab, Noorhazirah Sunar, Sharifah HS Ariffin, Keng Yinn Wong, Yichiet Aun, et al. Indoor positioning system: A review. International Journal of Advanced Computer Science and Applications, 13 0 (6), 2022

    work page 2022

  3. [3]

    Indoor intelligent fingerprint-based localization: Principles, approaches and challenges

    Xiaoqiang Zhu, Wenyu Qu, Tie Qiu, Laiping Zhao, Mohammed Atiquzzaman, and Dapeng Oliver Wu. Indoor intelligent fingerprint-based localization: Principles, approaches and challenges. IEEE Communications Surveys & Tutorials, 22 0 (4): 0 2634--2657, 2020

    work page 2020

  4. [4]

    Deep learning methods for fingerprint-based indoor positioning: A review

    Fahad Alhomayani and Mohammad H Mahoor. Deep learning methods for fingerprint-based indoor positioning: A review. Journal of Location Based Services, 14 0 (3): 0 129--200, 2020

    work page 2020

  5. [5]

    Caterpillar: A pure-mlp architecture with shifted-pillars-concatenation

    Jin Sun, Xiaoshuang Shi, Zhiyuan Wang, Kaidi Xu, Heng Tao Shen, and Xiaofeng Zhu. Caterpillar: A pure-mlp architecture with shifted-pillars-concatenation. In Proceedings of the 32nd ACM International Conference on Multimedia, pages 7123--7132, 2024

    work page 2024

  6. [6]

    A survey on deep learning tools dealing with data scarcity: definitions, challenges, solutions, tips, and applications

    Laith Alzubaidi, Jinshuai Bai, Aiman Al-Sabaawi, Jose Santamar \' a, Ahmed Shihab Albahri, Bashar Sami Nayyef Al-dabbagh, Mohammed A Fadhel, Mohamed Manoufali, Jinglan Zhang, Ali H Al-Timemy, et al. A survey on deep learning tools dealing with data scarcity: definitions, challenges, solutions, tips, and applications. Journal of Big Data, 10 0 (1): 0 46, 2023

    work page 2023

  7. [7]

    Addressing failure prediction by learning model confidence

    Charles Corbi \`e re, Nicolas Thome, Avner Bar-Hen, Matthieu Cord, and Patrick P \'e rez. Addressing failure prediction by learning model confidence. Advances in neural information processing systems, 32, 2019

    work page 2019

  8. [8]

    Positioning using wireless networks: Applications, recent progress and future challenges

    Yang Yang, Mingzhe Chen, Yufei Blankenship, Jemin Lee, Zabih Ghassemlooy, Julian Cheng, and Shiwen Mao. Positioning using wireless networks: Applications, recent progress and future challenges. IEEE Journal on Selected Areas in Communications, 2024

    work page 2024

Show all 40 references
  1. [9]

    A survey of machine learning for indoor positioning

    Ahasanun Nessa, Bhagawat Adhikari, Fatima Hussain, and Xavier N Fernando. A survey of machine learning for indoor positioning. IEEE access, 8: 0 214945--214965, 2020

    2020

  2. [10]

    Robust human locomotion and localization activity recognition over multisensory

    Danyal Khan, Mohammed Alonazi, Maha Abdelhaq, Naif Al Mudawi, Asaad Algarni, Ahmad Jalal, and Hui Liu. Robust human locomotion and localization activity recognition over multisensory. Frontiers in Physiology, 15: 0 1344887, 2024

    2024

  3. [11]

    A survey of methods for explaining black box models

    Riccardo Guidotti, Anna Monreale, Salvatore Ruggieri, Franco Turini, Fosca Giannotti, and Dino Pedreschi. A survey of methods for explaining black box models. ACM computing surveys (CSUR), 51 0 (5): 0 1--42, 2018

    2018

  4. [12]

    Conu: Conformal uncertainty in large language models with correctness coverage guarantees

    Zhiyuan Wang, Jinhao Duan, Lu Cheng, Yue Zhang, Qingni Wang, Xiaoshuang Shi, Kaidi Xu, Heng Tao Shen, and Xiaofeng Zhu. Conu: Conformal uncertainty in large language models with correctness coverage guarantees. In Findings of the Association for Computational Linguistics: EMNL...

    2024

  5. [13]

    Sample then identify: A general framework for risk control and assessment in multimodal large language models

    Qingni Wang, Tiantian Geng, Zhiyuan Wang, Teng Wang, Bo Fu, and Feng Zheng. Sample then identify: A general framework for risk control and assessment in multimodal large language models. In The Thirteenth International Conference on Learning Representations, 2025 a

    2025

  6. [14]

    Word-sequence entropy: Towards uncertainty estimation in free-form medical question answering applications and beyond

    Zhiyuan Wang, Jinhao Duan, Chenxi Yuan, Qingyu Chen, Tianlong Chen, Yue Zhang, Ren Wang, Xiaoshuang Shi, and Kaidi Xu. Word-sequence entropy: Towards uncertainty estimation in free-form medical question answering applications and beyond. Engineering Applications of Artificial ...

    2025

  7. [15]

    Survey on federated learning enabling indoor navigation for industry 4.0 in b5g

    Saeed Hamood Alsamhi, Alexey V Shvetsov, Ammar Hawbani, Svetlana V Shvetsova, Santosh Kumar, and Liang Zhao. Survey on federated learning enabling indoor navigation for industry 4.0 in b5g. Future Generation Computer Systems, 148: 0 250--265, 2023

    2023

  8. [16]

    Roadmap on signal processing for next generation measurement systems

    Dimitris K Iakovidis, Melanie Ooi, Ye Chow Kuang, Serge Demidenko, Alexandr Shestakov, Vladimir Sinitsin, Manus Henry, Andrea Sciacchitano, Stefano Discetti, Silvano Donati, et al. Roadmap on signal processing for next generation measurement systems. Measurement Science and Te...

    2021

  9. [17]

    Application and evaluation of quantization for narrow bit-width resampling of sequential monte carlo

    Hiroki Nishimoto, Renyuan Zhang, and Yasuhiko Nakashima. Application and evaluation of quantization for narrow bit-width resampling of sequential monte carlo. In 2022 IEEE 35th International System-on-Chip Conference (SOCC), pages 1--6. IEEE, 2022

    2022

  10. [18]

    Method for distributed node metric measurement in wireless sensor networks based on multiple attributes

    Tao Liu and Chunwei Zhang. Method for distributed node metric measurement in wireless sensor networks based on multiple attributes. International Journal of High Speed Electronics and Systems, page 2440118, 2024

    2024

  11. [19]

    Zigbee-based indoor location system by k-nearest neighbor algorithm with weighted rssi

    Chih-Ning Huang and Chia-Tai Chan. Zigbee-based indoor location system by k-nearest neighbor algorithm with weighted rssi. Procedia Computer Science, 5: 0 58--65, 2011

    2011

  12. [20]

    Metaloc: Learning to learn wireless localization

    Jun Gao, Dongze Wu, Feng Yin, Qinglei Kong, Lexi Xu, and Shuguang Cui. Metaloc: Learning to learn wireless localization. IEEE Journal on Selected Areas in Communications, 2023

    2023

  13. [21]

    A novel convolutional neural network based indoor localization framework with wifi fingerprinting

    Xudong Song, Xiaochen Fan, Chaocan Xiang, Qianwen Ye, Leyu Liu, Zumin Wang, Xiangjian He, Ning Yang, and Gengfa Fang. A novel convolutional neural network based indoor localization framework with wifi fingerprinting. IEEE access, 7: 0 110698--110709, 2019

    2019

  14. [22]

    A survey on ubiquitous wifi-based indoor localization system for smartphone users from implementation perspectives

    Priya Roy and Chandreyee Chowdhury. A survey on ubiquitous wifi-based indoor localization system for smartphone users from implementation perspectives. CCF Transactions on Pervasive Computing and Interaction, 4 0 (3): 0 298--318, 2022

    2022

  15. [23]

    Minloc: Magnetic field patterns-based indoor localization using convolutional neural networks

    Imran Ashraf, Mingyu Kang, Soojung Hur, and Yongwan Park. Minloc: Magnetic field patterns-based indoor localization using convolutional neural networks. IEEE Access, 8: 0 66213--66227, 2020

    2020

  16. [24]

    Indoor localization fusing wifi with smartphone inertial sensors using lstm networks

    Mingyang Zhang, Jie Jia, Jian Chen, Yansha Deng, Xingwei Wang, and Abdol Hamid Aghvami. Indoor localization fusing wifi with smartphone inertial sensors using lstm networks. IEEE Internet of Things Journal, 8 0 (17): 0 13608--13623, 2021

    2021

  17. [25]

    Attention mechanism and lstm network for fingerprint-based indoor location system

    Zhen Wu, Peng Hu, Shuangyue Liu, and Tao Pang. Attention mechanism and lstm network for fingerprint-based indoor location system. Sensors, 24 0 (5): 0 1398, 2024

    2024

  18. [26]

    A novel multi-modal feature-level fusion scheme for high accurate indoor localization

    Siyu Tang, Kaixuan Huang, and Shunqing Zhang. A novel multi-modal feature-level fusion scheme for high accurate indoor localization. IEEE Sensors Journal, 2024

    2024

  19. [27]

    Enhancing indoor localization with semi-crowdsourced fingerprinting and gan-based data augmentation

    Suhardi Azliy Junoh and Jae-Young Pyun. Enhancing indoor localization with semi-crowdsourced fingerprinting and gan-based data augmentation. IEEE Internet of Things Journal, 2023

    2023

  20. [28]

    Machine learning based indoor localization using wi-fi rssi fingerprints: An overview

    Navneet Singh, Sangho Choe, and Rajiv Punmiya. Machine learning based indoor localization using wi-fi rssi fingerprints: An overview. IEEE access, 9: 0 127150--127174, 2021

    2021

  21. [29]

    Deepvip: Deep learning-based vehicle indoor positioning using smartphones

    Baoding Zhou, Zhining Gu, Fuqiang Gu, Peng Wu, Chengjing Yang, Xu Liu, Linchao Li, Yan Li, and Qingquan Li. Deepvip: Deep learning-based vehicle indoor positioning using smartphones. IEEE Transactions on Vehicular Technology, 71 0 (12): 0 13299--13309, 2022

    2022

  22. [30]

    The adaptive fingerprint localization in dynamic environment

    Keliu Long, Chongwei Zheng, Kun Zhang, Chuan Tian, and Chong Shen. The adaptive fingerprint localization in dynamic environment. IEEE Sensors Journal, 22 0 (13): 0 13562--13580, 2022

    2022

  23. [31]

    Wideep: Wifi-based accurate and robust indoor localization system using deep learning

    Moustafa Abbas, Moustafa Elhamshary, Hamada Rizk, Marwan Torki, and Moustafa Youssef. Wideep: Wifi-based accurate and robust indoor localization system using deep learning. In 2019 IEEE International Conference on Pervasive Computing and Communications (PerCom, pages 1--10. IEEE, 2019

    2019

  24. [32]

    Uncertainty quantification and deep ensembles

    Rahul Rahaman et al. Uncertainty quantification and deep ensembles. Advances in neural information processing systems, 34: 0 20063--20075, 2021

    2021

  25. [33]

    Uncertainty estimation via monte carlo dropout in cnn-based mmwave mimo localization

    Mohammad Amin Maleki Sadr, Jo \ a o Gante, Benoit Champagne, Gabriel Falcao, and Leonel Sousa. Uncertainty estimation via monte carlo dropout in cnn-based mmwave mimo localization. IEEE Signal Processing Letters, 29: 0 269--273, 2021

    2021

  26. [34]

    Sconu: Selective conformal uncertainty in large language models

    Zhiyuan Wang, Qingni Wang, Yue Zhang, Tianlong Chen, Xiaofeng Zhu, Xiaoshuang Shi, and Kaidi Xu. Sconu: Selective conformal uncertainty in large language models. arXiv preprint arXiv:2504.14154, 2025 c

    2025 arXiv

  27. [35]

    Avariento, Tomás J

    Joaquín Torres-Sospedra, Raúl Montoliu, Adolfo Martínez-Usó, Joan P. Avariento, Tomás J. Arnau, Mauri Benedito-Bordonau, and Joaquín Huerta. Ujiindoorloc: A new multi-building and multi-floor database for wlan fingerprint-based indoor localization problems. In 2014 Internation...

    2014

  28. [36]

    Mobilenets: Efficient convolutional neural networks for mobile vision applications

    Andrew G Howard. Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861, 2017

    2017 arXiv

  29. [37]

    Very deep convolutional networks for large-scale image recognition

    Karen Simonyan and Andrew Zisserman. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556, 2014

    2014 arXiv

  30. [38]

    Inverted residuals and linear bottlenecks: Mobile networks for classification, detection and segmentation

    Andrew Howard, Andrey Zhmoginov, Liang-Chieh Chen, Mark Sandler, and Menglong Zhu. Inverted residuals and linear bottlenecks: Mobile networks for classification, detection and segmentation. In Proc. CVPR, pages 4510--4520, 2018

    2018

  31. [39]

    Deep residual learning for image recognition

    Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770--778, 2016

    2016

  32. [40]

    Efficientnet: Rethinking model scaling for convolutional neural networks

    Mingxing Tan and Quoc Le. Efficientnet: Rethinking model scaling for convolutional neural networks. In International conference on machine learning, pages 6105--6114. PMLR, 2019

    2019

Pith tools

Reviewed August 16, 2026 · model on record in the stance chip above.