Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-06T21:43:59.091234Z
Paper Citation Record · LEDGER
A Novel Active Learning Approach to Label One Million Unknown Malware Variants
As of 22 August 2026, this Paper Citation Record lists 51 of 51 outbound references and 0 inbound Pith citation observations for arXiv:2507.02959.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
51 of 51 outbound references displayed
- verified exact4
- verified fuzzy41
- unresolved6
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 1cb53df8-2e03-4802-a340-0cc04d717e8f · outbound
This paper cites Vision transformers for remote sensing image classification.
A Novel Active Learning Approach to Label One Million Unknown Malware Variants Vision transformers for remote sensing image classification
Reference 1
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T21:44:08.558649Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 65a8740c-c4f2-4394-b767-5beda7eaad13 · outbound
This paper cites an unresolved cited work.
A Novel Active Learning Approach to Label One Million Unknown Malware Variants Unresolved cited work
Reference 2
- Resolution
- unresolved
- raw_fallback, observed 2026-08-06T21:44:08.368832Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 212c6b07-74f0-4da7-b245-bd40306158eb · outbound
This paper cites Cnn-lstm and transfer learning models for malware classification based on opcodes and api calls.
A Novel Active Learning Approach to Label One Million Unknown Malware Variants Cnn-lstm and transfer learning models for malware classification based on opcodes and api calls
Reference 3
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T21:44:08.034423Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation d744a2bc-dd6a-4efe-9c6b-56d4b321f4a1 · outbound
This paper cites Optimized detection of cyber-attacks on iot networks via hybrid deep learning models.
A Novel Active Learning Approach to Label One Million Unknown Malware Variants Optimized detection of cyber-attacks on iot networks via hybrid deep learning models
Reference 4
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T21:44:07.728559Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 429d7ebe-da5d-491d-931d-7a9e90f047a5 · outbound
This paper cites A survey of malware detection using deep learning.
A Novel Active Learning Approach to Label One Million Unknown Malware Variants A survey of malware detection using deep learning
Reference 5
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T21:44:07.505740Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 9d6beb15-ee16-4e54-9444-7f83f2a0eb72 · outbound
This paper cites Understandingrobustnessoftransformersforimage classification, in: Proceedings of the IEEE/CVF international conference on computer vision, pp.
A Novel Active Learning Approach to Label One Million Unknown Malware Variants Understandingrobustnessoftransformersforimage classification, in: Proceedings of the IEEE/CVF international conference on computer vision, pp
Reference 6
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T21:44:07.274530Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 8ffd1afe-e50d-4043-9418-04a150d148ee · outbound
This paper cites Weight uncertainty in neural network, in: International conference on machine learning, PMLR.
A Novel Active Learning Approach to Label One Million Unknown Malware Variants Weight uncertainty in neural network, in: International conference on machine learning, PMLR
Reference 7
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T21:44:07.079627Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 8b9967a3-e804-4e4a-aac1-89b7b266685f · outbound
This paper cites an unresolved cited work.
A Novel Active Learning Approach to Label One Million Unknown Malware Variants Unresolved cited work
Reference 8
- Resolution
- verified exact
- raw_fallback, observed 2026-08-06T21:43:59.964170Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 4be177cb-a34c-479a-a12a-0e48bc020c14 · outbound
This paper cites End-to-end object detection with transformers, in: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part I 16, Springer.
A Novel Active Learning Approach to Label One Million Unknown Malware Variants End-to-end object detection with transformers, in: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part I 16, Springer
Reference 9
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T21:44:06.889101Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation ef24f99b-0c8d-45dc-a2a1-bff475fe938b · outbound
This paper cites Crossvit: Cross-attention multi-scale vision transformer for image classification, in: Proceedings of the IEEE/CVF international conference on computer vision, pp.
A Novel Active Learning Approach to Label One Million Unknown Malware Variants Crossvit: Cross-attention multi-scale vision transformer for image classification, in: Proceedings of the IEEE/CVF international conference on computer vision, pp
Reference 10
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T21:44:06.721688Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 36f16064-7435-4c4c-a60c-02b869664cc1 · outbound
This paper cites Malware family classification using active learning by learning, in: 2020 22nd International Conference on Advanced Communication Technology (ICACT), IEEE.
A Novel Active Learning Approach to Label One Million Unknown Malware Variants Malware family classification using active learning by learning, in: 2020 22nd International Conference on Advanced Communication Technology (ICACT), IEEE
Reference 11
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T21:44:06.528545Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 02dc6115-02b8-4668-ae66-562a7f4e945c · outbound
This paper cites Semi-supervised active learning for object detection.
A Novel Active Learning Approach to Label One Million Unknown Malware Variants Semi-supervised active learning for object detection
Reference 12
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T21:44:06.330779Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 0d055703-5f3e-4291-82ee-e2effd585227 · outbound
This paper cites an unresolved cited work.
A Novel Active Learning Approach to Label One Million Unknown Malware Variants Unresolved cited work
Reference 13
- Resolution
- unresolved
- raw_fallback, observed 2026-08-06T21:44:06.142154Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 35d146f0-41a1-44ba-b4df-669e5ef77db9 · outbound
This paper cites A novel transfer learning based approach for pneumonia detection in chest x-ray images.
A Novel Active Learning Approach to Label One Million Unknown Malware Variants A novel transfer learning based approach for pneumonia detection in chest x-ray images
Reference 14
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T21:44:05.953272Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation d6b12671-ef53-4da5-97ff-c120fa09049e · outbound
This paper cites Active learning-based mobile malware detection utilizing auto-labeling and data drift detection, in: 2024 IEEE International Conference on Cyber Security and Resilience (CSR), IEEE.
A Novel Active Learning Approach to Label One Million Unknown Malware Variants Active learning-based mobile malware detection utilizing auto-labeling and data drift detection, in: 2024 IEEE International Conference on Cyber Security and Resilience (CSR), IEEE
Reference 15
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T21:44:05.808146Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 2042d74c-9d34-424b-8c9d-26d26f6a3e87 · outbound
This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.
A Novel Active Learning Approach to Label One Million Unknown Malware Variants An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
Reference 16
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T21:43:56.411835Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d7a1a762-78f9-491c-90a4-509e765a2ce2 · outbound
This paper cites Uncertainty-guided Continual Learning with Bayesian Neural Networks.
A Novel Active Learning Approach to Label One Million Unknown Malware Variants Uncertainty-guided Continual Learning with Bayesian Neural Networks
Reference 17
- Resolution
- verified exact
- local_arxiv, observed 2026-08-06T21:43:59.682089Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 12205b36-bf60-4361-b14f-b4b956975b89 · outbound
This paper cites Efficientclassificationofimbalancednaturaldisastersdatausinggenerativeadversarial networks for data augmentation.
A Novel Active Learning Approach to Label One Million Unknown Malware Variants Efficientclassificationofimbalancednaturaldisastersdatausinggenerativeadversarial networks for data augmentation
Reference 18
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T21:44:05.617827Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation d2d0e39d-cbf2-4a21-aca5-8e32e585c898 · outbound
This paper cites Multiscale vision transformers, in: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp.
A Novel Active Learning Approach to Label One Million Unknown Malware Variants Multiscale vision transformers, in: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp
Reference 19
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T21:44:05.426872Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation ddde4ce2-755c-4f11-95ee-dccf4ce2b3db · outbound
This paper cites On the expressiveness of approximate inference in bayesian neural networks.
A Novel Active Learning Approach to Label One Million Unknown Malware Variants On the expressiveness of approximate inference in bayesian neural networks
Reference 20
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T21:44:05.288004Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 973fd5fb-5ee0-404c-8cca-44ff415eb745 · outbound
This paper cites Expertsstillneeded:boostinglong-termandroidmalwaredetectionwithactivelearning.
A Novel Active Learning Approach to Label One Million Unknown Malware Variants Expertsstillneeded:boostinglong-termandroidmalwaredetectionwithactivelearning
Reference 21
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T21:44:05.106407Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation be7ad5d3-e4df-48ad-b7b0-f762848c502b · outbound
This paper cites Evidential uncertainty sampling strategies for active learning.
A Novel Active Learning Approach to Label One Million Unknown Malware Variants Evidential uncertainty sampling strategies for active learning
Reference 22
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T21:44:04.955010Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 3bfbc1bf-0235-4dc5-8371-b2f16ae03756 · outbound
This paper cites Deep Active Learning with Augmentation-based Consistency Estimation.
A Novel Active Learning Approach to Label One Million Unknown Malware Variants Deep Active Learning with Augmentation-based Consistency Estimation
Reference 23
- Resolution
- verified exact
- local_arxiv, observed 2026-08-06T21:43:59.507409Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 5d908f0f-75a3-471f-9c93-54b5bc315888 · outbound
This paper cites Uncertainty-driven active developmental learning.
A Novel Active Learning Approach to Label One Million Unknown Malware Variants Uncertainty-driven active developmental learning
Reference 24
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T21:44:04.765277Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 3a962f89-0d06-423f-9e63-944e26a79066 · outbound
This paper cites Deepactivelearningwithweightingfilterforobjectdetection.
A Novel Active Learning Approach to Label One Million Unknown Malware Variants Deepactivelearningwithweightingfilterforobjectdetection
Reference 25
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T21:44:04.575484Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 4dce9436-c9e0-4933-9bbd-620621d0b08d · outbound
This paper cites Whatuncertaintiesdoweneedinbayesiandeeplearningforcomputervision? Advancesinneuralinformation processing systems 30.
A Novel Active Learning Approach to Label One Million Unknown Malware Variants Whatuncertaintiesdoweneedinbayesiandeeplearningforcomputervision? Advancesinneuralinformation processing systems 30
Reference 26
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T21:44:04.429213Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation ff7b7b7f-750f-4bbb-903d-73ab29fe1bd2 · outbound
This paper cites Active learning for data quality control: A survey.
A Novel Active Learning Approach to Label One Million Unknown Malware Variants Active learning for data quality control: A survey
Reference 27
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T21:44:04.211137Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation c1105461-aaf2-4977-913f-ec5e9850de03 · outbound
This paper cites Unlabeleddataselectionforactivelearninginimageclassification.
A Novel Active Learning Approach to Label One Million Unknown Malware Variants Unlabeleddataselectionforactivelearninginimageclassification
Reference 28
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T21:44:04.069248Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 8397844b-c14b-4096-8179-d3d8fe1169bc · outbound
This paper cites Deepactivelearningwithnoisestability,in:ProceedingsoftheAAAI Conference on Artificial Intelligence, pp.
A Novel Active Learning Approach to Label One Million Unknown Malware Variants Deepactivelearningwithnoisestability,in:ProceedingsoftheAAAI Conference on Artificial Intelligence, pp
Reference 29
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T21:44:03.876748Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 371576ba-de65-4fa3-be6d-446758b2f2f5 · outbound
This paper cites Uncertainty-aware twin support vector machines.
A Novel Active Learning Approach to Label One Million Unknown Malware Variants Uncertainty-aware twin support vector machines
Reference 30
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T21:44:03.682815Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 5d041f0c-db9a-4990-bfd4-b97e569dcf58 · outbound
This paper cites Active Learning Under Malicious Mislabeling and Poisoning Attacks.
A Novel Active Learning Approach to Label One Million Unknown Malware Variants Active Learning Under Malicious Mislabeling and Poisoning Attacks
Reference 31
- Resolution
- verified exact
- local_arxiv, observed 2026-08-06T21:43:59.330267Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 71d04b2d-f1be-4313-9403-4814e9e53239 · outbound
This paper cites Multiplicative normalizing flows for variational bayesian neural networks, in: International Conference on Machine Learning, PMLR.
A Novel Active Learning Approach to Label One Million Unknown Malware Variants Multiplicative normalizing flows for variational bayesian neural networks, in: International Conference on Machine Learning, PMLR
Reference 32
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T21:44:03.517387Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation b4be03dc-e7b8-4ba6-865a-441ec40b2c3b · outbound
This paper cites Multisurface proximal support vector machine classification via generalized eigenvalues.
A Novel Active Learning Approach to Label One Million Unknown Malware Variants Multisurface proximal support vector machine classification via generalized eigenvalues
Reference 33
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T21:44:03.365181Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 041d3a48-69f1-4858-9144-32e25f651050 · outbound
This paper cites Adecadesurveyoftransferlearning(2010–2020).
A Novel Active Learning Approach to Label One Million Unknown Malware Variants Adecadesurveyoftransferlearning(2010–2020)
Reference 34
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T21:44:03.170559Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation b6d5404a-2a7a-4d58-990c-56fc094e03e4 · outbound
This paper cites What is a support vector machine? Nature biotechnology 24, 1565–1567.
A Novel Active Learning Approach to Label One Million Unknown Malware Variants What is a support vector machine? Nature biotechnology 24, 1565–1567
Reference 35
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T21:44:02.991515Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 1ab1b401-81fe-40a3-a051-4e9b0d3d6c04 · outbound
This paper cites Activelearningforobjectdetectionwithevidentialdeeplearningandhierarchical uncertainty aggregation, in: The Eleventh International Conference on Learning Representations.
A Novel Active Learning Approach to Label One Million Unknown Malware Variants Activelearningforobjectdetectionwithevidentialdeeplearningandhierarchical uncertainty aggregation, in: The Eleventh International Conference on Learning Representations
Reference 36
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T21:44:02.776905Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation fd948a61-e082-4f4f-b8a4-507955a4c2d9 · outbound
This paper cites Active learning literature survey.
A Novel Active Learning Approach to Label One Million Unknown Malware Variants Active learning literature survey
Reference 37
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T21:44:02.563922Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 23a912ca-0ac8-4f9e-b494-84ba9270699f · outbound
This paper cites A mathematical theory of communication.
A Novel Active Learning Approach to Label One Million Unknown Malware Variants A mathematical theory of communication
Reference 38
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T21:44:02.388081Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation d2d6cf48-b0e4-4ea1-bcac-dc964cab9976 · outbound
This paper cites Improvements on twin support vector machines.
A Novel Active Learning Approach to Label One Million Unknown Malware Variants Improvements on twin support vector machines
Reference 39
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T21:44:02.240123Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation f5db5576-e5fa-46ba-8af7-94fa6447021c · outbound
This paper cites Rethinking deep active learning: Using unlabeled data at model training, in: 2020 25th International conference on pattern recognition (ICPR), IEEE.
A Novel Active Learning Approach to Label One Million Unknown Malware Variants Rethinking deep active learning: Using unlabeled data at model training, in: 2020 25th International conference on pattern recognition (ICPR), IEEE
Reference 40
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T21:44:02.034329Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 388ee4b6-dd76-4f80-b37f-14846f3fa1d0 · outbound
This paper cites Inception-v4,inception-resnetandtheimpactofresidualconnectionsonlearning, in: Proceedings of the AAAI Conference on Artificial Intelligence, pp.
A Novel Active Learning Approach to Label One Million Unknown Malware Variants Inception-v4,inception-resnetandtheimpactofresidualconnectionsonlearning, in: Proceedings of the AAAI Conference on Artificial Intelligence, pp
Reference 41
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T21:44:01.874727Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 4f4da4fc-43dc-460b-9365-58db4fb2641e · outbound
This paper cites an unresolved cited work.
A Novel Active Learning Approach to Label One Million Unknown Malware Variants Unresolved cited work
Reference 42
- Resolution
- unresolved
- raw_fallback, observed 2026-08-06T21:44:01.687460Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 17990291-435f-491a-ab33-9eae5d41eae0 · outbound
This paper cites an unresolved cited work.
A Novel Active Learning Approach to Label One Million Unknown Malware Variants Unresolved cited work
Reference 43
- Resolution
- unresolved
- raw_fallback, observed 2026-08-06T21:44:01.532097Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation b8a1e100-4920-49b5-9d20-1cfe2a823853 · outbound
This paper cites Fixing the train-test resolution discrepancy.
A Novel Active Learning Approach to Label One Million Unknown Malware Variants Fixing the train-test resolution discrepancy
Reference 44
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T21:44:01.312318Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 54322502-3742-4b24-9da2-b33b5479b32f · outbound
This paper cites Eigenfaces for recognition.
A Novel Active Learning Approach to Label One Million Unknown Malware Variants Eigenfaces for recognition
Reference 45
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T21:44:01.147412Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 6250c920-c1da-45ed-8965-083db1982776 · outbound
This paper cites Linear maximum margin classifier for learning from uncertain data.
A Novel Active Learning Approach to Label One Million Unknown Malware Variants Linear maximum margin classifier for learning from uncertain data
Reference 46
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T21:44:00.969436Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation c05dc242-e29a-4aed-879d-07c6c9a23be9 · outbound
This paper cites Tokens-to-token vit: Training vision transformers from scratch on imagenet, in: Proceedings of the IEEE/CVF international conference on computer vision, pp.
A Novel Active Learning Approach to Label One Million Unknown Malware Variants Tokens-to-token vit: Training vision transformers from scratch on imagenet, in: Proceedings of the IEEE/CVF international conference on computer vision, pp
Reference 47
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T21:44:00.756022Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 1d050640-1505-4247-a452-35298b9b2d0b · outbound
This paper cites Multiple instance active learning for object detection, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp.
A Novel Active Learning Approach to Label One Million Unknown Malware Variants Multiple instance active learning for object detection, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp
Reference 48
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T21:44:00.602142Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 4048b4c3-2a9b-4016-9afc-ce3abb607051 · outbound
This paper cites Cyclical Stochastic Gradient MCMC for Bayesian Deep Learning.
A Novel Active Learning Approach to Label One Million Unknown Malware Variants Cyclical Stochastic Gradient MCMC for Bayesian Deep Learning
Reference 49
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T21:43:58.958090Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 5a383b4a-f769-4a66-ad82-c723f3ad5931 · outbound
This paper cites Active learning based on belief functions.
A Novel Active Learning Approach to Label One Million Unknown Malware Variants Active learning based on belief functions
Reference 50
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T21:44:00.384816Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 4a43b9a2-55d4-456f-979f-dcd1ebd0e2dc · outbound
This paper cites Powersvm:Generalizationwithexemplarclassificationuncertainty,in:2012IEEEConference on Computer Vision and Pattern Recognition, IEEE.
A Novel Active Learning Approach to Label One Million Unknown Malware Variants Powersvm:Generalizationwithexemplarclassificationuncertainty,in:2012IEEEConference on Computer Vision and Pattern Recognition, IEEE
Reference 51
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T21:44:00.191222Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Pith citing papers
No inbound Pith citation observations are available.