Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-06T17:55:14.383990Z
Paper Citation Record · LEDGER
Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness
As of 8 August 2026, this Paper Citation Record lists 51 of 51 outbound references and 0 inbound Pith citation observations for arXiv:2507.09687.
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 2026-08-08T06:32:00.761636+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
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- unresolved17
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External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 65f7db7a-ffd4-43ae-9783-bad233b30ecf · outbound
This paper cites Pattern recognition and machine learning.
Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness Pattern recognition and machine learning
Reference 1
- Resolution
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Source-reported events for the cited work
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Observation 46096209-e9cd-482a-8745-83417ccf525d · outbound
This paper cites Understanding and Overcoming the Challenges of Efficient Transformer Quantization.
Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness Understanding and Overcoming the Challenges of Efficient Transformer Quantization
Reference 2
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 0255ca02-8f77-4275-b247-daf081271c35 · outbound
This paper cites Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality, march 2023.
Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality, march 2023
Reference 3
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 6e8fbcc4-ee55-4852-9ccd-3f435a5cd2a4 · outbound
This paper cites A comparative survey of instance selection methods applied to non-neural and transformer-based text classification.
Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness A comparative survey of instance selection methods applied to non-neural and transformer-based text classification
Reference 4
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation bcfe193e-0b5d-4b9d-81e7-5ce229546182 · outbound
This paper cites Latent-Variable Generative Models for Data-Efficient Text Classification.
Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness Latent-Variable Generative Models for Data-Efficient Text Classification
Reference 5
- Resolution
- verified exact
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 388b0c98-b6e7-46b0-8b5a-7f8ea005b5f4 · outbound
This paper cites Matrix completion via memoryless scalar quantization.
Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness Matrix completion via memoryless scalar quantization
Reference 6
- Resolution
- verified exact
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 8b6f505e-be6e-4521-be51-583d6161aa85 · outbound
This paper cites Learned Step Size Quantization.
Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness Learned Step Size Quantization
Reference 7
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 625a0bd6-d97e-48ee-ab1c-f513a4570235 · outbound
This paper cites Xilinx/brevitas.
Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness Xilinx/brevitas
Reference 8
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a291fdc2-735c-4b5b-b9ba-09f0e437f211 · outbound
This paper cites The elements of statistical learning: data mining, inference, and prediction.
Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness The elements of statistical learning: data mining, inference, and prediction
Reference 9
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 38eab526-e852-40c8-a084-27db2344c68a · outbound
This paper cites Long short-term memory.
Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness Long short-term memory
Reference 10
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation bb0750e7-3af5-47f0-aed8-1504af057dce · outbound
This paper cites spacy: Industrial-strength natural language processing in python.
Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness spacy: Industrial-strength natural language processing in python
Reference 11
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation b9b26f11-88d9-43df-b9dd-b93422b560d2 · outbound
This paper cites Fastai: A layered api for deep learning.
Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness Fastai: A layered api for deep learning
Reference 12
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T17:55:15.470431Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 944ed20b-e9b6-467c-9992-3e43d4af3857 · outbound
This paper cites Empirical Evaluation of Post-Training Quantization Methods for Language Tasks.
Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness Empirical Evaluation of Post-Training Quantization Methods for Language Tasks
Reference 13
- Resolution
- verified exact
- local_arxiv, observed 2026-08-06T17:55:14.945323Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation ce41274b-dc62-48e1-bffc-21015ffebe91 · outbound
This paper cites Accurate post training quantization with small calibration sets, in: International Conference on Machine Learning, PMLR.
Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness Accurate post training quantization with small calibration sets, in: International Conference on Machine Learning, PMLR
Reference 14
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation fa0b2d82-b89c-42b6-83e7-93970c07896e · outbound
This paper cites Speech and Language Processing.
Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness Speech and Language Processing
Reference 15
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation eaebde46-d846-4bff-8456-6d011fbed0dd · outbound
This paper cites Sulla determinazione empirica di una legge di distribuzione.
Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness Sulla determinazione empirica di una legge di distribuzione
Reference 16
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 6410144a-5d72-4ea3-ab49-482e6011dcdd · outbound
This paper cites Generative models improve fairness of medical classifiers under distribution shifts.
Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness Generative models improve fairness of medical classifiers under distribution shifts
Reference 17
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T17:55:15.395699Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation ca045df6-9332-4a61-aca0-78a1c7cd720b · outbound
This paper cites Precision and recall metrics for generative models, in: Advances in Neural Information Processing Systems.
Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness Precision and recall metrics for generative models, in: Advances in Neural Information Processing Systems
Reference 18
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T17:55:15.379315Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 7a354b85-64b5-4654-a7a1-477b9bacf587 · outbound
This paper cites Robust inference via generative classifiers for handling noisy labels, in: Proceedings of the 36th International Conference on Machine Learning (ICML).
Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness Robust inference via generative classifiers for handling noisy labels, in: Proceedings of the 36th International Conference on Machine Learning (ICML)
Reference 19
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 2ffe9e50-70a2-4887-8de8-2489562af9c1 · outbound
This paper cites FP8-BERT: Post-Training Quantization for Transformer.
Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness FP8-BERT: Post-Training Quantization for Transformer
Reference 20
- Resolution
- verified exact
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 331c618e-2ccc-4c6e-bb5e-71621d0386a4 · outbound
This paper cites On the Impact of Calibration Data in Post-training Quantization and Pruning.
Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness On the Impact of Calibration Data in Post-training Quantization and Pruning
Reference 21
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d38901f7-4e0e-42c7-80da-6369142d105e · outbound
This paper cites Are Generative Classifiers More Robust to Adversarial Attacks?.
Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness Are Generative Classifiers More Robust to Adversarial Attacks?
Reference 22
- Resolution
- verified exact
- local_arxiv, observed 2026-08-06T17:55:14.873141Z
Source-reported events for the cited work
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Observation b6ce0028-eb07-478c-a59e-f2fe7fb8fdfb · outbound
This paper cites SpinQuant: LLM quantization with learned rotations.
Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness SpinQuant: LLM quantization with learned rotations
Reference 23
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 58f2f187-b49a-4e4f-9033-08cb895cbc1a · outbound
This paper cites A Greedy Algorithm for Quantizing Neural Networks.
Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness A Greedy Algorithm for Quantizing Neural Networks
Reference 24
- Resolution
- verified exact
- local_arxiv, observed 2026-08-06T17:55:14.821188Z
Source-reported events for the cited work
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Observation 888c3692-9d08-4969-941d-4033b743e314 · outbound
This paper cites Efficient Estimation of Word Representations in Vector Space.
Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness Efficient Estimation of Word Representations in Vector Space
Reference 25
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e7f21b11-fa44-4318-a874-3ac1e9ad4786 · outbound
This paper cites Up or Down? Adaptive Rounding for Post-Training Quantization.
Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness Up or Down? Adaptive Rounding for Post-Training Quantization
Reference 26
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 51bec0e9-5646-46f7-ae5e-8a4ce9a923ee · outbound
This paper cites Data-free quantization through weight equalization and bias correction, in: Proceedings of the IEEE/CVF international conference on computer vision, pp.
Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness Data-free quantization through weight equalization and bias correction, in: Proceedings of the IEEE/CVF international conference on computer vision, pp
Reference 27
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 1df8a0b4-125a-40a9-8872-53a59e06fd3f · outbound
This paper cites A White Paper on Neural Network Quantization.
Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness A White Paper on Neural Network Quantization
Reference 28
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 53311228-94fd-4d30-8973-7ffa0c13a32d · outbound
This paper cites On discriminative vs.
Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness On discriminative vs
Reference 29
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 5ac9ae95-12ee-44e8-a135-85d2d06355b8 · outbound
This paper cites Deep neural networks are easily fooled: High confidence predictions for unrecognizable images, in: Proceedings of the IEEE conference on computer vision and pattern recognition, pp.
Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness Deep neural networks are easily fooled: High confidence predictions for unrecognizable images, in: Proceedings of the IEEE conference on computer vision and pattern recognition, pp
Reference 30
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 1bd34eb8-51bb-479d-b7c9-c818fa0daba7 · outbound
This paper cites On estimation of a probability density function and mode.
Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness On estimation of a probability density function and mode
Reference 31
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 3eba2354-6d5f-4372-bb35-fe3a4b70b722 · outbound
This paper cites PyTorch: An Imperative Style, High-Performance Deep Learning Library.
Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness PyTorch: An Imperative Style, High-Performance Deep Learning Library
Reference 32
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation fb1dfa66-6d12-456a-8cb1-281e3a527ff2 · outbound
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Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness GloVe: Global vectors for word representation, in: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp
Reference 33
- Resolution
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Source-reported events for the cited work
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Observation 4012d018-999b-4872-ad4e-fe2417d4f999 · outbound
This paper cites Remarks on some nonparametric estimates of a density function.
Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness Remarks on some nonparametric estimates of a density function
Reference 34
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation aab68413-fef3-4c73-96f5-56883b051a8a · outbound
This paper cites Generative and Discriminative Deep Belief Network Classifiers: Comparisons Under an Approximate Computing Framework.
Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness Generative and Discriminative Deep Belief Network Classifiers: Comparisons Under an Approximate Computing Framework
Reference 35
- Resolution
- verified exact
- local_arxiv, observed 2026-08-06T17:55:14.709095Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 26ff210c-0eb0-4f2e-83dc-f86f1e631e8e · outbound
This paper cites Table for estimating the goodness of fit of empirical distributions.
Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness Table for estimating the goodness of fit of empirical distributions
Reference 36
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T17:55:15.235433Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation e9a0806a-a046-4045-80f8-abb9167ac1d3 · outbound
This paper cites How to fine-tune bert for text classification?, in: China national conference on Chinese computational linguistics, Springer.
Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness How to fine-tune bert for text classification?, in: China national conference on Chinese computational linguistics, Springer
Reference 37
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T17:55:15.214341Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 56c56114-15a7-42f8-b5e8-a952b638f3fa · outbound
This paper cites LLaMA: Open and Efficient Foundation Language Models.
Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness LLaMA: Open and Efficient Foundation Language Models
Reference 38
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d89a0034-70f8-4d1f-8497-00f5f2f960a0 · outbound
This paper cites Attention is all you need.
Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness Attention is all you need
Reference 39
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 6d50a9ab-b5c7-4eec-9397-f522db0099f2 · outbound
This paper cites HAQ: Hardware-Aware Automated Quantization with Mixed Precision.
Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness HAQ: Hardware-Aware Automated Quantization with Mixed Precision
Reference 40
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d988fac6-9c14-4c82-a9b0-116d49c1975b · outbound
This paper cites The effect of class imbalance on precision-recall curves.
Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness The effect of class imbalance on precision-recall curves
Reference 41
- Resolution
- verified exact
- doi, observed 2026-08-06T17:55:14.428592Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 4c971d1f-ba83-4554-989a-9741f7b3e1e0 · outbound
This paper cites Easyquant: Post-training quantization via scale optimization, in: CVPR.
Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness Easyquant: Post-training quantization via scale optimization, in: CVPR
Reference 42
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T17:55:15.185672Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 7d7a5f1f-1ea8-4185-b279-34961ffdd2df · outbound
This paper cites Zeroquant: Efficient and affordable post-training quantization for large-scale transformers.
Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness Zeroquant: Efficient and affordable post-training quantization for large-scale transformers
Reference 43
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T17:55:15.168392Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation fc384a65-2736-4c6c-bff6-f9b673ffe72b · outbound
This paper cites Generative and Discriminative Text Classification with Recurrent Neural Networks.
Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness Generative and Discriminative Text Classification with Recurrent Neural Networks
Reference 44
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation c7421fcd-d3c3-4af6-98a6-b56cf838a902 · outbound
This paper cites Q8BERT: Quantized 8Bit BERT.
Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness Q8BERT: Quantized 8Bit BERT
Reference 45
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 4f43806d-ef24-4357-8f44-d640d3134318 · outbound
This paper cites OPT: Open Pre-trained Transformer Language Models.
Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness OPT: Open Pre-trained Transformer Language Models
Reference 46
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T17:55:14.358367Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a4b6d98c-86d9-4452-920a-f67f158dc280 · outbound
This paper cites Qronos: Correcting the past by shaping the future.
Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness Qronos: Correcting the past by shaping the future
Reference 47
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T17:55:14.363617Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 16c77153-0086-4c44-b6f1-e7511591b5d9 · outbound
This paper cites Learning low-precision structured subnetworks using joint layerwise channel pruning and uniform quantization.
Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness Learning low-precision structured subnetworks using joint layerwise channel pruning and uniform quantization
Reference 48
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T17:55:15.147570Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation c365e039-dd17-4a76-91d0-be22d1e99236 · outbound
This paper cites Character-level Convolutional Networks for Text Classification.
Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness Character-level Convolutional Networks for Text Classification
Reference 49
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T17:55:14.373213Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 540f0ca6-d3bf-4e4e-b506-3273cf4992de · outbound
This paper cites A Sensitivity Analysis of (and Practitioners' Guide to) Convolutional Neural Networks for Sentence Classification.
Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness A Sensitivity Analysis of (and Practitioners' Guide to) Convolutional Neural Networks for Sentence Classification
Reference 50
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T17:55:14.379302Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 79a82d83-15b4-4926-beb7-16ffa80e55a5 · outbound
This paper cites Selectq: Calibration data selection for post-training quantization.
Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness Selectq: Calibration data selection for post-training quantization
Reference 51
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T17:55:15.126581Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Pith citing papers
No inbound Pith citation observations are available.