Paper Citation Record · LEDGER

BEEM: Boosting Performance of Early Exit DNNs using Multi-Exit Classifiers as Experts

As of 10 August 2026, this Paper Citation Record lists 33 of 33 outbound references and 0 inbound Pith citation observations for arXiv:2502.00745.

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

pith.paper-citation-record.v1
2502.00745 v1

Coverage vector

measured 33 of 33 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-09T17:57:25.860723Z

measured 33 of 33 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

33 of 33 outbound references displayed

verified exact5
verified fuzzy10
unresolved18
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation c19945f3-c170-4a08-bd3e-bf37d7014e74 · outbound

This paper cites BinaryBERT: Pushing the Limit of BERT Quantization.

BEEM: Boosting Performance of Early Exit DNNs using Multi-Exit Classifiers as Experts BinaryBERT: Pushing the Limit of BERT Quantization

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-09T17:57:25.705245Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 815d24bb-bab9-4ff3-8f72-5d99b1b43c58 · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

BEEM: Boosting Performance of Early Exit DNNs using Multi-Exit Classifiers as Experts BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-09T17:57:25.730545Z

Source-reported events for the cited work

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Observation e9b19f68-5d25-49f6-a169-3cd83a1c6d67 · outbound

This paper cites Reducing Transformer Depth on Demand with Structured Dropout.

BEEM: Boosting Performance of Early Exit DNNs using Multi-Exit Classifiers as Experts Reducing Transformer Depth on Demand with Structured Dropout

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-09T17:57:25.735528Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 81f8680b-dd7c-41c0-b7ec-7f9e4817c9d6 · outbound

This paper cites RomeBERT: Robust Training of Multi-Exit BERT.

BEEM: Boosting Performance of Early Exit DNNs using Multi-Exit Classifiers as Experts RomeBERT: Robust Training of Multi-Exit BERT

Reference 9

Resolution: verified exact; local_arxiv, observed 2026-08-09T17:57:26.140681Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 36675b5f-c8a7-4161-957f-7b814341f069 · outbound

This paper cites TinyBERT: Distilling BERT for Natural Language Understanding.

BEEM: Boosting Performance of Early Exit DNNs using Multi-Exit Classifiers as Experts TinyBERT: Distilling BERT for Natural Language Understanding

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-09T17:57:25.760709Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 34fa7dc1-6eba-4684-9b81-1f88003c90e7 · outbound

This paper cites Low cost early exit decision unit design for cnn accelerator.

BEEM: Boosting Performance of Early Exit DNNs using Multi-Exit Classifiers as Experts Low cost early exit decision unit design for cnn accelerator

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:57:26.387768Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation d07ecede-5fb8-4aff-bd74-3bd4dbbe1bb5 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

BEEM: Boosting Performance of Early Exit DNNs using Multi-Exit Classifiers as Experts Adam: A Method for Stochastic Optimization

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-09T17:57:25.770642Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 39b9da16-f0fa-4ccd-89a5-f09c3295d7f9 · outbound

This paper cites Blip: Bootstrapping language-image pre- training for unified vision-language understanding and generation.

BEEM: Boosting Performance of Early Exit DNNs using Multi-Exit Classifiers as Experts Blip: Bootstrapping language-image pre- training for unified vision-language understanding and generation

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:57:26.372276Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation eb36cacd-d4d4-417e-a597-46904bd7ecc2 · outbound

This paper cites Microsoft coco: Common objects in context.

BEEM: Boosting Performance of Early Exit DNNs using Multi-Exit Classifiers as Experts Microsoft coco: Common objects in context

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-09T17:57:25.780229Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 69e111f4-b6da-476b-9f03-8dadc841542b · outbound

This paper cites Towards Efficient NLP: A Standard Evaluation and A Strong Baseline.

BEEM: Boosting Performance of Early Exit DNNs using Multi-Exit Classifiers as Experts Towards Efficient NLP: A Standard Evaluation and A Strong Baseline

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-09T17:57:25.789873Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7ee251dd-54c5-4ab2-9789-79882dbea912 · outbound

This paper cites Calibration-aided edge inference offloading via adaptive model partitioning of deep neural networks.

BEEM: Boosting Performance of Early Exit DNNs using Multi-Exit Classifiers as Experts Calibration-aided edge inference offloading via adaptive model partitioning of deep neural networks

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:57:26.345958Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation dfaa2317-d54b-4a51-bb8c-0057ef44c292 · outbound

This paper cites Jointly-Learned Exit and Inference for a Dynamic Neural Network : JEI-DNN.

BEEM: Boosting Performance of Early Exit DNNs using Multi-Exit Classifiers as Experts Jointly-Learned Exit and Inference for a Dynamic Neural Network : JEI-DNN

Reference 20

Resolution: verified exact; local_arxiv, observed 2026-08-09T17:57:26.039697Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation a112d15c-db21-4062-9329-1ce313c48b77 · outbound

This paper cites DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter.

BEEM: Boosting Performance of Early Exit DNNs using Multi-Exit Classifiers as Experts DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-09T17:57:25.804656Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 85984723-a515-477d-91a4-c8b9c15459b6 · outbound

This paper cites The Right Tool for the Job: Matching Model and Instance Complexities.

BEEM: Boosting Performance of Early Exit DNNs using Multi-Exit Classifiers as Experts The Right Tool for the Job: Matching Model and Instance Complexities

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-09T17:57:25.809524Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 53667ac5-4a18-447c-846b-fd3c0643f1c1 · outbound

This paper cites Patient Knowledge Distillation for BERT Model Compression.

BEEM: Boosting Performance of Early Exit DNNs using Multi-Exit Classifiers as Experts Patient Knowledge Distillation for BERT Model Compression

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-09T17:57:25.814259Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b0fe9732-1402-479d-8c1e-6c8534d59e27 · outbound

This paper cites Early Exiting with Ensemble Internal Classifiers.

BEEM: Boosting Performance of Early Exit DNNs using Multi-Exit Classifiers as Experts Early Exiting with Ensemble Internal Classifiers

Reference 24

Resolution: verified exact; local_arxiv, observed 2026-08-09T17:57:25.968860Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 83f5f883-648d-4782-8b5c-5f82a18b3b3f · outbound

This paper cites A Simple Hash-Based Early Exiting Approach For Language Understanding and Generation.

BEEM: Boosting Performance of Early Exit DNNs using Multi-Exit Classifiers as Experts A Simple Hash-Based Early Exiting Approach For Language Understanding and Generation

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-09T17:57:25.823896Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7c25490e-2b00-46d6-a35d-f61b70679dca · outbound

This paper cites You need multiple exiting: Dynamic early exiting for accelerating unified vision language model.

BEEM: Boosting Performance of Early Exit DNNs using Multi-Exit Classifiers as Experts You need multiple exiting: Dynamic early exiting for accelerating unified vision language model

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:57:26.328745Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation de5fc520-c4ef-431e-a2ab-50397a31056d · outbound

This paper cites Branchynet: Fast inference via early exiting from deep neural networks.

BEEM: Boosting Performance of Early Exit DNNs using Multi-Exit Classifiers as Experts Branchynet: Fast inference via early exiting from deep neural networks

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:57:26.313173Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 10c0ec90-5f5e-44bc-a123-54a6a7c29592 · outbound

This paper cites DeeBERT: Dynamic Early Exiting for Accelerating BERT Inference.

BEEM: Boosting Performance of Early Exit DNNs using Multi-Exit Classifiers as Experts DeeBERT: Dynamic Early Exiting for Accelerating BERT Inference

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-09T17:57:25.837531Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 848d4f10-71e4-43ad-9356-8e9a8a5e6935 · outbound

This paper cites TernaryBERT: Distillation-aware Ultra-low Bit BERT.

BEEM: Boosting Performance of Early Exit DNNs using Multi-Exit Classifiers as Experts TernaryBERT: Distillation-aware Ultra-low Bit BERT

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-09T17:57:25.842227Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0d3d402a-fac2-4289-95ff-82da93155662 · outbound

This paper cites Pcee-bert: Accel- erating bert inference via patient and confident early exiting.

BEEM: Boosting Performance of Early Exit DNNs using Multi-Exit Classifiers as Experts Pcee-bert: Accel- erating bert inference via patient and confident early exiting

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:57:26.297816Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation b0e96430-915b-4144-8e19-fff037dca3a5 · outbound

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

BEEM: Boosting Performance of Early Exit DNNs using Multi-Exit Classifiers as Experts To prune, or not to prune: exploring the efficacy of pruning for model compression

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-09T17:57:25.851352Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 43bbf726-7631-4c8c-b22c-1ac1840f63f1 · outbound

This paper cites For simplicity, we prove it for the binary classification case.

BEEM: Boosting Performance of Early Exit DNNs using Multi-Exit Classifiers as Experts For simplicity, we prove it for the binary classification case

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:57:26.282176Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation c5daa3b5-7323-4ff2-a542-c73c60eae22c · outbound

This paper cites an unresolved cited work.

BEEM: Boosting Performance of Early Exit DNNs using Multi-Exit Classifiers as Experts Unresolved cited work

Reference 33

Resolution: unresolved; raw_fallback, observed 2026-08-09T17:57:26.266459Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 5c1225dc-7cd3-474b-a999-2154ac31a53f · outbound

This paper cites FastBERT: a Self-distilling BERT with Adaptive Inference Time.

BEEM: Boosting Performance of Early Exit DNNs using Multi-Exit Classifiers as Experts FastBERT: a Self-distilling BERT with Adaptive Inference Time

Reference 2014

Resolution: unresolved; no resolver link, observed 2026-08-09T17:57:25.784962Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e8d990ab-617e-4b47-8750-8ccad488748b · outbound

This paper cites Early exit with disentangled representation and equiangular tight frame.

BEEM: Boosting Performance of Early Exit DNNs using Multi-Exit Classifiers as Experts Early exit with disentangled representation and equiangular tight frame

Reference 2017

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:57:26.403224Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 2f86425d-5086-4271-b553-e8e6a986a774 · outbound

This paper cites Flexdnn: Input-adaptive on-device deep learning for efficient mobile vision.

BEEM: Boosting Performance of Early Exit DNNs using Multi-Exit Classifiers as Experts Flexdnn: Input-adaptive on-device deep learning for efficient mobile vision

Reference 2019

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:57:26.418374Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 747fad03-0b3f-4f75-b4e4-4c062c9f452b · outbound

This paper cites SplitEE: Early Exit in Deep Neural Networks with Split Computing.

BEEM: Boosting Performance of Early Exit DNNs using Multi-Exit Classifiers as Experts SplitEE: Early Exit in Deep Neural Networks with Split Computing

Reference 2020

Resolution: verified exact; local_arxiv, observed 2026-08-09T17:57:26.234347Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation f4eb3635-d368-4c20-b227-bfd362c870b2 · outbound

This paper cites Multi-Scale Dense Networks for Resource Efficient Image Classification.

BEEM: Boosting Performance of Early Exit DNNs using Multi-Exit Classifiers as Experts Multi-Scale Dense Networks for Resource Efficient Image Classification

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-09T17:57:25.750139Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f68f6f9c-a06c-4c7a-a04d-23a20c29d99d · outbound

This paper cites PonderNet: Learning to Ponder.

BEEM: Boosting Performance of Early Exit DNNs using Multi-Exit Classifiers as Experts PonderNet: Learning to Ponder

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-09T17:57:25.725709Z

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Unavailable: canonical work link unavailable.

Observation 1bd5dc8a-5c06-4935-9eac-35e58bd6a124 · outbound

This paper cites CAPEEN: Image Captioning with Early Exits and Knowledge Distillation.

BEEM: Boosting Performance of Early Exit DNNs using Multi-Exit Classifiers as Experts CAPEEN: Image Captioning with Early Exits and Knowledge Distillation

Reference 2023

Resolution: verified exact; local_arxiv, observed 2026-08-09T17:57:26.211736Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 6ed14b9b-1c21-41dc-b6a6-deed74cf1a9b · outbound

This paper cites Palbert: Teaching albert to ponder.

BEEM: Boosting Performance of Early Exit DNNs using Multi-Exit Classifiers as Experts Palbert: Teaching albert to ponder

Reference 2024

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:57:26.434133Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Pith citing papers

No inbound Pith citation observations are available.