Paper Citation Record · LEDGER

Ternary Weight Networks

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 22 inbound Pith citation observations for arXiv:1605.04711.

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

pith.paper-citation-record.v1
1605.04711 v3

Coverage vector

measured 0 of 0 reference resolution

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measured 22 of 22 standing notices

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Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 22 of 22 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T12:00:53.833004Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T13:19:50.534492Z

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Outbound references

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Pith citing papers

Observation e79354ed-e6a9-43f4-9fa3-4a5c2549b874 · inbound

Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation cites this paper.

Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation Ternary Weight Networks

Reference 27

Resolution: verified exact; arxiv_id, observed 2026-05-12T15:21:28.922526Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 997c3b96-bfaa-411d-bdcf-90fc31b2fa67 · inbound

Weight Normalization based Quantization for Deep Neural Network Compression cites this paper.

Weight Normalization based Quantization for Deep Neural Network Compression Ternary Weight Networks

Reference 20

Resolution: verified exact; arxiv_id, observed 2026-05-25T11:45:45.056664Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation fbc0a9f7-dc55-4deb-b08c-36662cae542a · inbound

Learning Multimodal Fixed-Point Weights using Gradient Descent cites this paper.

Learning Multimodal Fixed-Point Weights using Gradient Descent Ternary Weight Networks

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-05-24T20:54:54.723909Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation da936086-d311-4845-81c0-cedf217746c0 · inbound

Forget the Data and Fine-Tuning! Just Fold the Network to Compress cites this paper.

Forget the Data and Fine-Tuning! Just Fold the Network to Compress Ternary Weight Networks

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-07T19:04:45.624168Z

Source-reported events for the cited work

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Observation f4f21833-47cd-41d5-816f-a411ca08ccc7 · inbound

Optimizing Binary and Ternary Neural Network Inference on RRAM Crossbars using CIM-Explorer cites this paper.

Optimizing Binary and Ternary Neural Network Inference on RRAM Crossbars using CIM-Explorer Ternary Weight Networks

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-07T15:43:04.335248Z

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Observation 6da3d452-503b-4e7f-b8ab-a0e78515e32c · inbound

DFQ-ViT: Data-Free Quantization for Vision Transformers without Fine-tuning cites this paper.

DFQ-ViT: Data-Free Quantization for Vision Transformers without Fine-tuning Ternary Weight Networks

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-06T16:09:08.001032Z

Source-reported events for the cited work

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Observation dffe1159-20b7-4089-bdc3-aae1db301e08 · inbound

A Lower Bound for the Number of Linear Regions of Ternary ReLU Regression Neural Networks cites this paper.

A Lower Bound for the Number of Linear Regions of Ternary ReLU Regression Neural Networks Ternary Weight Networks

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-05-19T03:32:01.758364Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation bdba1c5e-aef7-421a-94e0-e4d71cfe5e8a · inbound

Latent-Space Mean-Field Theory for Deep BitNet-like Training: Constrained Gradient Flows with Smooth Quantization and STE Limits cites this paper.

Latent-Space Mean-Field Theory for Deep BitNet-like Training: Constrained Gradient Flows with Smooth Quantization and STE Limits Ternary Weight Networks

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-05T14:11:15.494158Z

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Observation e5c95b08-1749-4acc-bfa5-2143be40776e · inbound

Quantization Impact on the Accuracy and Communication Efficiency Trade-off in Federated Learning for Aerospace Predictive Maintenance cites this paper.

Quantization Impact on the Accuracy and Communication Efficiency Trade-off in Federated Learning for Aerospace Predictive Maintenance Ternary Weight Networks

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-05-11T05:26:02.217795Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 13e41782-e43c-4066-bfa2-eefc3f3e6606 · inbound

FairyFuse: Multiplication-Free LLM Inference on CPUs via Fused Ternary Kernels cites this paper.

FairyFuse: Multiplication-Free LLM Inference on CPUs via Fused Ternary Kernels Ternary Weight Networks

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-05-11T13:41:04.747151Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation e046ab5d-ad40-4c73-bc29-06a9860ce64d · inbound

Multibit neural inference in a N-ary crossbar architecture cites this paper.

Multibit neural inference in a N-ary crossbar architecture Ternary Weight Networks

Reference 17

Resolution: verified exact; arxiv_id, observed 2026-05-12T00:46:12.485949Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 46cf677f-1c54-4926-ace0-939c720914c9 · inbound

Multibit neural inference in a N-ary crossbar architecture cites this paper.

Multibit neural inference in a N-ary crossbar architecture Ternary Weight Networks

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-02T15:26:25.793477Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 67ef8a8e-77e7-4314-b6ff-cd84c64ca606 · inbound

SURGE: Surrogate Gradient Adaptation in Binary Neural Networks cites this paper.

SURGE: Surrogate Gradient Adaptation in Binary Neural Networks Ternary Weight Networks

Reference 96

Resolution: metadata mismatch; arxiv_id, observed 2026-05-13T06:37:26.559659Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 3ff40d1a-c71b-4819-af76-9dde00bd0541 · inbound

SURGE: Surrogate Gradient Adaptation in Binary Neural Networks cites this paper.

SURGE: Surrogate Gradient Adaptation in Binary Neural Networks Ternary Weight Networks

Reference 96

Resolution: metadata mismatch; arxiv_id, observed 2026-05-19T17:52:42.899012Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation ef11572e-34eb-4f00-9be3-1098a99f116a · inbound

EMO-BOOST: Emotion-Augmented Audio-Visual Features for Improved Generalization in Deepfake Detection cites this paper.

EMO-BOOST: Emotion-Augmented Audio-Visual Features for Improved Generalization in Deepfake Detection Ternary Weight Networks

Reference 20

Resolution: verified exact; arxiv_id, observed 2026-05-20T05:48:04.334050Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation b9b09f42-7bdb-441f-8896-3abca9d65e9d · inbound

FTerViT: Fully Ternary Vision Transformer cites this paper.

FTerViT: Fully Ternary Vision Transformer Ternary Weight Networks

Reference 28

Resolution: verified exact; arxiv_id, observed 2026-05-21T06:03:59.472187Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 6eb432b6-0f07-4425-a3bb-56cf0e52f4e9 · inbound

A Reconfigurable Computing In-Memory Macro with Charge-sharing-based Weighted Accumulator cites this paper.

A Reconfigurable Computing In-Memory Macro with Charge-sharing-based Weighted Accumulator Ternary Weight Networks

Reference 41

Resolution: verified exact; arxiv_id, observed 2026-06-28T20:52:37.885224Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation f599e4a6-36a0-458d-9c8f-8073527078ef · inbound

TWLA: Achieving Ternary Weights and Low-Bit Activations for LLMs via Post-Training Quantization cites this paper.

TWLA: Achieving Ternary Weights and Low-Bit Activations for LLMs via Post-Training Quantization Ternary Weight Networks

Reference 13

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T13:38:19.687353Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation fe4ff7cd-ee66-4af0-a606-50be94046415 · inbound

On the Expressive Power of Weight Quantization in Large Language Models cites this paper.

On the Expressive Power of Weight Quantization in Large Language Models Ternary Weight Networks

Reference 24

Resolution: verified exact; arxiv_id, observed 2026-07-04T08:19:44.233326Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 44e375dc-b890-49ff-aa30-80c2d13034da · inbound

CAT-Q: Cost-efficient and Accurate Ternary Quantization for LLMs cites this paper.

CAT-Q: Cost-efficient and Accurate Ternary Quantization for LLMs Ternary Weight Networks

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-07-04T13:19:50.536501Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation b40a251a-68f6-405d-93de-bcaf3ad2a5ea · inbound

ExTernD: Expanded-Rank Ternary Decomposition Ternary LLM PTQ with Accuracy Approaching Any Quantization Level cites this paper.

ExTernD: Expanded-Rank Ternary Decomposition Ternary LLM PTQ with Accuracy Approaching Any Quantization Level Ternary Weight Networks

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-02T05:02:32.547154Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b10a39b5-722c-433f-a0c8-b031381073d6 · inbound

APQF: Agentic Profiling-Guided Structured Pruning and Mixed-Precision Quantization with Adaptive Fine-Tuning cites this paper.

APQF: Agentic Profiling-Guided Structured Pruning and Mixed-Precision Quantization with Adaptive Fine-Tuning Ternary Weight Networks

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-08T12:00:53.833004Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.