Paper Citation Record · LEDGER

Learning from Loss Landscape: Generalizable Mixed-Precision Quantization via Adaptive Sharpness-Aware Gradient Aligning

As of 21 August 2026, this Paper Citation Record lists 20 of 20 outbound references and 2 inbound Pith citation observations for arXiv:2505.04877.

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

pith.paper-citation-record.v1
2505.04877 v1

Coverage vector

measured 20 of 20 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T23:25:30.186633Z

measured 22 of 22 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-14T04:38:43.942401Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T16:59:58.450016Z

Reference resolution

20 of 20 outbound references displayed

verified exact0
verified fuzzy5
unresolved15
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 0a1cd589-c158-452d-9e52-169edae8b995 · outbound

This paper cites Food-101– mining discriminative components with random forests.

Learning from Loss Landscape: Generalizable Mixed-Precision Quantization via Adaptive Sharpness-Aware Gradient Aligning Food-101– mining discriminative components with random forests

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-15T23:25:30.093506Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 34e2ed55-b545-479c-93d6-ad2603aa3c85 · outbound

This paper cites Single path one-shot neural architecture search with uniform sampling.

Learning from Loss Landscape: Generalizable Mixed-Precision Quantization via Adaptive Sharpness-Aware Gradient Aligning Single path one-shot neural architecture search with uniform sampling

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-15T23:25:30.118168Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c43b80b5-ebbc-4078-b9e2-bc8cd073fcff · outbound

This paper cites V ., Jennings, R.

Learning from Loss Landscape: Generalizable Mixed-Precision Quantization via Adaptive Sharpness-Aware Gradient Aligning V ., Jennings, R

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:25:30.470377Z

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 467484c6-f878-46e7-843e-72a7a505d889 · outbound

This paper cites Simplified pac-bayesian margin bounds.

Learning from Loss Landscape: Generalizable Mixed-Precision Quantization via Adaptive Sharpness-Aware Gradient Aligning Simplified pac-bayesian margin bounds

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:25:30.454547Z

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 9ad7c615-c3c3-463e-bcc5-0877934bf831 · outbound

This paper cites Mixed Precision DNNs: All you need is a good parametrization.

Learning from Loss Landscape: Generalizable Mixed-Precision Quantization via Adaptive Sharpness-Aware Gradient Aligning Mixed Precision DNNs: All you need is a good parametrization

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-15T23:25:30.152432Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c42b3453-f93a-4c35-a3d8-ad486b6da0be · outbound

This paper cites Mixed Precision Quantization of ConvNets via Differentiable Neural Architecture Search.

Learning from Loss Landscape: Generalizable Mixed-Precision Quantization via Adaptive Sharpness-Aware Gradient Aligning Mixed Precision Quantization of ConvNets via Differentiable Neural Architecture Search

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-15T23:25:30.157791Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f68e3b0d-a77d-4574-bd55-86cd6fc0ebe0 · outbound

This paper cites Search what you want: Barrier panelty nas for mixed precision quantization.

Learning from Loss Landscape: Generalizable Mixed-Precision Quantization via Adaptive Sharpness-Aware Gradient Aligning Search what you want: Barrier panelty nas for mixed precision quantization

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:25:30.428887Z

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 35aad018-5cbe-41e1-89cd-d7dae2ef0216 · outbound

This paper cites Surrogate Gap Minimization Improves Sharpness-Aware Training.

Learning from Loss Landscape: Generalizable Mixed-Precision Quantization via Adaptive Sharpness-Aware Gradient Aligning Surrogate Gap Minimization Improves Sharpness-Aware Training

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-15T23:25:30.172502Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0b0a3854-7a53-4e46-949f-38b3534a039f · outbound

This paper cites an unresolved cited work.

Learning from Loss Landscape: Generalizable Mixed-Precision Quantization via Adaptive Sharpness-Aware Gradient Aligning Unresolved cited work

Reference 18

Resolution: unresolved; raw_fallback, observed 2026-08-15T23:25:30.413181Z

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 e56d02e5-1600-466f-8e86-9561182fe9a2 · outbound

This paper cites This shows that ASGA effectively reduces the upper bound of the generalization error during the MPQ process.

Learning from Loss Landscape: Generalizable Mixed-Precision Quantization via Adaptive Sharpness-Aware Gradient Aligning This shows that ASGA effectively reduces the upper bound of the generalization error during the MPQ process

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:25:30.397694Z

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 b49eba51-1ed6-4089-b224-c5b83bbab535 · outbound

This paper cites 14 Learning from Loss Landscape: Generalizable Mixed-Precision Quantization via Adaptive Sharpness-Aware Gradient Aligning Details of models.

Learning from Loss Landscape: Generalizable Mixed-Precision Quantization via Adaptive Sharpness-Aware Gradient Aligning 14 Learning from Loss Landscape: Generalizable Mixed-Precision Quantization via Adaptive Sharpness-Aware Gradient Aligning Details of models

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:25:30.382492Z

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 3d1c0207-8807-4573-807d-ef7f542fbb62 · outbound

This paper cites and Zisserman, A.

Learning from Loss Landscape: Generalizable Mixed-Precision Quantization via Adaptive Sharpness-Aware Gradient Aligning and Zisserman, A

Reference 2003

Resolution: unresolved; no resolver link, observed 2026-08-15T23:25:30.141692Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5bff0267-8237-48ee-b53d-2267c762ad18 · outbound

This paper cites Sharpness-Aware Minimization for Efficiently Improving Generalization.

Learning from Loss Landscape: Generalizable Mixed-Precision Quantization via Adaptive Sharpness-Aware Gradient Aligning Sharpness-Aware Minimization for Efficiently Improving Generalization

Reference 2010

Resolution: unresolved; no resolver link, observed 2026-08-15T23:25:30.113568Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1cbee745-a14f-4b9d-8f06-698807ca2d60 · outbound

This paper cites An Adaptive Policy to Employ Sharpness-Aware Minimization.

Learning from Loss Landscape: Generalizable Mixed-Precision Quantization via Adaptive Sharpness-Aware Gradient Aligning An Adaptive Policy to Employ Sharpness-Aware Minimization

Reference 2016

Resolution: unresolved; no resolver link, observed 2026-08-15T23:25:30.127668Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 76fb5c4c-ebfa-4c9c-a1b4-e9539adeb18c · outbound

This paper cites Imagenet: A large-scale hierarchical image database.

Learning from Loss Landscape: Generalizable Mixed-Precision Quantization via Adaptive Sharpness-Aware Gradient Aligning Imagenet: A large-scale hierarchical image database

Reference 2018

Resolution: unresolved; no resolver link, observed 2026-08-15T23:25:30.104116Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6f016767-6db1-44a6-b060-7655cb905541 · outbound

This paper cites Learned Step Size Quantization.

Learning from Loss Landscape: Generalizable Mixed-Precision Quantization via Adaptive Sharpness-Aware Gradient Aligning Learned Step Size Quantization

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-15T23:25:30.108649Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8456f338-05fa-4043-b994-88fa7db67f01 · outbound

This paper cites PACT: Parameterized Clipping Activation for Quantized Neural Networks.

Learning from Loss Landscape: Generalizable Mixed-Precision Quantization via Adaptive Sharpness-Aware Gradient Aligning PACT: Parameterized Clipping Activation for Quantized Neural Networks

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-15T23:25:30.098821Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2f4369b3-2a7a-43f3-a3c3-d8a6a56acaff · outbound

This paper cites Very Deep Convolutional Networks for Large-Scale Image Recognition.

Learning from Loss Landscape: Generalizable Mixed-Precision Quantization via Adaptive Sharpness-Aware Gradient Aligning Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-15T23:25:30.147509Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation be27764b-c930-4614-9b60-6a2b6ffa1033 · outbound

This paper cites On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima.

Learning from Loss Landscape: Generalizable Mixed-Precision Quantization via Adaptive Sharpness-Aware Gradient Aligning On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-15T23:25:30.132499Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 86c40bd2-f783-4fd8-ba39-70db70f9b903 · outbound

This paper cites DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients.

Learning from Loss Landscape: Generalizable Mixed-Precision Quantization via Adaptive Sharpness-Aware Gradient Aligning DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-15T23:25:30.167198Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 62642afe-89ba-4a24-a73e-461589a752d7 · inbound

Neural Network Quantization by Learning Low-Loss Subspaces cites this paper.

Neural Network Quantization by Learning Low-Loss Subspaces Learning from Loss Landscape: Generalizable Mixed-Precision Quantization via Adaptive Sharpness-Aware Gradient Aligning

Reference 37

Resolution: verified exact; arxiv_id, observed 2026-07-04T16:59:58.451661Z

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 f722e321-f99a-449a-aa68-11fb7378dbdc · inbound

Understanding Calibration and Truncation Error Propagation in Training-Free Low-Rank Compression for LLMs cites this paper.

Understanding Calibration and Truncation Error Propagation in Training-Free Low-Rank Compression for LLMs Learning from Loss Landscape: Generalizable Mixed-Precision Quantization via Adaptive Sharpness-Aware Gradient Aligning

Reference 111

Resolution: unresolved; no resolver link, observed 2026-08-14T04:38:43.942401Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.