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Researcher Evidence Record

Junbiao Pang

This bounded record lists 24 Pith paper rows and 0 imported work rows attributed to this corpus identity. The enumerated, non-disputed paper rows include cs.CV, cs.LG, cs.IR work dated 2023 to 2026. The record describes sources and coverage; it makes no judgment about the person.

Compiled coverage vector

Measured lane counts only. Not a trust score or person verdict.

Enumerated paper scope: 6 fields (cs.CV, cs.LG, cs.IR, +3 more) · 2023-2026 sources: authors, author_identifiers · paper_authors · author_works · current_verdicts · cited_works

A sourced case file for attributed work. It is neither a profile score nor a verdict about this researcher.

Attributed works

A bounded ledger from the Pith paper and imported-work queries. Counts and source confidence stay with each work.

  1. 2026 Pith paper

    From Perturbation Correction to Geometry-Aware Sampling: Sharpness-Guided Equilibrium Sampling for Balanced Flat Minima in Long-Tailed Learning

    cs.LG provisional measured, no current review

    Sources and evidence
    Authorship source
    arxiv_oai
    Printed name
    Junbiao Pang
    Author position
    2
    Identity state
    provisional
    Source confidence
    0.7
    Review coverage
    Measured: no current Pith review exists.
    Citation counts
    No source count is attached to this work row.
  2. 2026 Pith paper

    Artificial Intelligence for Understanding and Managing Transportation Behavior in Sustainable Smart Cities

    cs.AI provisional measured, no current review

    Sources and evidence
    Authorship source
    arxiv_oai
    Printed name
    Junbiao Pang
    Author position
    1
    Identity state
    provisional
    Source confidence
    0.7
    Review coverage
    Measured: no current Pith review exists.
    Citation counts
    No source count is attached to this work row.
  3. 2026 Pith paper

    Gradient-Energy Guided Block-Wise Perturbations for Sharpness-Aware Minimization

    cs.LG provisional measured, no current review

    Sources and evidence
    Authorship source
    arxiv_oai
    Printed name
    Junbiao Pang
    Author position
    3
    Identity state
    provisional
    Source confidence
    0.7
    Review coverage
    Measured: no current Pith review exists.
    Citation counts
    No source count is attached to this work row.
  4. 2026 Pith paper

    Efficient Tuning Before Low-Bit Post-Training Quantization for Stochastic Gradient Descent-optimized Models

    cs.CV provisional measured, no current review

    Sources and evidence
    Authorship source
    arxiv_oai
    Printed name
    Junbiao Pang
    Author position
    2
    Identity state
    provisional
    Source confidence
    0.7
    Review coverage
    Measured: no current Pith review exists.
    Citation counts
    No source count is attached to this work row.
  5. 2026 Pith paper

    Adversarial LassoNet: Robust Feature Selection via Stability-Driven Sparse Learning

    cs.LG provisional current review present

    Sources and evidence
    Authorship source
    arxiv_oai
    Printed name
    Junbiao Pang
    Author position
    3
    Identity state
    provisional
    Source confidence
    0.7
    Review coverage
    Measured: a current Pith review exists.
    Citation counts
    No source count is attached to this work row.
  6. 2026 Pith paper

    Discovering Sparse Counterfactual Factors via Latent Adjustment for Survey-based Community Intervention

    cs.LG provisional current review present

    Sources and evidence
    Authorship source
    backfill
    Printed name
    Junbiao Pang
    Author position
    3
    Identity state
    provisional
    Source confidence
    0.7
    Review coverage
    Measured: a current Pith review exists.
    Citation counts
    No source count is attached to this work row.
  7. 2026 Pith paper

    NCSAM Noise-Compensated Sharpness-Aware Minimization for Noisy Label Learning

    cs.LG provisional measured, no current review

    Sources and evidence
    Authorship source
    arxiv_oai
    Printed name
    Junbiao Pang
    Author position
    2
    Identity state
    provisional
    Source confidence
    0.7
    Review coverage
    Measured: no current Pith review exists.
    Citation counts
    No source count is attached to this work row.
  8. 2025 Pith paper

    Stabilizing Quantization-Aware Training by Implicit-Regularization on Hessian Matrix

    cs.CV provisional measured, no current review

    Sources and evidence
    Authorship source
    arxiv_oai
    Printed name
    Junbiao Pang
    Author position
    1
    Identity state
    provisional
    Source confidence
    0.7
    Review coverage
    Measured: no current Pith review exists.
    Citation counts
    No source count is attached to this work row.
  9. 2025 Pith paper

    Uncertainty-aware Long-tailed Weights Model the Utility of Pseudo-labels for Semi-supervised Learning

    cs.CV provisional measured, no current review

    Sources and evidence
    Authorship source
    arxiv_oai
    Printed name
    Junbiao Pang
    Author position
    2
    Identity state
    provisional
    Source confidence
    0.7
    Review coverage
    Measured: no current Pith review exists.
    Citation counts
    No source count is attached to this work row.
  10. 2024 Pith paper

    Unsupervised Abnormal Stop Detection for Long Distance Coaches with Low-Frequency GPS

    cs.LG provisional measured, no current review

    Sources and evidence
    Authorship source
    arxiv_oai
    Printed name
    Junbiao Pang
    Author position
    2
    Identity state
    provisional
    Source confidence
    0.7
    Review coverage
    Measured: no current Pith review exists.
    Citation counts
    No source count is attached to this work row.
  11. 2024 Pith paper

    Modeling the Popularity of Events on Web by Sparsity and Mutual-Excitation Guided Graph Neural Network

    cs.MM provisional measured, no current review

    Sources and evidence
    Authorship source
    arxiv_oai
    Printed name
    Junbiao Pang
    Author position
    3
    Identity state
    provisional
    Source confidence
    0.7
    Review coverage
    Measured: no current Pith review exists.
    Citation counts
    No source count is attached to this work row.
  12. 2024 Pith paper

    Bilateral Sharpness-Aware Minimization for Flatter Minima

    cs.CV provisional measured, no current review

    Sources and evidence
    Authorship source
    arxiv_oai
    Printed name
    Junbiao Pang
    Author position
    2
    Identity state
    provisional
    Source confidence
    0.7
    Review coverage
    Measured: no current Pith review exists.
    Citation counts
    No source count is attached to this work row.
  13. 2024 Pith paper

    Bundle Fragments into a Whole: Mining More Complete Clusters via Submodular Selection of Interesting webpages for Web Topic Detection

    cs.IR provisional measured, no current review

    Sources and evidence
    Authorship source
    arxiv_oai
    Printed name
    Junbiao Pang
    Author position
    1
    Identity state
    provisional
    Source confidence
    0.7
    Review coverage
    Measured: no current Pith review exists.
    Citation counts
    No source count is attached to this work row.
  14. 2024 Pith paper

    Decorrelating Structure via Adapters Makes Ensemble Learning Practical for Semi-supervised Learning

    cs.CV provisional measured, no current review

    Sources and evidence
    Authorship source
    arxiv_oai
    Printed name
    Junbiao Pang
    Author position
    2
    Identity state
    provisional
    Source confidence
    0.7
    Review coverage
    Measured: no current Pith review exists.
    Citation counts
    No source count is attached to this work row.
  15. 2024 Pith paper

    Towards Scalable Topic Detection on Web via Simulating Levy Walks Nature of Topics in Similarity Space

    cs.IR provisional measured, no current review

    Sources and evidence
    Authorship source
    arxiv_oai
    Printed name
    Junbiao Pang
    Author position
    1
    Identity state
    provisional
    Source confidence
    0.7
    Review coverage
    Measured: no current Pith review exists.
    Citation counts
    No source count is attached to this work row.
  16. 2024 Pith paper

    Asymptotic Unbiased Sample Sampling to Speed Up Sharpness-Aware Minimization

    cs.CV provisional measured, no current review

    Sources and evidence
    Authorship source
    arxiv_oai
    Printed name
    Junbiao Pang
    Author position
    2
    Identity state
    provisional
    Source confidence
    0.7
    Review coverage
    Measured: no current Pith review exists.
    Citation counts
    No source count is attached to this work row.
  17. 2024 Pith paper

    Modeling Multi-Granularity Context Information Flow for Pavement Crack Detection

    cs.CV provisional measured, no current review

    Sources and evidence
    Authorship source
    arxiv_oai
    Printed name
    Junbiao Pang
    Author position
    1
    Identity state
    provisional
    Source confidence
    0.7
    Review coverage
    Measured: no current Pith review exists.
    Citation counts
    No source count is attached to this work row.
  18. 2024 Pith paper

    Finding A Taxi with Illegal Driver Substitution Activity via Behavior Modelings

    cs.CY provisional measured, no current review

    Sources and evidence
    Authorship source
    arxiv_oai
    Printed name
    Junbiao Pang
    Author position
    1
    Identity state
    provisional
    Source confidence
    0.7
    Review coverage
    Measured: no current Pith review exists.
    Citation counts
    No source count is attached to this work row.
  19. 2024 Pith paper

    Pixel-Wise Symbol Spotting via Progressive Points Location for Parsing CAD Images

    cs.CV provisional measured, no current review

    Sources and evidence
    Authorship source
    arxiv_oai
    Printed name
    Junbiao Pang
    Author position
    1
    Identity state
    provisional
    Source confidence
    0.7
    Review coverage
    Measured: no current Pith review exists.
    Citation counts
    No source count is attached to this work row.
  20. 2024 Pith paper

    A Channel-ensemble Approach: Unbiased and Low-variance Pseudo-labels is Critical for Semi-supervised Classification

    cs.CV provisional measured, no current review

    Sources and evidence
    Authorship source
    arxiv_oai
    Printed name
    Junbiao Pang
    Author position
    2
    Identity state
    provisional
    Source confidence
    0.7
    Review coverage
    Measured: no current Pith review exists.
    Citation counts
    No source count is attached to this work row.
  21. 2024 Pith paper

    Effective Gradient Sample Size via Variation Estimation for Accelerating Sharpness aware Minimization

    cs.CV provisional measured, no current review

    Sources and evidence
    Authorship source
    arxiv_oai
    Printed name
    Junbiao Pang
    Author position
    2
    Identity state
    provisional
    Source confidence
    0.7
    Review coverage
    Measured: no current Pith review exists.
    Citation counts
    No source count is attached to this work row.
  22. 2024 Pith paper

    In-Distribution Consistency Regularization Improves the Generalization of Quantization-Aware Training

    cs.CV provisional measured, no current review

    Sources and evidence
    Authorship source
    arxiv_oai
    Printed name
    Junbiao Pang
    Author position
    1
    Identity state
    provisional
    Source confidence
    0.7
    Review coverage
    Measured: no current Pith review exists.
    Citation counts
    No source count is attached to this work row.
  23. 2023 Pith paper

    Generating Unbiased Pseudo-labels via a Theoretically Guaranteed Chebyshev Constraint to Unify Semi-supervised Classification and Regression

    cs.CV provisional measured, no current review

    Sources and evidence
    Authorship source
    arxiv_oai
    Printed name
    Junbiao Pang
    Author position
    2
    Identity state
    provisional
    Source confidence
    0.7
    Review coverage
    Measured: no current Pith review exists.
    Citation counts
    No source count is attached to this work row.
  24. 2023 Pith paper

    Modeling the Uncertainty with Maximum Discrepant Students for Semi-supervised 2D Pose Estimation

    cs.CV provisional measured, no current review

    Sources and evidence
    Authorship source
    arxiv_oai
    Printed name
    Junbiao Pang
    Author position
    2
    Identity state
    provisional
    Source confidence
    0.7
    Review coverage
    Measured: no current Pith review exists.
    Citation counts
    No source count is attached to this work row.

Evidence apparatus

The machinery behind this record. Every lane states whether Pith measured it, did not query it, could not reach it, or withheld it.

LaneStateObservedBoundary and source
identity Measured 2 Canonical identity row plus public typed identifiers.
source=authors, author_identifiers
papers Measured 24 of 24 bounded rows Rows attributed to this author UUID in the Pith corpus.
source=paper_authors
works Measured zero 0 of 0 bounded rows Imported works not duplicated by the paper ledger.
source=author_works
reviews Measured 2 of 24 bounded rows Coverage count only. No review outcome is projected onto the person.
source=current_verdicts
citations Measured zero 0 of 24 bounded rows Counts remain itemized by work and source.
source=cited_works
coauthors Measured 24 of 24 bounded rows Shared-work edges from admitted paper rows.
source=paper_authors
account Unavailable No public count of 1 bounded rows Account metadata is separate from corpus evidence.
source=users.author_id
Public identity sources
  • name variant
    Junbiao Pang
    backfill
    confidence 0.6
Enumerated research scope

Fields and dates come only from enumerated, non-disputed Pith paper rows. They do not claim career completeness.

  • cs.CV13 rows
  • cs.LG6 rows
  • cs.IR2 rows
  • cs.AI1 rows
  • cs.CY1 rows
  • cs.MM1 rows
  • 20232 rows
  • 202413 rows
  • 20252 rows
  • 20267 rows
Shared-work index
Record scope

The work queries are bounded. Missing rows may mean measured zero, an unavailable source, a query that did not run, or private data that Pith withheld. The lane table keeps those cases separate.

Paper findings remain attached to papers. They do not become findings about this researcher.