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Writing study: Lottery Prior: Randomized Neural Compression for Zero-Shot Inverse Problems

← ICML 2026 oral writing corpus

Haotian Wu, Di You, Pier Luigi Dragotti, Deniz Gunduz · Official ICML 2026 oral record

Figure, table and section ledger

Editorial contribution type: Training, objectives & optimization (primary).

Layout extraction, not a required paper template. Counts are distinct caption labels; a multi-panel figure counts once. Section spans are estimated from heading positions, including floats and whitespace. Method and limitations.

Canonical PDF
37 physical pages
Before references / appendix
≈ 9.15 page-equivalents
Main figures / tables
6 / 6
Attached appendix figures / tables
10 / 14
Attached appendix span
≈ 24.92 page-equivalents
Before references / appendix: ≈ 24.73%References / intervening material: ≈ 7.92%Attached appendix: ≈ 67.35%Estimated layout envelopes, not text/artwork area. Unresolved appendix boundaries remain within the undivided post-body material.

Source-bound correction: Join wrapped printed heading text using adjacent source lines; original heading start coordinates are unchanged.

Section-by-section lengths and source pages

The title/abstract/front matter precede the first numbered heading. A span ends at the next detected top-level heading, not at the last sentence; end-page whitespace is included. References and statements are listed separately. These are not measurements of text-only or artwork area.

Top-level headingStarts on PDF pageEstimated spanShare of entire PDF
1. Introduction1≈ 2.58 pp.6.97%
2. Related work3≈ 0.44 pp.1.19%
3. Theoretical Foundations of Compression-Based Inverse Solvers4≈ 1.4 pp.3.78%
4. Lottery Prior5≈ 1.54 pp.4.16%
5. Experimental results7≈ 2.51 pp.6.78%
6. Conclusion and future work9≈ 0.26 pp.0.7%
Impact Statement10≈ 0.05 pp.0.14%
Acknowledgments10≈ 0.05 pp.0.14%
References10≈ 2.94 pp.7.95%
Appendix13≈ 0.2 pp.0.54%
A. Proof of theorem and corollary13≈ 6 pp.16.22%
B. Implementation details19≈ 4.8 pp.12.97%
C. Visualization24≈ 0.32 pp.0.86%
D. Ablation studies and more experiments24≈ 2.74 pp.7.41%
E. Detailed datapoints27≈ 10.86 pp.29.35%
All detected figure and table locations

Argument and organization

  1. 1 Introduction; 2 Related Work

    Reformulates single-observation restoration as compression-regularized fitting with randomized priors.

  2. 3 Theoretical Foundations; 4 Lottery Prior

    Separates finite-codebook guarantees from entropy, mask and ensemble implementation.

  3. 5 Experimental Results; 6 Conclusion and Future Work

    Moves from core inverse tasks to adaptation, cost and operator-dependent limitations.

Problem, claim and contribution

The abstract defines zero-shot as recovery from one degraded observation without external training data, clarifying the resource constraint behind the compact-model claim.

Abstract, p.1 · Main paper

Argument and section order

The theory discussion distinguishes measurement-domain guarantees from signal-domain guarantees requiring a restricted injectivity assumption, before introducing the learned practical codebook.

Section 3.3 and Section 4 opening, p.5 · Main paper

Figures, captions and visual explanation

The codec schematic connects a rate-distortion example to latent modulation and a masked network; restoration panels use matching crop boxes across original, corrupted and reconstructed images.

Figure 3, p.3; Figures 5-6, p.8 · Main paper

Tables, comparisons and evidence

The demosaicing table labels pretrained and fine-tuned foundation-prior comparators and retains their stronger results; runtime distinguishes per-step cost, required optimization steps and multi-prior ensembling.

Tables 4 and 6 with discussion, p.9 · Main paper

Appendix and reproducibility

The appendix begins with a contents roadmap and explicitly limits the stated maximum-likelihood derivation and guarantees to Gaussian linear inverse problems despite broader empirical noise tests.

Appendix contents and A.1, p.13 · Main paper

Clarity and writing conventions

The bound comparison states operating regimes where a prior result is tighter, while the conclusion admits that severely ill-conditioned operators remain challenging.

Remark 3.4, p.5; limitations, p.9 · Main paper

What to reuse—and what not to infer

Transferable lesson: Separate theorem assumptions from the adaptive implementation and report optimization/ensemble costs alongside compact parameter counts.

Boundary: Fixed-codebook Gaussian guarantees do not automatically certify every learned-codebook, Poisson or nonlinear experiment; fewer effective parameters do not eliminate per-image optimization.

Apply this lesson using the argument and evidence worksheet.

Inspection record and sources

Checked
2026-10-07
Reading status
Writing-focused manuscript scan + selected visuals inspected
Main text
Writing-oriented scan of the main argument through physical p.9, with focused physical-page text inspection on 1, 3, 5, 7, 9. Framing, section progression, mechanism/proof explanation, displays, comparison conditions and conclusions were inspected. This is not exhaustive technical reading, proof verification or reproduction.
Appendix / supplement
Appendix organization and main-text pointers were checked; focused text inspection of physical pages 13. Remaining appendix proofs and details were not exhaustively read. No separately linked supplement inspected.
Visually inspected PDF pages
3, 8

This is a writing-focused scan of the manuscript and the stated supplementary scope: framing, organization, visual explanation, evidence presentation and wording. It is not a line-by-line proof audit, reproduction of results or exhaustive scientific review.