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
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 heading | Starts on PDF page | Estimated span | Share of entire PDF |
|---|---|---|---|
| 1. Introduction | 1 | ≈ 2.58 pp. | 6.97% |
| 2. Related work | 3 | ≈ 0.44 pp. | 1.19% |
| 3. Theoretical Foundations of Compression-Based Inverse Solvers | 4 | ≈ 1.4 pp. | 3.78% |
| 4. Lottery Prior | 5 | ≈ 1.54 pp. | 4.16% |
| 5. Experimental results | 7 | ≈ 2.51 pp. | 6.78% |
| 6. Conclusion and future work | 9 | ≈ 0.26 pp. | 0.7% |
| Impact Statement | 10 | ≈ 0.05 pp. | 0.14% |
| Acknowledgments | 10 | ≈ 0.05 pp. | 0.14% |
| References | 10 | ≈ 2.94 pp. | 7.95% |
| Appendix | 13 | ≈ 0.2 pp. | 0.54% |
| A. Proof of theorem and corollary | 13 | ≈ 6 pp. | 16.22% |
| B. Implementation details | 19 | ≈ 4.8 pp. | 12.97% |
| C. Visualization | 24 | ≈ 0.32 pp. | 0.86% |
| D. Ablation studies and more experiments | 24 | ≈ 2.74 pp. | 7.41% |
| E. Detailed datapoints | 27 | ≈ 10.86 pp. | 29.35% |
All detected figure and table locations
Main text
Attached appendix
- Table 7 · p. 21
- Figure 7 · p. 22
- Table 8 · p. 24
- Table 9 · p. 25
- Table 10 · p. 25
- Table 11 · p. 25
- Table 12 · p. 26
- Table 13 · p. 26
- Table 14 · p. 26
- Figure 8 · p. 27
- Figure 9 · p. 28
- Figure 10 · p. 29
- Figure 11 · p. 30
- Figure 12 · p. 31
- Figure 13 · p. 32
- Figure 14 · p. 33
- Figure 15 · p. 34
- Figure 16 · p. 35
- Table 15 · p. 36
- Table 16 · p. 36
- Table 17 · p. 37
- Table 18 · p. 37
- Table 19 · p. 37
- Table 20 · p. 37
Argument and organization
- 1 Introduction; 2 Related Work
Reformulates single-observation restoration as compression-regularized fitting with randomized priors.
- 3 Theoretical Foundations; 4 Lottery Prior
Separates finite-codebook guarantees from entropy, mask and ensemble implementation.
- 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.