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@davidkfoss davidkfoss released this 21 May 05:50
v0.1.0
08dc8c5

Archived Result Bundle for AEES Thesis and ICONIP 2026 Submission

This release provides the archived experiment-result bundle used to regenerate the reported tables and figures for the AEES thesis and the ICONIP 2026 paper submission.

The reusable AEES implementation itself is maintained separately as the pulseopt package:

Contents

The archive contains run-result JSON files and related compact result artifacts used by the reporting scripts in this repository. It is intended to support regeneration of aggregate tables and figures without rerunning the full training suite.

Expected extraction layout:

archived_results/
  checkpointing/
  cifar_clean/
  cifar_noisy/
  clean_agnews/
  compute_overhead/
  instability_penalty_ablation/
  nlp_noise_ablation/
  noisy_agnews/
  sst2/
  trend_context_ablation/

Folder summary:

  • checkpointing/: peak-validation checkpoint rerun outputs used for checkpoint-diagnostics tables and figures.
  • cifar_clean/: clean CIFAR-100 experiment outputs.
  • cifar_noisy/: noisy CIFAR-100 runs, fixed-multiplier ablations, and related diagnostics.
  • clean_agnews/: clean AG News fine-tuning outputs.
  • compute_overhead/: timing outputs used for wall-clock overhead tables and figures.
  • instability_penalty_ablation/: supplementary ablation outputs for instability-penalty variants.
  • nlp_noise_ablation/: AG News sigma-axis falsification outputs.
  • noisy_agnews/: noisy AG News main and axis-ablation outputs.
  • sst2/: SST-2 fine-tuning outputs used in the thesis.
  • trend_context_ablation/: supplementary ablation outputs for trend-context variants.

The ICONIP 2026 paper submission uses a scoped subset of this archive, primarily:

archived_results/checkpointing/
archived_results/cifar_noisy/
archived_results/noisy_agnews/
archived_results/nlp_noise_ablation/
archived_results/compute_overhead/

The thesis uses the broader archive, including clean CIFAR-100, clean AG News, SST-2, and supplementary ablation results.

What is excluded

This archive does not include model checkpoints, dataset caches, HuggingFace caches, raw temporary logs, or large transient training artifacts.

The archive is not intended to provide bitwise replay of every training trajectory. Instead, it provides the archived result files treated as the authoritative numerical source for the reported aggregate tables and figures.

How to use

Download the archive and extract it at the repository root so that the extracted directory is named:

archived_results/

The repository separates three artifact roles:

archived_results/        canonical archived result inputs
generated_artifacts/     curated tables, CSV summaries, and figures used in the thesis / paper submission
reproduced_artifacts/    local regenerated outputs for comparison

Regenerated outputs should be written to reproduced_artifacts/ and compared against the curated files in generated_artifacts/ before replacing any committed artifact.

Regenerating tables and figures

Table-generation scripts are configured with default paths. In normal use, they can be run directly and will read from archived_results/ and write to reproduced_artifacts/tables/....

Example table command:

uv run python scripts/tables/make_nlp_noise_ablation_table.py

Figure-generation scripts require explicit input and output paths. The correct --runs-root differs by script, and some scripts require additional flags. Check each script's --help or the repository READMEs for the expected layout.

Example figure command:

uv run python -m scripts.plots.cifar.plot_cifar_noisy_curves \
  --runs-root archived_results/cifar_noisy \
  --out-dir reproduced_artifacts/figures/cifar \
  --noise-setting sym40

When a figure script cannot find its inputs, it may exit non-zero and write a <figure-name>.MISSING.md file in the output directory explaining what was missing.

The TikZ method schematic is generated separately by:

bash scripts/plots/method/build_aees_overview.sh

Reproducibility notes

The archived files are the source used by the table and figure generation scripts. Re-running the original training jobs with the same seeds is expected to reproduce the same qualitative behavior and similar aggregate results, but exact bitwise or epoch-level reproduction across machines is not guaranteed.

Possible sources of variation include GPU architecture, CUDA/cuDNN kernels, PyTorch and torchvision versions, HuggingFace/transformers versions, DataLoader behavior, plotting backends, fonts, and other runtime details.

PDF and PNG figures may differ slightly across systems. For numerical checks, compare generated summary CSV or text files where available.

Checksum

A SHA-256 checksum file is provided alongside the archive. After downloading, verify with:

shasum -a 256 -c archived_results_aees_thesis_iconip2026.sha256

License

The repository code is released under the MIT license. The archived result artifacts are provided for reproducibility of the thesis and ICONIP 2026 paper submission.

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