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Nirav Vaghasiya

Proposed research direction

How does dataset structure affect the effectiveness of deep neural network training?

Model architecture gets most of the attention, but balance, diversity, size, augmentation and preprocessing can set the ceiling on what training achieves. This proposed study would treat dataset structure as the independent variable and ask how each dimension changes what a model learns and how well it generalises.

Pipeline

Proposed pipelinedata → model → generalisation

  1. Dataset characteristics

    Balance, diversity, size, augmentation, preprocessing

  2. Model architecture

    Convolutional networks and Transformers

  3. Training

    Fixed budgets and controlled schedules

  4. Evaluation

    Held-out and shifted test sets

  5. Generalization

    The gap between fitting and understanding

Proposed protocol: vary one data property while holding architecture and training budget fixed, then measure held-out and shifted-set generalisation. No findings are claimed.

Dimensions

Six variables, six questions for a proposed study.

The proposed protocol would vary each dimension on its own while keeping architecture and training budget fixed. These are design questions; no findings are claimed without a public artifact.

  • Vary one property of the data while holding architecture and training budget fixed.
  • Measure held-out and shifted-set generalisation, not only training loss.
  • Pre-register comparisons and retain negative and null results.

01

Class balance

How much does imbalance cost, and which interventions recover it without distorting the decision boundary?

02

Diversity

Does within-class variety matter more than raw volume once a dataset passes a certain size?

03

Dataset size

Where do returns diminish, and does the curve differ between convolutional and attention-based models?

04

Augmentation

Which augmentations act as genuine regularisers and which merely inflate the training set?

05

Preprocessing

How sensitive is training to normalisation, resolution and tokenisation choices made before the first epoch?

06

Architecture sensitivity

Do CNNs and Transformers respond differently to the same structural change in the data?

Shipped engineering evaluation

The same discipline, applied to shipped systems.

Separate from the proposed research direction above, these controlled baselines, ablations, calibration checks and faithfulness checks already exist in public repositories.

BrainOS

Published on PyPI · runtime in alpha

Ablation ladder and long-run validation

Each memory mechanism — temporal supersession, stale exclusion, security, consolidation, forgetting — can be switched off independently. Three long-running workloads check bounded growth, stable recall and p95 latency.

Read the case study

BrainOS Context Lab

v0.1.0 · early

Controlled baselines with effect sizes

Five conditions and four ablations over a seeded, hash-pinned benchmark with seven question categories; paired t-tests, Cohen's d and a nine-label failure taxonomy — with early observations explicitly not presented as research results.

Read the case study

Chess Analysis Engine

Live demo on Hugging Face Spaces

Faithfulness checks for generated text

Selected numerical and move-label claims in each model-written annotation are checked against engine output. Famous-game regressions pin twenty reviewed expectations with written rationales.

Read the case study

Polyglot Swarm

Alpha

Calibration and five-arm ablation

The verifier's confidence is measured with expected calibration error, and the tutoring stack is ablated from conversation-only to the full system on scripted learners in deterministic mode.

Read the case study