💡
Idea

📚
Literature
_{300–470 papers}

🧪
Hypothesis
_{experiment design}

💻
Code
_{2K–15K lines}

🔬
Execute
_{sandbox + refine}

📝
Write
_{review & audit}

📄
Paper
_{NeurIPS PDF}

_{👆 Click to read the full paper}

💡 Idea

Systematically quantify pre-asymptotic, finite-N deviations of empirical eigenvalue densities from the Marchenko–Pastur law across N = 50 to 5,000, decomposing error into bulk vs. edge components and testing lightweight correction models.

⚙️ Pipeline Journey

🎯 Key Result

Produced a finite-N correction atlas showing convergence rates of spectral densities, with edge deviations persisting significantly longer than bulk errors — providing practical guidance for when the MP law is "close enough."

_{👆 Click to read the full paper}

💡 Idea

Reframe the classical 2SLS / LIML / Fuller-k / JIVE comparison around decision-relevant risk surfaces, mapping finite-sample phase diagrams that show where each estimator is preferred under realistic weak-IV conditions.

⚙️ Pipeline Journey

🎯 Key Result

Generated estimator-switching phase diagrams revealing that Fuller-k dominates in specific small-n, many-instrument regions, while JIVE's bias reduction is systematically offset by variance inflation — providing actionable guidance for empirical researchers.

_{👆 Click to read the full paper}

💡 Idea

Map the boundary between structural and practical identifiability in SIR vs. SEIR models across realistic observation regimes, and quantify when Fisher Information Matrix gives false confidence relative to profile likelihood.

⚙️ Pipeline Journey

🎯 Key Result

Demonstrated that parameterization and observer design choices affect identifiability diagnostics more strongly than the choice between SIR and SEIR structure — with FIM producing overconfident estimates in specific observation-limited regimes where profile likelihood correctly flags non-identifiability.

_{👆 Click to read the full paper}

💡 Idea

Go beyond "which preconditioner wins" — build a feature-conditioned decision map for ILU / Jacobi / SSOR / AMG with CG / GMRES / BiCGSTAB, stratified by sparsity-graph structure and matrix pathology under realistic setup-vs-solve cost budgets.

⚙️ Pipeline Journey

🎯 Key Result

Produced a setup-vs-solve tradeoff analysis showing that methods considered "best" under solve-time alone are often suboptimal under realistic memory and setup budgets — with AMG dominance limited to specific elliptic SPD matrix families.

_{👆 Click to read the full paper}

💡 Idea

Most LoRA configurations use a fixed, uniform rank across all layers. GARD proposes using the spectrum of layer-wise gradients — eigenvalues of gradient covariance — to dynamically allocate rank where it matters most, under a strict parameter budget.

⚙️ Pipeline Journey

🎯 Key Contribution

A principled alternative to uniform rank allocation: GARD links intrinsic gradient dimensionality to low-rank adapter capacity, periodically updating ranks during training using smoothed spectra.

_{👆 Click to read the full paper}

💡 Idea

Count-based exploration in RL relies on state visitation counts, but raw state spaces are too large for effective counting. LACE designs online-learned, task-aware state abstractions optimized specifically for count-based exploration in sparse-reward environments.

⚙️ Pipeline Journey

🎯 Key Result

DQN baseline achieves 356.7 mean reward in sparse-reward gridworld tasks. The paper analyzes the trade-off between abstraction compactness for counting and information sufficiency for downstream control.

_{👆 Click to read the full paper}

💡 Idea

Existing ViT token pruning methods reduce tokens based on attention or saliency without considering frequency content. FAME uses DCT/FFT-based spectral filters to distinguish high-frequency detail tokens from low-frequency background tokens, merging progressively across layers.

⚙️ Pipeline Journey

🎯 Key Result

ViT-B/16 baseline: 56.54% accuracy (3 seeds). Detailed analysis of the accuracy-efficiency tradeoff and per-layer metric breakdowns for frequency-aware vs. similarity-based merging.

_{👆 Click to read the full paper}

💡 Idea

Standard knowledge distillation transfers teacher knowledge assuming train/test distributions match. CRAFT introduces reliability-aware contrastive feature alignment that aligns teacher-student features across clean and corrupted views, while suppressing fragile teacher directions via a de-alignment loss.

⚙️ Pipeline Journey

🎯 Key Result

Attention-based feature KD improves robustness by +3 pts over ERM, while naive CRD degrades it by -12 pts — motivating CRAFT's reliability-aware design.