Covariant

Covariant AI

Covariant AI

Covariant AI

Covariant AI

Covariant AI

Covariant AI

Covariant AI

Covariant AI

Covariant AI

Covariant AI

Built a Hybrid NAS tool for RNN architectures (HyNAS-R) – Looking for feedback for my final year evaluation [R]

Hi everyone, I'm currently in the evaluation phase of my Final Year Project and am looking for feedback on the system I've built. It's called HyNAS-R, a Neural Architecture Search tool designed to automatically find the best RNN architectures for NLP tasks by combining a zero-cost proxy with metaheuristic optimization. I have recorded a video explaining the core algorithm and the technology stack behind the system, specifically how it uses an Improved Grey Wolf Optimizer and a Hidden Covariance proxy to search through thousands of architectures without expensive training runs. Video Explanation: https://youtu.be/mh5kOF84vHY If anyone is willing to watch the breakdown and share their thoughts, I would greatly appreciate it. Your insights will be directly used for my final university evaluation. Live demo link is inside the form for anyone interested. Feedback Form: https://forms.gle/keLrigwSXBb74od7A Thank you in advance for your time and feedback! submitted by /u/PittuPirate [link] [comments]

[R] From Garbage to Gold: A Formal Proof that GIGO Fails for High-Dimensional Data with Latent Structure — with a Connection to Benign Overfitting Prerequisites

Paper (Full Presentation): https://arxiv.org/abs/2603.12288 GitHub (R simulation, Paper Summary, Audio Overview): https://github.com/tjleestjohn/from-garbage-to-gold I'm Terry, the first author. This paper has been 2.5 years in the making. It synthesizes concepts, logic, and tools from latent factor models, psychometrics and information theory with modern ML. I'd genuinely welcome technical critique from this community. The core result: We formally prove that for data generated by a latent hierarchical structure — Y ← S¹ → S² → S'² — a Breadth strategy of expanding the predictor set asymptotically dominates a Depth strategy of cleaning a fixed predictor set. The proof follows from partitioning predictor-space noise into two formally distinct components: Predictor Error: Observational discrepancy between true and measured predictor values. Addressable by cleaning, repeated measurement, or expanding the predictor set with distinct proxies of S¹. Structural Uncertainty: The irreducible ambiguity arising from the probabilistic S¹ → S² generative mapping — the information deficit that persists even with perfect measurement of a fixed predictor set. Only resolvable by expanding the predictor set with distinct proxies of S¹. The distinction matters because these two noise types obey different information-theoretic limits. Cleaning strategies are provably bounded by Structural Uncertainty regardless of measurement precision. Breadth strategies are not. The BO connection: We formally show that the primary structure Y ← S¹ → S² → S'² naturally produces low-rank-plus-diagonal covariance structure in S'² — precisely the spiked covariance prerequisite that the Benign Overfitting literature (Bartlett et al., Hastie et al., Tsigler & Bartlett) identifies as enabling interpolating classifiers to generalize. This provides a generative data-architectural explanation for why the BO conditions hold empirically rather than being imposed as abstract mathematical prerequisites. Empirical grounding: The theory was motivated by a peer-reviewed clinical result at Cleveland Clinic Abu Dhabi — .909 AUC predicting stroke/MI in 558k patients using over 3.4 million time points and thousands of uncurated EHR variables with no manual cleaning, published in PLOS Digital Health — that could not be explained by existing theory. Honest scope: The framework requires data with a latent hierarchical structure. The paper provides heuristics for assessing whether this condition holds. We are explicit that traditional DCAI's focus on outcome variable cleaning remains distinctly powerful in specific conditions — particularly where Common Method Variance is present. The paper is long — 120 pages with 8 appendices — because GIGO is deeply entrenched and the theory is nuanced. The core proofs are in Sections 3-4. The BO connection is Section 7. Limitations are Section 15 and are extensive. Fully annotated R simulation in the repo demonstrating Dirty Breadth vs Clean Parsimony across varying noise conditions. Happy to engage with technical questions or pushback on the proofs. submitted by /u/Chocolate_Milk_Son [link] [comments]