Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Deep Learning meets Nonparametric Regression: Are Weight-Decayed DNNs Locally Adaptive?
Authors: Kaiqi Zhang, Yu-Xiang Wang
ICLR 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 5 EXPERIMENT We empirically compare a parallel neural network (PNN) and a vanilla Re LU neural network (NN) with smoothing spline, trend filtering (TF) (Tibshirani, 2014), and wavelet denoising. [...] The results are shown in Figure 3. |
| Researcher Affiliation | Academia | Kaiqi Zhang Department of Electrical and Computer Engineering University of California, Santa Barbara EMAIL Yu-Xiang Wang Department of Computer Science University of California, Santa Barbara EMAIL |
| Pseudocode | No | No explicit pseudocode or algorithm block was found in the paper. |
| Open Source Code | No | The paper does not contain an explicit statement about the release of open-source code for the described methodology, nor does it provide a link to a code repository. |
| Open Datasets | Yes | We use two target functions: a Doppler function whose frequency is decreasing(Figure 3(a)-(c)(h)), and a combination of piecewise linear function and piecewise cubic function, or vary function (Figure 3(d)-(f)(i)). H.1 TARGET FUNCTIONS The doppler function used in Figure 3(d)-(f) is f(x) = sin(4/(x + 0.01)) + 1.5. The vary function used in Figure 3(g)-(i) is f(x) = M1(x/0.01) + M1((x 0.02)/0.02) + M1((x 0.06)/0.03) + M1((x 0.12)/0.04) + M3((x 0.2)/0.02) + M3((x 0.28)/0.04) + M3((x 0.44)/0.06) + M3((x 0.68)/0.08), where M1, M3 are first and third order Cardinal B-spline bases functions respectively. |
| Dataset Splits | No | The paper describes using a 'training dataset' (Dn) and calculating MSE, but it does not specify explicit train/validation/test splits, nor does it mention a validation set. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., CPU, GPU models, or cloud instance types) used for running the experiments. |
| Software Dependencies | No | The paper mentions software like CVXPY, MOSEK, and R (for smooth.spline), but it does not specify their version numbers, which are necessary for reproducible ancillary software details. |
| Experiment Setup | Yes | In the piecewise polynomial function ( vary ) experiment, the depth of the PNN L = 10, the width of each subnetwork w = 10, and the model contains M = 500 subnetworks. [...] We used Adam optimizer with learning rate of 10-3. We first train the neural network layer by layer without weight decay. |