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 [1].
On feature learning in neural networks with global convergence guarantees
Authors: Zhengdao Chen, Eric Vanden-Eijnden, Joan Bruna
ICLR 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We also show empirically that, unlike in the Neural Tangent Kernel (NTK) regime, our multi-layer model exhibits feature learning and can achieve better generalization performance than its NTK counterpart. and 4 NUMERICAL EXPERIMENTS |
| Researcher Affiliation | Academia | Zhengdao Chen Courant Institute of Mathematical Sciences New York University New York, NY 10012, USA EMAIL Eric Vanden-Eijnden Courant Institute of Mathematical Sciences New York University New York, NY 10012, USA EMAIL Joan Bruna Courant Institute of Mathematical Sciences and Center for Data Science New York University New York, NY 10012, USA EMAIL |
| Pseudocode | No | No explicit pseudocode or algorithm blocks were found in the paper. |
| Open Source Code | No | The paper does not contain an explicit statement about the release of source code for the described methodology, nor does it provide a link to a code repository. |
| Open Datasets | No | The data set is inspired by [69]: We sample both the training and the test set i.i.d. from the distribution (x, y) D on Rd+1, under which the joint distribution of (x1, x2, y) is P(x1 = 1, x2 = 0, y = 1) =1 P(x1 = 1, x2 = 0, y = 1) =1 P(x1 = 0, x2 = 1, y = 1) =1 P(x1 = 0, x2 = 1, y = 1) =1 and x3, ..., xd each follow the uniform distribution in [ 1, 1], independently from each other as well as x1, x2 and y. While inspired by [69], they describe a generative process rather than providing access to a specific pre-existing public dataset. |
| Dataset Splits | No | The paper mentions training and test sets but does not specify a validation set or explicit train/test/validation dataset splits. |
| Hardware Specification | Yes | The experiments are run with NVIDIA GPUs (1080ti and Titan RTX). |
| Software Dependencies | No | The paper does not provide specific version numbers for software dependencies used in their experiments. |
| Experiment Setup | Yes | We choose to train the models using 50000 steps of (full-batch) GD with step size δ = 1. and We choose σ to be tanh. and For each choice of n, we run the experiment with 5 different random seeds... |