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..
Improving Neural ODE Training with Temporal Adaptive Batch Normalization
Authors: Su Zheng, Zhengqi Gao, Fan-Keng Sun, Duane Boning, Bei Yu, Martin D. Wong
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive numerical experiments on image classification and physical system modeling substantiate the superiority of TA-BN compared to baseline methods. |
| Researcher Affiliation | Academia | Su Zheng1 , Zhengqi Gao2 , Fan-Keng Sun2, Duane S. Boning2, Bei Yu1, Martin Wong1 1Department of CSE, CUHK 2 Department of EECS, MIT |
| Pseudocode | Yes | Algorithm 1 The forward pass of a TA-BN layer at time tj |
| Open Source Code | Yes | We put part of the code for reproduciblity in supplementary. It will be released upon acceptance. |
| Open Datasets | Yes | We conduct image classification across datasets including MNIST [26], SVHN [33], CIFAR-10, CIFAR-100 [22], and Tiny-Image Net [24]. |
| Dataset Splits | No | The paper explicitly states a 90% training and remaining testing split for the Charge Pump circuit modeling dataset, but does not provide details for a validation split for any of the datasets used. |
| Hardware Specification | Yes | All experiments are run on a Linux server with RTX 3090 GPUs. |
| Software Dependencies | No | The paper mentions software like 'Py Torch' and 'Torch Diffeq' but does not specify their version numbers. |
| Experiment Setup | Yes | We employ the dopri5 solver with a tolerance of 10 3 for ODE solving and adopt the Adam W optimizer [27] with a learning rate of 10 3 to train the neural networks for 128 epochs. The training batch size is 256. We set M = 100 for TA-BN. |