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..
Transferring Learning Trajectories of Neural Networks
Authors: Daiki Chijiwa
ICLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We empirically show that the transferred parameters achieve non-trivial accuracy before any direct training, and can be trained significantly faster than training from scratch. |
| Researcher Affiliation | Industry | Daiki Chijiwa NTT Computer and Data Science Laboratories, NTT Corporation |
| Pseudocode | Yes | Algorithm 1 Gradient Matching along Trajectory (GMT) |
| Open Source Code | No | The paper does not explicitly provide a link to open-source code for the methodology or state that the code is available. |
| Open Datasets | Yes | MNIST (Le Cun et al., 1998) is a dataset of 28 28 images of hand-written digits, which is available under the terms of the CC BY-SA 3.0 license. |
| Dataset Splits | Yes | For all datasets, we split the officially given training dataset into 9:1 for training and validation. |
| Hardware Specification | Yes | Our computing environment is a machine with 12 Intel CPUs, 140 GB CPU memory and a single A100 GPU. |
| Software Dependencies | No | The paper mentions 'Python 3' and 'Py Torch library' but does not specify their version numbers. |
| Experiment Setup | Yes | We used E = 15, B = 128, ฮฑ = 0.01, ฮป = 0.0, ยต = 0.9. |