Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Transferring Learning Trajectories of Neural Networks
Authors: Daiki Chijiwa
ICLR 2024 | Venue PDF | LLM Run Details | Input Tokens: 30,057 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,788 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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. |