Transferring Learning Trajectories of Neural Networks
Authors: Daiki Chijiwa
ICLR 2024 | Conference PDF | Archive PDF | Plain Text | 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, α = 0.01, λ = 0.0, µ = 0.9. |