Recurrent Kernel Networks
Authors: Dexiong Chen, Laurent Jacob, Julien Mairal
NeurIPS 2019 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We experimentally show that our approach is well suited to biological sequences, where it outperforms existing methods for protein classification tasks. 4 Experiments We evaluate RKN and compare it to typical string kernels and RNN for protein fold recognition. |
| Researcher Affiliation | Academia | Dexiong Chen Inria EMAIL Laurent Jacob CNRS EMAIL Julien Mairal Inria EMAIL Univ. Grenoble Alpes, Inria, CNRS, Grenoble INP, LJK, 38000 Grenoble, France. Univ. Lyon, Université Lyon 1, CNRS, Laboratoire de Biométrie et Biologie Evolutive UMR 5558, 69000 Lyon, France |
| Pseudocode | No | The paper describes computational procedures using dynamic programming and equations (e.g., Theorem 1 and Eq. 7) but does not present them in a structured pseudocode or algorithm block. |
| Open Source Code | Yes | Pytorch code is provided with the submission and additional details given in Appendix E. |
| Open Datasets | Yes | The resulting dataset can be downloaded from http://www.bioinf.jku.at/software/LSTM_protein. |
| Dataset Splits | Yes | for each of the 85 tasks, we hold out one quarter of the training samples as a validation set, use it to tune α, gap penalty λ and the regularization parameter µ in the prediction layer. |
| Hardware Specification | No | The paper does not specify the hardware used for running the experiments (e.g., GPU/CPU models, memory). |
| Software Dependencies | No | The paper mentions 'Pytorch code' but does not specify its version or any other software dependencies with version numbers. |
| Experiment Setup | Yes | The initial learning rate for Adam is fixed to 0.05 and is halved as long as there is no decrease of the validation loss for 5 successive epochs. We fix k to 10, the number of anchor points q to 128 and use single layer CKN and RKN throughout the experiments. We train 100 epochs for each dataset. |