Recurrent Kernel Networks
Authors: Dexiong Chen, Laurent Jacob, Julien Mairal
NeurIPS 2019 | Conference 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 dexiong.chen@inria.fr Laurent Jacob CNRS laurent.jacob@univ-lyon1.fr Julien Mairal Inria julien.mairal@inria.fr 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. |