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 [1].
DeepSITH: Efficient Learning via Decomposition of What and When Across Time Scales
Authors: Brandon Jacques, Zoran Tiganj, Marc Howard, Per B Sederberg
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We compare Deep SITH to LSTMs and other recent RNNs on several time series prediction and decoding tasks. Deep SITH achieves results comparable to state-of-the-art performance on these problems and continues to perform well even as the delays are increased. |
| Researcher Affiliation | Academia | Brandon G. Jacques Department of Psychology University Of Virginia EMAIL Tiganj Department of Computer Science Indiana University EMAIL W. Howard Department of Psychological and Brain Sciences Boston University EMAIL B. Sederberg Department of Psychology University of Virginia EMAIL |
| Pseudocode | No | No structured pseudocode or algorithm blocks were found in the paper. |
| Open Source Code | Yes | We provide all the code for Deep SITH and the subesquent analysis in our github here. |
| Open Datasets | Yes | In the MNIST task [22], handwritten numerical digits can be identi๏ฌed by neural networks with almost 100% accuracy utilizing a convolutional neural network (CNN). |
| Dataset Splits | Yes | The Deep SITH network is trained with a batch size of 64, with a cross entropy loss function, with a training/test split of 80%-20%. |
| Hardware Specification | No | We did not ran anything that could not be ran on a single GPU |
| Software Dependencies | No | The paper mentions "Py Torch machine learning framework [19]" but does not provide specific version numbers for PyTorch or any other software dependencies. |
| Experiment Setup | Yes | Table 1 summarizes the hyperparameters used in the experiments. ... The Deep SITH network is trained with a batch size of 64, with a cross entropy loss function, with a training/test split of 80%-20%. In between each layer we applied batch normalization, and applied a step learning rate annealing after every third of the training epochs (2e-3, 2e-4, 2e-5). |