Sequence Learning Using Equilibrium Propagation
Authors: Malyaban Bal, Abhronil Sengupta
IJCAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In the experiments reported in this section, we focus on benchmarking with models that are trained using BP such as LSTMs, GRUs, etc. that can be potentially implemented in a neuromorphic setting. and Table 1: Comparing our models with other models trained using BP on the IMDB & SNLI datasets. |
| Researcher Affiliation | Academia | Malyaban Bal , Abhronil Sengupta School of Electrical Engineering and Computer Science The Pennsylvania State University {mjb7906, sengupta}@psu.edu |
| Pseudocode | No | No pseudocode or clearly labeled algorithm block was found. The paper describes procedures using text and mathematical equations. |
| Open Source Code | Yes | Our implementation source code is available at https://github.com/ Neuro Comp Lab-psu/Eq Prop-Seq Learning. |
| Open Datasets | Yes | For testing our proposed work on sentiment analysis problems, we chose the IMDB Dataset and for NLI problems, we chose the Stanford Natural Language Inference (SNLI) dataset. and citations [Maas et al., 2011] and [Bowman et al., 2015]. |
| Dataset Splits | No | IMDB dataset comprises of 50K reviews, 25K for training and 25K for testing. (This only specifies train/test, not validation explicitly or implicitly through citation of a standard split including validation.) |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory amounts) used for experiments were mentioned in the paper. |
| Software Dependencies | No | No specific software dependencies with version numbers were mentioned in the paper. |
| Experiment Setup | Yes | Table 2: Hyper-parameters & Perf. Metrics for IMDB dataset. (Includes Optimal Influence Factor (β), T ( Free Phase ), K ( Nudge Phase ), Epochs, Layers (Linear & FC), Layer-wise lr, Batch Size). and Table 3: Hyper-parameters & Perf. Metrics for SNLI dataset. (Includes similar hyperparameters). |