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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Sequence Learning Using Equilibrium Propagation
Authors: Malyaban Bal, Abhronil Sengupta
IJCAI 2023 | Venue PDF | 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 EMAIL |
| 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). |