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..
Interpretable Tensor Fusion
Authors: Saurabh Varshneya, Antoine Ledent, Philipp Liznerski, Andriy Balinskyy, Purvanshi Mehta, Waleed Mustafa, Marius Kloft
IJCAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on six realworld datasets show that In Tense outperforms existing state-of-the-art multimodal interpretable approaches in terms of accuracy and interpretability. |
| Researcher Affiliation | Collaboration | Saurabh Varshneya1 , Antoine Ledent2 , Philipp Liznerski1 , Andriy Balinskyy1 , Purvanshi Mehta3 , Waleed Mustafa1 and Marius Kloft1 1RPTU Kaiserslautern-Landau 2Singapore Management University 3Lica World |
| Pseudocode | No | The paper does not contain any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Full paper with technical appendix and code is available at: https://arxiv.org/abs/2405.04671 |
| Open Datasets | Yes | To evaluate In Tense in sentiment analysis, we employed CMU-MOSEI [Bagher Zadeh et al., 2018], the largest dataset of sentence-level sentiment analysis for real-world online videos, and CMU-MOSI [Zadeh et al., 2016], a collection of annotated opinion video clips... To assess our approach s effectiveness in these tasks, we utilized UR-FUNNY [Hasan et al., 2019] for humor detection and MUSt ARD [Castro et al., 2019] for sarcasm detection... For this paper, we considered the ENRICO [Leiva et al., 2020] dataset as an example for layout design categorization. We also include results for Audiovision-MNIST (AV-MNIST) [Vielzeuf et al., 2018], a multimodal dataset comprising images of handwritten and recordings of spoken digits. |
| Dataset Splits | Yes | In order to compare performance and ensure reproducibility, we followed the experimental setup (e.g., data preprocessing, encodings of different modalities) of the Multi Bench [Liang et al., 2021] benchmark for all the experiments. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running experiments (e.g., GPU/CPU models, memory specifications). |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., 'Python 3.8, PyTorch 1.9'). |
| Experiment Setup | No | The paper states that it 'followed the experimental setup (e.g., data preprocessing, encodings of different modalities) of the Multi Bench [Liang et al., 2021] benchmark', but it does not explicitly list concrete hyperparameter values or detailed training configurations within its own text. |