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..
What Makes Multi-Modal Learning Better than Single (Provably)
Authors: Yu Huang, Chenzhuang Du, Zihui Xue, Xuanyao Chen, Hang Zhao, Longbo Huang
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 5 Experiment We conduct experiments to validate our theoretical results. The source of the data we consider is two-fold, multi-modal real-world dataset and well-designed generated dataset. |
| Researcher Affiliation | Academia | Yu Huang1, , Chenzhuang Du1,*, Zihui Xue2, Xuanyao Chen3,4, Hang Zhao1, Longbo Huang1, 1 Institute for Interdisciplinary Information Sciences, Tsinghua University 2 The University of Texas at Austin 3 Fudan University 4 Shanghai Qi Zhi Institute |
| Pseudocode | No | The paper does not contain any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access to source code for the methodology described. |
| Open Datasets | Yes | The natural dataset we use is the Interactive Emotional Dyadic Motion Capture (IEMOCAP) database, which is an acted multi-modal and multi-speaker database [6]. |
| Dataset Splits | No | The paper mentions 13200 data for training and 3410 for testing, but does not specify a validation set or its size, nor does it describe the splitting methodology in detail for reproducibility. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. It only mentions training settings like optimizer and batch size. |
| Software Dependencies | No | The paper mentions using "Adam [25] as the optimizer" but does not provide specific version numbers for any software libraries, programming languages, or other dependencies. |
| Experiment Setup | Yes | For all experiments on IEMOCAP, we use one linear neural network layer to extract the latent feature, and we set the hidden dimension to be 128. In multi-modal network, different modalities do not share encoders and we concatenate the features first, and then map the feature to the task space. We use Adam [25] as the optimizer and set the learning rate to be 0.01, with other hyper-parameters default. The batch size is 2048 for the data. For this classification task, the top-1 accuracy is used for performance measurement. |