Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Stochastic Multiple Target Sampling Gradient Descent
Authors: Hoang Phan, Ngoc Tran, Trung Le, Toan Tran, Nhat Ho, Dinh Phung
NeurIPS 2022 | Venue PDF | LLM Run Details | Input Tokens: 16,945 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 2,981 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we conduct comprehensive experiments to demonstrate the merit of our approach to multi-task learning. |
| Researcher Affiliation | Collaboration | 1 Vin AI Research, Vietnam 2 Monash University, Australia 3 University of Texas, Austin |
| Pseudocode | Yes | Algorithm 1 Pseudocode for MT-SGD. Input: Multiple unnormalized target densities p1:K. Output: The optimal particles θ1, θ2, . . . , θM. ... Algorithm 2 Pseudocode for multi-task learning MT-SGD. Input: A training set D = {(xi, yi1, ..., yi K)}N i=1. Output: The models θm = θj m K j=1 with m = 1, ..., M, where θj m = αm, βj m . |
| Open Source Code | Yes | Our codes are available at https://github.com/VietHoang1512/MT-SGD. |
| Open Datasets | Yes | Our method is validated on different benchmark datasets: (i) Multi-Fashion+MNIST [23], (ii) Multi-MNIST, and (iii) Multi-Fashion. Each of them consists of 120,000 training and 20,000 testing images generated from MNIST [12] and Fashion MNIST [27] by overlaying an image on top of another: one in the top-left corner and one in the bottom-right corner. |
| Dataset Splits | No | The paper specifies training and testing image counts but does not provide an explicit validation dataset split or count in the main text. |
| Hardware Specification | No | The detailed training and configuration are given in the supplementary material. ... Did you include the total amount of compute and the type of resources used (e.g., type of GPUs, internal cluster, or cloud provider)? [Yes] The detailed training environment will be attached in the supplementary material. |
| Software Dependencies | No | The tools used in our implementation are presented in our supplementary material. |
| Experiment Setup | No | The detailed training and configuration are given in the supplementary material. |