Multi-Objective Deep Learning with Adaptive Reference Vectors
Authors: Weiyu Chen, James Kwok
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on an extensive set of learning scenarios demonstrate the superiority of the proposed algorithm over the state-of-the-art. 4 Experiments In this section, extensive experiments are performed, including synthetic problems (Section 4.1), multi-task learning (Section 4.2), accuracy-fairness trade-off (Section 4.3), and usage on larger networks (Section 4.4). Finally, ablation study is presented in Section 4.5. |
| Researcher Affiliation | Academia | Weiyu Chen James T. Kwok Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong {wchenbx, jamesk}@cse.ust.hk |
| Pseudocode | Yes | Algorithm 1 Gradient-based Multi-Objective Optimization with Adaptive Reference vectors (GMOOAR). |
| Open Source Code | No | The paper does not contain an explicit statement about open-sourcing its code or provide a link to a code repository for the described methodology. |
| Open Datasets | Yes | In this experiment, we use three benchmark datasets from [31]: Multi-MNIST, Multi-Fashion, and Multi-Fashion+MNIST. [...] we aim to achieve both high accuracy and fairness on three tabular datasets: Adult [16], Compass [1], and Default [51]. [...] selected from the 40 tasks in Celeb A [35]. |
| Dataset Splits | Yes | They are evaluated on the validation set every 5 epochs. We only keep the solutions of iteration kbest as the final solution set, where kbest is the iteration that yields the solution set with the largest validation HV. |
| Hardware Specification | Yes | All experiments are conducted on an RTX-2080Ti with 11GB memory. |
| Software Dependencies | No | The paper mentions using a 'neural network' and 'Le Net' but does not specify any software frameworks (like PyTorch, TensorFlow) or their version numbers, nor any other libraries with versions. |
| Experiment Setup | Yes | As in [40], a neural network (with 2 hidden layers, each with 20 units) is used. Following common practice [44], we obtain a set of n solutions in each iteration (with n = 15 in all experiments). For EPO, PHN-LS, PHN-EPO and COSMOS, we generate reference vectors following the strategy in [44]. For GMOOAR, the reference vectors are initialized randomly. |