Summary of Multi-objective Deep Learning: Taxonomy and Survey Of the State Of the Art, by Sebastian Peitz and Sedjro Salomon Hotegni
Multi-objective Deep Learning: Taxonomy and Survey of the State of the Art
by Sebastian Peitz, Sedjro Salomon Hotegni
First submitted to arxiv on: 2 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Optimization and Control (math.OC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper surveys recent advancements in multi-objective deep learning, which considers multiple objectives simultaneously to achieve various benefits like multi-task learning or sparsity. The focus on deep learning architectures poses challenges due to large numbers of parameters, strong nonlinearities, and stochasticity. To address these challenges, the paper introduces a taxonomy of existing methods based on training algorithms and decision-maker needs, covering recent advancements, successful applications, and all three main learning paradigms: supervised, unsupervised, and reinforcement learning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how machine learning models can learn to do multiple things at once. This is helpful for many reasons, like making AI better at doing different tasks or using less data. However, when we use very complex deep learning models, it gets harder because there are so many parts to the model and it’s hard to predict what will happen. The paper shows what new ideas have come up recently in this area and how they’ve been used to solve real-world problems. |
Keywords
» Artificial intelligence » Deep learning » Machine learning » Multi task » Reinforcement learning » Supervised » Unsupervised
