Summary of Can Transformers Smell Like Humans?, by Farzaneh Taleb et al.
Can Transformers Smell Like Humans?
by Farzaneh Taleb, Miguel Vasco, Antônio H. Ribeiro, Mårten Björkman, Danica Kragic
First submitted to arxiv on: 5 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 The paper investigates whether pre-trained transformer models of chemical structures can encode representations that align with human olfactory perception. It demonstrates that such representations are highly aligned with human olfactory perception, using multiple datasets and perceptual representations to show that they can predict labels, continuous ratings, and similarity ratings associated with odorants provided by experts and human participants. The alignment is also found to be associated with physicochemical features of odorants relevant for olfactory decoding. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study shows how pre-trained transformer models can help us understand and work with our sense of smell. Scientists used these models to see if they could predict what people think about different smells, based on the chemical structure of those smells. They found that the models are really good at doing this! This is important because it helps us understand how we perceive smells in the first place. |
Keywords
» Artificial intelligence » Alignment » Transformer
