93 citations · 153 across the 18 of their papers we have counts for
6 papers · 1 filter
On the Informativeness of Supervision Signals
Ilia Sucholutsky, Ruairidh M. Battleday, Katherine M. Collins +7
Supervised learning typically focuses on learning transferable representations from training examples annotated by humans. While rich annotations (like soft labels) carry more info…
Human-in-the-Loop Mixup
Katherine M. Collins, Umang Bhatt, Weiyang Liu +4
Aligning model representations to humans has been found to improve robustness and generalization. However, such methods often focus on standard observational data. Synthetic data i…
Analyzing Diffusion as Serial Reproduction
Raja Marjieh, Ilia Sucholutsky, Thomas A. Langlois +2
Diffusion models are a class of generative models that learn to synthesize samples by inverting a diffusion process that gradually maps data into noise. While these models have enj…
Words are all you need? Language as an approximation for human similarity judgments
Raja Marjieh, Pol van Rijn, Ilia Sucholutsky +4
Human similarity judgments are a powerful supervision signal for machine learning applications based on techniques such as contrastive learning, information retrieval, and model al…
Predicting Human Similarity Judgments Using Large Language Models
Raja Marjieh, Ilia Sucholutsky, Theodore R. Sumers +2
Similarity judgments provide a well-established method for accessing mental representations, with applications in psychology, neuroscience and machine learning. However, collecting…
Can Humans Do Less-Than-One-Shot Learning?
Maya Malaviya, Ilia Sucholutsky, Kerem Oktar +1
Being able to learn from small amounts of data is a key characteristic of human intelligence, but exactly {\em how} small? In this paper, we introduce a novel experimental paradigm…