no code implementations • 3 Feb 2023 • Pol van Rijn, Yue Sun, Harin Lee, Raja Marjieh, Ilia Sucholutsky, Francesca Lanzarini, Elisabeth André, Nori Jacoby
Six behavioral experiments (N=236) in six countries and eight languages show that (a) our test can distinguish between native speakers of closely related languages, (b) the test is reliable ($r=0. 82$), and (c) performance strongly correlates with existing tests (LexTale) and self-reports.
no code implementations • 2 Feb 2023 • Raja Marjieh, Ilia Sucholutsky, Pol van Rijn, Nori Jacoby, Thomas L. Griffiths
We reformulate this problem as that of distilling psychophysical information from text and show how this can be done by combining large language models (LLMs) with a classic psychophysical method based on similarity judgments.
no code implementations • 27 Jan 2023 • Ilia Sucholutsky, Thomas L. Griffiths
Should we care whether AI systems have representations of the world that are similar to those of humans?
no code implementations • 2 Nov 2022 • Katherine M. Collins, Umang Bhatt, Weiyang Liu, Vihari Piratla, Ilia Sucholutsky, Bradley Love, Adrian Weller
We focus on the synthetic data used in mixup: a powerful regularizer shown to improve model robustness, generalization, and calibration.
no code implementations • 2 Nov 2022 • Ilia Sucholutsky, Raja Marjieh, Nori Jacoby, Thomas L. Griffiths
Learning transferable representations by training a classifier is a well-established technique in deep learning (e. g., ImageNet pretraining), but it remains an open theoretical question why this kind of task-specific pre-training should result in ''good'' representations that actually capture the underlying structure of the data.
no code implementations • 29 Sep 2022 • Raja Marjieh, Ilia Sucholutsky, Thomas A. Langlois, Nori Jacoby, Thomas L. Griffiths
Diffusion models are a class of generative models that learn to synthesize samples by inverting a diffusion process that gradually maps data into noise.
no code implementations • 8 Jun 2022 • Raja Marjieh, Pol van Rijn, Ilia Sucholutsky, Theodore R. Sumers, Harin Lee, Thomas L. Griffiths, Nori Jacoby
Based on the results of this comprehensive study, we provide a concise guide for researchers interested in collecting or approximating human similarity data.
no code implementations • 9 Feb 2022 • Raja Marjieh, Ilia Sucholutsky, Theodore R. Sumers, Nori Jacoby, Thomas L. Griffiths
Similarity judgments provide a well-established method for accessing mental representations, with applications in psychology, neuroscience and machine learning.
no code implementations • 9 Feb 2022 • Maya Malaviya, Ilia Sucholutsky, Kerem Oktar, Thomas L. Griffiths
Being able to learn from small amounts of data is a key characteristic of human intelligence, but exactly {\em how} small?
2 code implementations • 15 Feb 2021 • Ilia Sucholutsky, Nam-Hwui Kim, Ryan P. Browne, Matthias Schonlau
We propose a novel, modular method for generating soft-label prototypical lines that still maintains representational accuracy even when there are fewer prototypes than the number of classes in the data.
2 code implementations • 31 Oct 2020 • Ilia Sucholutsky, Matthias Schonlau
Using prototype methods to reduce the size of training datasets can drastically reduce the computational cost of classification with instance-based learning algorithms like the k-Nearest Neighbour classifier.
1 code implementation • 19 Sep 2020 • Ilia Sucholutsky, Matthias Schonlau
We leverage what are typically considered the worst qualities of deep learning algorithms - high computational cost, requirement for large data, no explainability, high dependence on hyper-parameter choice, overfitting, and vulnerability to adversarial perturbations - in order to create a method for the secure and efficient training of remotely deployed neural networks over unsecured channels.
4 code implementations • 17 Sep 2020 • Ilia Sucholutsky, Matthias Schonlau
We propose the `less than one'-shot learning task where models must learn $N$ new classes given only $M<N$ examples and we show that this is achievable with the help of soft labels.
4 code implementations • 6 Oct 2019 • Ilia Sucholutsky, Matthias Schonlau
We propose to simultaneously distill both images and their labels, thus assigning each synthetic sample a `soft' label (a distribution of labels).
no code implementations • 10 Apr 2019 • Ilia Sucholutsky, Apurva Narayan, Matthias Schonlau, Sebastian Fischmeister
The output of the model will be a close reconstruction of the true data, and can be fed to algorithms that rely on clean data.