Search Results for author:
Found 15 papers, 5 papers with code
Around the world in 60 words: A generative vocabulary test for online research
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.
What Language Reveals about Perception: Distilling Psychophysical Knowledge from Large Language Models
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.
Alignment with human representations supports robust few-shot learning
Should we care whether AI systems have representations of the world that are similar to those of humans?
Human-in-the-Loop Mixup
We focus on the synthetic data used in mixup: a powerful regularizer shown to improve model robustness, generalization, and calibration.
On the Informativeness of Supervision Signals
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.
Analyzing Diffusion as Serial Reproduction
Diffusion models are a class of generative models that learn to synthesize samples by inverting a diffusion process that gradually maps data into noise.
Words are all you need? Language as an approximation for human similarity judgments
Based on the results of this comprehensive study, we provide a concise guide for researchers interested in collecting or approximating human similarity data.
Predicting Human Similarity Judgments Using Large Language Models
Similarity judgments provide a well-established method for accessing mental representations, with applications in psychology, neuroscience and machine learning.
Can Humans Do Less-Than-One-Shot Learning?
Being able to learn from small amounts of data is a key characteristic of human intelligence, but exactly {\em how} small?
One Line To Rule Them All: Generating LO-Shot Soft-Label Prototypes
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.
Optimal 1-NN Prototypes for Pathological Geometries
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.
SecDD: Efficient and Secure Method for Remotely Training Neural Networks
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.
'Less Than One'-Shot Learning: Learning N Classes From M<N Samples
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.
Soft-Label Dataset Distillation and Text Dataset Distillation
We propose to simultaneously distill both images and their labels, thus assigning each synthetic sample a `soft' label (a distribution of labels).
Deep Learning for System Trace Restoration
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.
