Search Results for author:
Found 6 papers, 4 papers with code
Visual cognition in multimodal large language models
A chief goal of artificial intelligence is to build machines that think like people.
The Acquisition of Physical Knowledge in Generative Neural Networks
As children grow older, they develop an intuitive understanding of the physical processes around them.
Stochastic Gradient Descent Captures How Children Learn About Physics
We find that the model's learning trajectory captures the developmental trajectories of children, thereby providing support to the idea of development as stochastic optimization.
Trivial or impossible -- dichotomous data difficulty masks model differences (on ImageNet and beyond)
We find that the ImageNet validation set, among others, suffers from dichotomous data difficulty (DDD): For the range of investigated models and their accuracies, it is dominated by 46. 0% "trivial" and 11. 5% "impossible" images (beyond label errors).
Trivial or Impossible --- dichotomous data difficulty masks model differences (on ImageNet and beyond)
We find that the ImageNet validation set, among others, suffers from dichotomous data difficulty (DDD): For the range of investigated models and their accuracies, it is dominated by 46. 0% ``trivial'' and 11. 5% ``impossible'' images (beyond label errors).
ImageNet suffers from dichotomous data difficulty
We find that the ImageNet validation set suffers from dichotomous data difficulty (DDD): For the range of investigated models and their accuracies, it is dominated by 46. 3% "trivial" and 11. 3% "impossible" images.
