ConvNets and ImageNet Beyond Accuracy: Understanding Mistakes and Uncovering Biases

Demonstrates that providing explantions and model criticism can be useful tools to improve the reliability of ImageNet-trained CNNs for end-users.

Intuitive experimentation in the physical world

Provides evidence that human experimentation in physical environments is effective at revealing properties of interest, and learning from observations relies on the learning goals.

The Developing Infant Creates a Curriculum for Statistical Learning

Reviews recent work that analyzes the egocentric view of infants, highlighting the connection between the data and internal machinery for statistical learning.

Adversarial Examples that Fool both Computer Vision and Time-Limited Humans

Adversarial examples trained on an ensemble of CNNs with a retinal preprocessing layer reduce the accuracy of time-limited humans in a two alternative forced choice task.

Toddler-Inspired Visual Object Learning

Computer vision models trained on data obtained from head-mounted cameras on children performs better than data from adults.

Automatic Goal Generation for Reinforcement Learning Agents

Applies curriculum learning to a RL context to achieve policies.