Excessive Invariance Causes Adversarial Vulnerability
Uses bijective networks to identify large subspaces of invariance-based vulnerability and introduces the independence cross-entropy loss which partially alleviates it.
On The Power of Curriculum Learning in Training Deep Networks
Demonstrates the benefit of curriculum learning with different scoring and pacing functions on various small datasets.
Learning Transferable Visual Models From Natural Language Supervision
Applies task-agnostic, web-scale pre-training to computer vision using natural language supervision, enabling powerful zero-shot transfer to many datasets.
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.
Could a Neuroscientist Understand a Microprocessor?
Addresses a popular belief in neuroscience that the field is primarily data limited by using a microprocessor as a model organism and applying modern data analysis methods from neuroscience to understand its information processing, with generally poor results.
Humans, but Not Deep Neural Networks, Often Miss Giant Targets in Scenes
Demonstrates that huamns use scene information to guide search towards likely target sizes, resulting in higher miss rates for mis-scaled targets, which does not occur for object detection DNNs.
Embodied Intelligence via Learning and Evolution
Large-scale evolutionary simulations by DERL yield insights into how the interaction between learning, evolution, and environmental complexity can lead to morphological intelligence.
