Artificial Intelligence

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.

Control What You Can: Intrinsically Motivated Task-Planning Agent

The Control What You Can (CWYC) method learns to control components of the environment to achieve multi-step goals by combining task planning with surprise and learning progress based intrinsic motivation.

Learning Physical Graph Representations from Visual Scenes

Introduces a novel hierarchical representation of visual scenes, Physical Scene Graphs (PSGs), as well as a network for learning them from RGB movies, PSGNet, which outperforms other unsupervised methods in scene segmentation.

Compositional Video Prediction

Novel method for video prediction from a single frame by decomposing the scene into entities with location and appearance features, capturing ambiguities with a global latent variable.

Embodied Multimodal Multitask Learning

Proposes multitask model to jointly learn semantic goal navigation and embodied question answering.

Automated Curriculum Learning for Neural Networks

Investigates automatically generating curricula based on a variety of progress signals that are computed for each data sample.

Automatic Goal Generation for Reinforcement Learning Agents

Applies curriculum learning to a RL context to achieve policies.