Recent research in intuitive physics, guided by knowledge-based and learning-based approaches, shifts to a probabilistic simulation framework that better explains human intuitive physics predictions compared to earlier heuristic models.
SimCLR, a simple unsupervised contrastive learning framework, uses data augmentation for positive pairs, a nonlinear projection head, normalized temperature-scaled cross entropy loss, and large batch sizes to achieve SotA in self-supervised, semi-supervised, and transfer learning domains.
Development of artificial neural networks should leverage the insight that much of animal behavior is innate as a result of wiring rules encoded in the genome, learned through billions of years of evolution.
The Object-centric perception, prediction, and planning (OP3) framework demonstrates strong generalization to novel configurations in block stacking tasks by symmetrically processing entity representations extracted from raw visual observations.
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.
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.
A large scale, comprehensive study challenges various assumptions in learning disentangled representations, which motivates demonstrating concrete benefits in robust experimental setups in future work.
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.