Stochastic Prediction of Multi-Agent Interactions from Partial Observations

We present a method that learns to integrate temporal information, from a learned dynamics model, with ambiguous visual information, from a learned vision model, in the context of interacting agents. Our method is based on a graph-structured variational recurrent neural network (Graph-VRNN), which is trained end-to-end to infer the current state of the (partially observed) world, as well as to forecast future states. We show that our method outperforms various baselines on two sports datasets, one based on real basketball trajectories, and one generated by a soccer game engine.Comment: ICLR 2019 camera read

Unsupervised Discovery of Parts, Structure, and Dynamics

Humans easily recognize object parts and their hierarchical structure by watching how they move; they can then predict how each part moves in the future. In this paper, we propose a novel formulation that simultaneously learns a hierarchical, disentangled object representation and a dynamics model for object parts from unlabeled videos. Our Parts, Structure, and Dynamics (PSD) model learns to, first, recognize the object parts via a layered image representation; second, predict hierarchy via a structural descriptor that composes low-level concepts into a hierarchical structure; and third, model the system dynamics by predicting the future. Experiments on multiple real and synthetic datasets demonstrate that our PSD model works well on all three tasks: segmenting object parts, building their hierarchical structure, and capturing their motion distributions.Comment: ICLR 2019. The first two authors contributed equally to this wor

Exposing piaget's scheme: Empirical evidence for the ontogenesis of coordination in learning a mathematical concept

The combination of two methodological resources-natural-user interfaces (NUI) and multimodal learning analytics (MMLA)-is creating opportunities for educational researchers to empirically evaluate seminal models for the hypothetical emergence of concepts from situated sensorimotor activity. 76 participants (9-14 yo) solved tablet-based non-symbolic manipulation tasks designed to foster grounded meanings for the mathematical concept of proportional equivalence. Data gathered in task-based semi-structured clinical interviews included action logging, eye-gaze tracking, and videography. Successful task performance coincided with spontaneous appearance of stable dynamical gaze-path patterns soon followed by multimodal articulation of strategy. Significantly, gaze patterns included uncued non-salient screen locations. We present cumulative results to argue that these 'attentional anchors' mediated participants' problem solving. We interpret the findings as enabling us to revisit, support, refine, and elaborate on central claims of Piaget's theory of genetic epistemology and in particular his insistence on the role of situated motor-action coordination in the process of reflective abstraction