Machine learning with artificial neural networks is revolutionizing science. The most advanced challenges require discovering answers autonomously. In the domain of reinforcement learning, control strategies are improved according to a reward function. The power of neural-network-based reinforcement learning has been highlighted by spectacular recent successes such as playing Go, but its benefits for physics are yet to be demonstrated. Here, we show how a network-based "agent" can discover complete quantum-error-correction strategies, protecting a collection of qubits against noise. These strategies require feedback adapted to measurement outcomes. Finding them from scratch without human guidance and tailored to different hardware resources is a formidable challenge due to the combinatorially large search space. To solve this challenge, we develop two ideas: two-stage learning with teacher and student networks and a reward quantifying the capability to recover the quantum information stored in a multiqubit system. Beyond its immediate impact on quantum computation, our work more generally demonstrates the promise of neural-network-based reinforcement learning in physics.
Organizers: Matthias Bauer
Since Hubel and Wiesel's seminal findings in the primary visual cortex (V1) more than 50 years ago, progress in vision science has been very limited along previous frameworks and schools of thoughts on understanding vision. Have we been asking the right questions? I will show observations motivating the new path. First, a drastic information bottleneck forces the brain to process only a tiny fraction of the massive visual input information; this selection is called the attentional selection, how to select this tiny fraction is critical. Second, a large body of evidence has been accumulating to suggest that the primary visual cortex (V1) is where this selection starts, suggesting that the visual cortical areas along the visual pathway beyond V1 must be investigated in light of this selection in V1. Placing attentional selection as the center stage, a new path to understanding vision is proposed (articulated in my book "Understanding vision: theory, models, and data", Oxford University Press 2014). I will show a first example of using this new path, which aims to ask new questions and make fresh progresses. I will relate our insights to artificial vision systems to discuss issues like top-down feedbacks in hierachical processing, analysis-by-synthesis, and image understanding.
I will survey our work on tracking and measurement, waypoints on the path to activity recognition and understanding, in sports video, highlighting some of our recent work on rectification and player tracking, not just in hockey but more recently in basketball, where we have addressed player identification both in a fully supervised and semi-supervised manner.
Methods for visual recognition have made dramatic strides in recent years on various online benchmarks, but performance in the real world still often falters. Classic gradient-histogram models make overly simplistic assumptions regarding image appearance statistics, both locally and globally. Recent progress suggests that new learning-based representations can improve recognition by devices that are embedded in a physical world.
I'll review new methods for domain adaptation which capture the visual domain shift between environments, and improve recognition of objects in specific places when trained from generic online sources. I'll discuss methods for cross-modal semi-supervised learning, which can leverage additional unlabeled modalities in a test environment.
Finally as time permits I'll present recent results learning hierarchical local image representations based on recursive probabilistic topic models, on learning strong object color models from sets of uncalibrated views using a new multi-view color constancy paradigm, and/or on recent results on monocular estimation of grasp affordances.
In the first part of the talk, I will describe methods that learn a single family of detectors for object classes that exhibit large within-class variation. One common solution is to use a divide-and-conquer strategy, where the space of possible within-class variations is partitioned, and different detectors are trained for different partitions.
However, these discrete partitions tend to be arbitrary in continuous spaces, and the classifiers have limited power when there are too few training samples in each subclass. To address this shortcoming, explicit feature sharing has been proposed, but it also makes training more expensive. We show that foreground-background classification (detection) and within-class classification of the foreground class (pose estimation) can be jointly solved in a multiplicative form of two kernel functions. One kernel measures similarity for foreground-background classification. The other kernel accounts for latent factors that control within-class variation and implicitly enables feature sharing among foreground training samples. The multiplicative kernel formulation enables feature sharing implicitly; the solution for the optimal sharing is a byproduct of SVM learning.
The resulting detector family is tuned to specific variations in the foreground. The effectiveness of this framework is demonstrated in experiments that involve detection, tracking, and pose estimation of human hands, faces, and vehicles in video.
Beginning with a seminal paper of Diaconis (1988), the aim of so-called "probabilistic numerics" is to compute probabilistic solutions to deterministic problems arising in numerical analysis by casting them as statistical inference problems. For example, numerical integration of a deterministic function can be seen as the integration of an unknown/random function, with evaluations of the integrand at the integration nodes proving partial information about the integrand. Advantages offered by this viewpoint include: access to the Bayesian representation of prior and posterior uncertainties; better propagation of uncertainty through hierarchical systems than simple worst-case error bounds; and appropriate accounting for numerical truncation and round-off error in inverse problems, so that the replicability of deterministic simulations is not confused with their accuracy, thereby yielding an inappropriately concentrated Bayesian posterior. This talk will describe recent work on probabilistic numerical solvers for ordinary and partial differential equations, including their theoretical construction, convergence rates, and applications to forward and inverse problems. Joint work with Andrew Stuart (Warwick).
Organizers: Philipp Hennig