12,784 research outputs found
CompILE: Compositional Imitation Learning and Execution
We introduce Compositional Imitation Learning and Execution (CompILE): a
framework for learning reusable, variable-length segments of
hierarchically-structured behavior from demonstration data. CompILE uses a
novel unsupervised, fully-differentiable sequence segmentation module to learn
latent encodings of sequential data that can be re-composed and executed to
perform new tasks. Once trained, our model generalizes to sequences of longer
length and from environment instances not seen during training. We evaluate
CompILE in a challenging 2D multi-task environment and a continuous control
task, and show that it can find correct task boundaries and event encodings in
an unsupervised manner. Latent codes and associated behavior policies
discovered by CompILE can be used by a hierarchical agent, where the high-level
policy selects actions in the latent code space, and the low-level,
task-specific policies are simply the learned decoders. We found that our
CompILE-based agent could learn given only sparse rewards, where agents without
task-specific policies struggle.Comment: ICML (2019
Abnormal Event Detection in Videos using Spatiotemporal Autoencoder
We present an efficient method for detecting anomalies in videos. Recent
applications of convolutional neural networks have shown promises of
convolutional layers for object detection and recognition, especially in
images. However, convolutional neural networks are supervised and require
labels as learning signals. We propose a spatiotemporal architecture for
anomaly detection in videos including crowded scenes. Our architecture includes
two main components, one for spatial feature representation, and one for
learning the temporal evolution of the spatial features. Experimental results
on Avenue, Subway and UCSD benchmarks confirm that the detection accuracy of
our method is comparable to state-of-the-art methods at a considerable speed of
up to 140 fps
The path inference filter: model-based low-latency map matching of probe vehicle data
We consider the problem of reconstructing vehicle trajectories from sparse
sequences of GPS points, for which the sampling interval is between 10 seconds
and 2 minutes. We introduce a new class of algorithms, called altogether path
inference filter (PIF), that maps GPS data in real time, for a variety of
trade-offs and scenarios, and with a high throughput. Numerous prior approaches
in map-matching can be shown to be special cases of the path inference filter
presented in this article. We present an efficient procedure for automatically
training the filter on new data, with or without ground truth observations. The
framework is evaluated on a large San Francisco taxi dataset and is shown to
improve upon the current state of the art. This filter also provides insights
about driving patterns of drivers. The path inference filter has been deployed
at an industrial scale inside the Mobile Millennium traffic information system,
and is used to map fleets of data in San Francisco, Sacramento, Stockholm and
Porto.Comment: Preprint, 23 pages and 23 figure
Quantum-inspired computational imaging
Computational imaging combines measurement and computational methods with the aim of forming images even when the measurement conditions are weak, few in number, or highly indirect. The recent surge in quantum-inspired imaging sensors, together with a new wave of algorithms allowing on-chip, scalable and robust data processing, has induced an increase of activity with notable results in the domain of low-light flux imaging and sensing. We provide an overview of the major challenges encountered in low-illumination (e.g., ultrafast) imaging and how these problems have recently been addressed for imaging applications in extreme conditions. These methods provide examples of the future imaging solutions to be developed, for which the best results are expected to arise from an efficient codesign of the sensors and data analysis tools.Y.A. acknowledges support from the UK Royal Academy of Engineering under the Research Fellowship Scheme (RF201617/16/31). S.McL. acknowledges financial support from the UK Engineering and Physical Sciences Research Council (grant EP/J015180/1). V.G. acknowledges support from the U.S. Defense Advanced Research Projects Agency (DARPA) InPho program through U.S. Army Research Office award W911NF-10-1-0404, the U.S. DARPA REVEAL program through contract HR0011-16-C-0030, and U.S. National Science Foundation through grants 1161413 and 1422034. A.H. acknowledges support from U.S. Army Research Office award W911NF-15-1-0479, U.S. Department of the Air Force grant FA8650-15-D-1845, and U.S. Department of Energy National Nuclear Security Administration grant DE-NA0002534. D.F. acknowledges financial support from the UK Engineering and Physical Sciences Research Council (grants EP/M006514/1 and EP/M01326X/1). (RF201617/16/31 - UK Royal Academy of Engineering; EP/J015180/1 - UK Engineering and Physical Sciences Research Council; EP/M006514/1 - UK Engineering and Physical Sciences Research Council; EP/M01326X/1 - UK Engineering and Physical Sciences Research Council; W911NF-10-1-0404 - U.S. Defense Advanced Research Projects Agency (DARPA) InPho program through U.S. Army Research Office; HR0011-16-C-0030 - U.S. DARPA REVEAL program; 1161413 - U.S. National Science Foundation; 1422034 - U.S. National Science Foundation; W911NF-15-1-0479 - U.S. Army Research Office; FA8650-15-D-1845 - U.S. Department of the Air Force; DE-NA0002534 - U.S. Department of Energy National Nuclear Security Administration)Accepted manuscrip
Transferable neural networks for enhanced sampling of protein dynamics
Variational auto-encoder frameworks have demonstrated success in reducing
complex nonlinear dynamics in molecular simulation to a single non-linear
embedding. In this work, we illustrate how this non-linear latent embedding can
be used as a collective variable for enhanced sampling, and present a simple
modification that allows us to rapidly perform sampling in multiple related
systems. We first demonstrate our method is able to describe the effects of
force field changes in capped alanine dipeptide after learning a model using
AMBER99. We further provide a simple extension to variational dynamics encoders
that allows the model to be trained in a more efficient manner on larger
systems by encoding the outputs of a linear transformation using time-structure
based independent component analysis (tICA). Using this technique, we show how
such a model trained for one protein, the WW domain, can efficiently be
transferred to perform enhanced sampling on a related mutant protein, the GTT
mutation. This method shows promise for its ability to rapidly sample related
systems using a single transferable collective variable and is generally
applicable to sets of related simulations, enabling us to probe the effects of
variation in increasingly large systems of biophysical interest.Comment: 20 pages, 10 figure
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