166 research outputs found
Informed Proposal Monte Carlo
Any search or sampling algorithm for solution of inverse problems needs
guidance to be efficient. Many algorithms collect and apply information about
the problem on the fly, and much improvement has been made in this way.
However, as a consequence of the the No-Free-Lunch Theorem, the only way we can
ensure a significantly better performance of search and sampling algorithms is
to build in as much information about the problem as possible. In the special
case of Markov Chain Monte Carlo sampling (MCMC) we review how this is done
through the choice of proposal distribution, and we show how this way of adding
more information about the problem can be made particularly efficient when
based on an approximate physics model of the problem. A highly nonlinear
inverse scattering problem with a high-dimensional model space serves as an
illustration of the gain of efficiency through this approach
What Will I Do Next? The Intention from Motion Experiment
In computer vision, video-based approaches have been widely explored for the
early classification and the prediction of actions or activities. However, it
remains unclear whether this modality (as compared to 3D kinematics) can still
be reliable for the prediction of human intentions, defined as the overarching
goal embedded in an action sequence. Since the same action can be performed
with different intentions, this problem is more challenging but yet affordable
as proved by quantitative cognitive studies which exploit the 3D kinematics
acquired through motion capture systems. In this paper, we bridge cognitive and
computer vision studies, by demonstrating the effectiveness of video-based
approaches for the prediction of human intentions. Precisely, we propose
Intention from Motion, a new paradigm where, without using any contextual
information, we consider instantaneous grasping motor acts involving a bottle
in order to forecast why the bottle itself has been reached (to pass it or to
place in a box, or to pour or to drink the liquid inside). We process only the
grasping onsets casting intention prediction as a classification framework.
Leveraging on our multimodal acquisition (3D motion capture data and 2D optical
videos), we compare the most commonly used 3D descriptors from cognitive
studies with state-of-the-art video-based techniques. Since the two analyses
achieve an equivalent performance, we demonstrate that computer vision tools
are effective in capturing the kinematics and facing the cognitive problem of
human intention prediction.Comment: 2017 IEEE Conference on Computer Vision and Pattern Recognition
Workshop
Excitation Backprop for RNNs
Deep models are state-of-the-art for many vision tasks including video action
recognition and video captioning. Models are trained to caption or classify
activity in videos, but little is known about the evidence used to make such
decisions. Grounding decisions made by deep networks has been studied in
spatial visual content, giving more insight into model predictions for images.
However, such studies are relatively lacking for models of spatiotemporal
visual content - videos. In this work, we devise a formulation that
simultaneously grounds evidence in space and time, in a single pass, using
top-down saliency. We visualize the spatiotemporal cues that contribute to a
deep model's classification/captioning output using the model's internal
representation. Based on these spatiotemporal cues, we are able to localize
segments within a video that correspond with a specific action, or phrase from
a caption, without explicitly optimizing/training for these tasks.Comment: CVPR 2018 Camera Ready Versio
Excitation Dropout: Encouraging Plasticity in Deep Neural Networks
We propose a guided dropout regularizer for deep networks based on the
evidence of a network prediction defined as the firing of neurons in specific
paths. In this work, we utilize the evidence at each neuron to determine the
probability of dropout, rather than dropping out neurons uniformly at random as
in standard dropout. In essence, we dropout with higher probability those
neurons which contribute more to decision making at training time. This
approach penalizes high saliency neurons that are most relevant for model
prediction, i.e. those having stronger evidence. By dropping such high-saliency
neurons, the network is forced to learn alternative paths in order to maintain
loss minimization, resulting in a plasticity-like behavior, a characteristic of
human brains too. We demonstrate better generalization ability, an increased
utilization of network neurons, and a higher resilience to network compression
using several metrics over four image/video recognition benchmarks
A Deep Learning approach to Reduced Order Modelling of Parameter Dependent Partial Differential Equations
Within the framework of parameter dependent PDEs, we develop a constructive
approach based on Deep Neural Networks for the efficient approximation of the
parameter-to-solution map. The research is motivated by the limitations and
drawbacks of state-of-the-art algorithms, such as the Reduced Basis method,
when addressing problems that show a slow decay in the Kolmogorov n-width. Our
work is based on the use of deep autoencoders, which we employ for encoding and
decoding a high fidelity approximation of the solution manifold. In order to
fully exploit the approximation capabilities of neural networks, we consider a
nonlinear version of the Kolmogorov n-width over which we base the concept of a
minimal latent dimension. We show that this minimal dimension is intimately
related to the topological properties of the solution manifold, and we provide
some theoretical results with particular emphasis on second order elliptic
PDEs. Finally, we report numerical experiments where we compare the proposed
approach with classical POD-Galerkin reduced order models. In particular, we
consider parametrized advection-diffusion PDEs, and we test the methodology in
the presence of strong transport fields, singular terms and stochastic
coefficients
HMCLab: a framework for solving diverse geophysical inverse problems using the Hamiltonian Monte Carlo method
The use of the probabilistic approach to solve inverse problems is becoming
more popular in the geophysical community, thanks to its ability to address
nonlinear forward problems and to provide uncertainty quantification. However,
such strategy is often tailored to specific applications and therefore there is
a lack of a common platform for solving a range of different geophysical
inverse problems and showing potential and pitfalls. We demonstrate a common
framework to solve such inverse problems ranging from, e.g, earthquake source
location to potential field data inversion and seismic tomography. Within this
approach, we can provide probabilities related to certain properties or
structures of the subsurface. Thanks to its ability to address high-dimensional
problems, the Hamiltonian Monte Carlo (HMC) algorithm has emerged as the
state-of-the-art tool for solving geophysical inverse problems within the
probabilistic framework. HMC requires the computation of gradients, which can
be obtained by adjoint methods, making the solution of tomographic problems
ultimately feasible. These results can be obtained with "HMCLab", a tool for
solving a range of different geophysical inverse problems using sampling
methods, focusing in particular on the HMC algorithm. HMCLab consists of a set
of samplers and a set of geophysical forward problems. For each problem its
misfit function and gradient computation are provided and, in addition, a set
of prior models can be combined to inject additional information into the
inverse problem. This allows users to experiment with probabilistic inverse
problems and also address real-world studies. We show how to solve a selected
set of problems within this framework using variants of the HMC algorithm and
analyze the results. HMCLab is provided as an open source package written both
in Python and Julia, welcoming contributions from the community.Comment: 21 pages, 4 figure
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