6,763 research outputs found
Alternating Back-Propagation for Generator Network
This paper proposes an alternating back-propagation algorithm for learning
the generator network model. The model is a non-linear generalization of factor
analysis. In this model, the mapping from the continuous latent factors to the
observed signal is parametrized by a convolutional neural network. The
alternating back-propagation algorithm iterates the following two steps: (1)
Inferential back-propagation, which infers the latent factors by Langevin
dynamics or gradient descent. (2) Learning back-propagation, which updates the
parameters given the inferred latent factors by gradient descent. The gradient
computations in both steps are powered by back-propagation, and they share most
of their code in common. We show that the alternating back-propagation
algorithm can learn realistic generator models of natural images, video
sequences, and sounds. Moreover, it can also be used to learn from incomplete
or indirect training data
Globally-Coordinated Locally-Linear Modeling of Multi-Dimensional Data
This thesis considers the problem of modeling and analysis of continuous, locally-linear, multi-dimensional spatio-temporal data. Our work extends the previously reported theoretical work on the global coordination model to temporal analysis of continuous, multi-dimensional data. We have developed algorithms for time-varying data analysis and used them in full-scale, real-world applications. The applications demonstrated in this thesis include tracking, synthesis, recognitions and retrieval of dynamic objects based on their shape, appearance and motion. The proposed approach in this thesis has advantages over existing approaches to analyzing complex spatio-temporal data. Experiments show that the new modeling features of our approach improve the performance of existing approaches in many applications. In object tracking, our approach is the first one to track nonlinear appearance variations by using low-dimensional representation of the appearance change in globally-coordinated linear subspaces. In dynamic texture synthesis, we are able to model non-stationary dynamic textures, which cannot be handled by any of the existing approaches. In human motion synthesis, we show that realistic synthesis can be performed without using specific transition points, or key frames
Modeling Dynamic Swarms
This paper proposes the problem of modeling video sequences of dynamic swarms
(DS). We define DS as a large layout of stochastically repetitive spatial
configurations of dynamic objects (swarm elements) whose motions exhibit local
spatiotemporal interdependency and stationarity, i.e., the motions are similar
in any small spatiotemporal neighborhood. Examples of DS abound in nature,
e.g., herds of animals and flocks of birds. To capture the local spatiotemporal
properties of the DS, we present a probabilistic model that learns both the
spatial layout of swarm elements and their joint dynamics that are modeled as
linear transformations. To this end, a spatiotemporal neighborhood is
associated with each swarm element, in which local stationarity is enforced
both spatially and temporally. We assume that the prior on the swarm dynamics
is distributed according to an MRF in both space and time. Embedding this model
in a MAP framework, we iterate between learning the spatial layout of the swarm
and its dynamics. We learn the swarm transformations using ICM, which iterates
between estimating these transformations and updating their distribution in the
spatiotemporal neighborhoods. We demonstrate the validity of our method by
conducting experiments on real video sequences. Real sequences of birds, geese,
robot swarms, and pedestrians evaluate the applicability of our model to real
world data.Comment: 11 pages, 17 figures, conference paper, computer visio
Livrable D2.2 of the PERSEE project : Analyse/Synthese de Texture
Livrable D2.2 du projet ANR PERSEECe rapport a été réalisé dans le cadre du projet ANR PERSEE (n° ANR-09-BLAN-0170). Exactement il correspond au livrable D2.2 du projet. Son titre : Analyse/Synthese de Textur
Geometric deep learning: going beyond Euclidean data
Many scientific fields study data with an underlying structure that is a
non-Euclidean space. Some examples include social networks in computational
social sciences, sensor networks in communications, functional networks in
brain imaging, regulatory networks in genetics, and meshed surfaces in computer
graphics. In many applications, such geometric data are large and complex (in
the case of social networks, on the scale of billions), and are natural targets
for machine learning techniques. In particular, we would like to use deep
neural networks, which have recently proven to be powerful tools for a broad
range of problems from computer vision, natural language processing, and audio
analysis. However, these tools have been most successful on data with an
underlying Euclidean or grid-like structure, and in cases where the invariances
of these structures are built into networks used to model them. Geometric deep
learning is an umbrella term for emerging techniques attempting to generalize
(structured) deep neural models to non-Euclidean domains such as graphs and
manifolds. The purpose of this paper is to overview different examples of
geometric deep learning problems and present available solutions, key
difficulties, applications, and future research directions in this nascent
field
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