1,063 research outputs found
SANet: Structure-Aware Network for Visual Tracking
Convolutional neural network (CNN) has drawn increasing interest in visual
tracking owing to its powerfulness in feature extraction. Most existing
CNN-based trackers treat tracking as a classification problem. However, these
trackers are sensitive to similar distractors because their CNN models mainly
focus on inter-class classification. To address this problem, we use
self-structure information of object to distinguish it from distractors.
Specifically, we utilize recurrent neural network (RNN) to model object
structure, and incorporate it into CNN to improve its robustness to similar
distractors. Considering that convolutional layers in different levels
characterize the object from different perspectives, we use multiple RNNs to
model object structure in different levels respectively. Extensive experiments
on three benchmarks, OTB100, TC-128 and VOT2015, show that the proposed
algorithm outperforms other methods. Code is released at
http://www.dabi.temple.edu/~hbling/code/SANet/SANet.html.Comment: In CVPR Deep Vision Workshop, 201
Graphical Modeling for High Dimensional Data
With advances in science and information technologies, many scientific fields are able to meet the challenges of managing and analyzing high-dimensional data. A so-called large p small n problem arises when the number of experimental units, n, is equal to or smaller than the number of features, p. A methodology based on probability and graph theory, termed graphical models, is applied to study the structure and inference of such high-dimensional data
Identification of Nonlinear Latent Hierarchical Models
Identifying latent variables and causal structures from observational data is
essential to many real-world applications involving biological data, medical
data, and unstructured data such as images and languages. However, this task
can be highly challenging, especially when observed variables are generated by
causally related latent variables and the relationships are nonlinear. In this
work, we investigate the identification problem for nonlinear latent
hierarchical causal models in which observed variables are generated by a set
of causally related latent variables, and some latent variables may not have
observed children.
We show that the identifiability of causal structures and latent variables
(up to invertible transformations) can be achieved under mild assumptions: on
causal structures, we allow for multiple paths between any pair of variables in
the graph, which relaxes latent tree assumptions in prior work; on structural
functions, we permit general nonlinearity and multi-dimensional continuous
variables, alleviating existing work's parametric assumptions. Specifically, we
first develop an identification criterion in the form of novel identifiability
guarantees for an elementary latent variable model. Leveraging this criterion,
we show that both causal structures and latent variables of the hierarchical
model can be identified asymptotically by explicitly constructing an estimation
procedure. To the best of our knowledge, our work is the first to establish
identifiability guarantees for both causal structures and latent variables in
nonlinear latent hierarchical models.Comment: NeurIPS 202
Games with permission structures: The conjunctive approach
Game Theory;econometrics
Information visualization for DNA microarray data analysis: A critical review
Graphical representation may provide effective means of making sense of the complexity and sheer volume of data produced by DNA microarray experiments that monitor the expression patterns of thousands of genes simultaneously. The ability to use ldquoabstractrdquo graphical representation to draw attention to areas of interest, and more in-depth visualizations to answer focused questions, would enable biologists to move from a large amount of data to particular records they are interested in, and therefore, gain deeper insights in understanding the microarray experiment results. This paper starts by providing some background knowledge of microarray experiments, and then, explains how graphical representation can be applied in general to this problem domain, followed by exploring the role of visualization in gene expression data analysis. Having set the problem scene, the paper then examines various multivariate data visualization techniques that have been applied to microarray data analysis. These techniques are critically reviewed so that the strengths and weaknesses of each technique can be tabulated. Finally, several key problem areas as well as possible solutions to them are discussed as being a source for future work
Active Topology Inference using Network Coding
Our goal is to infer the topology of a network when (i) we can send probes
between sources and receivers at the edge of the network and (ii) intermediate
nodes can perform simple network coding operations, i.e., additions. Our key
intuition is that network coding introduces topology-dependent correlation in
the observations at the receivers, which can be exploited to infer the
topology. For undirected tree topologies, we design hierarchical clustering
algorithms, building on our prior work. For directed acyclic graphs (DAGs),
first we decompose the topology into a number of two-source, two-receiver
(2-by-2) subnetwork components and then we merge these components to
reconstruct the topology. Our approach for DAGs builds on prior work on
tomography, and improves upon it by employing network coding to accurately
distinguish among all different 2-by-2 components. We evaluate our algorithms
through simulation of a number of realistic topologies and compare them to
active tomographic techniques without network coding. We also make connections
between our approach and alternatives, including passive inference, traceroute,
and packet marking
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