1,058 research outputs found
Analysis and Optimization of Deep Counterfactual Value Networks
Recently a strong poker-playing algorithm called DeepStack was published,
which is able to find an approximate Nash equilibrium during gameplay by using
heuristic values of future states predicted by deep neural networks. This paper
analyzes new ways of encoding the inputs and outputs of DeepStack's deep
counterfactual value networks based on traditional abstraction techniques, as
well as an unabstracted encoding, which was able to increase the network's
accuracy.Comment: Long version of publication appearing at KI 2018: The 41st German
Conference on Artificial Intelligence
(http://dx.doi.org/10.1007/978-3-030-00111-7_26). Corrected typo in titl
Faster K-Means Cluster Estimation
There has been considerable work on improving popular clustering algorithm
`K-means' in terms of mean squared error (MSE) and speed, both. However, most
of the k-means variants tend to compute distance of each data point to each
cluster centroid for every iteration. We propose a fast heuristic to overcome
this bottleneck with only marginal increase in MSE. We observe that across all
iterations of K-means, a data point changes its membership only among a small
subset of clusters. Our heuristic predicts such clusters for each data point by
looking at nearby clusters after the first iteration of k-means. We augment
well known variants of k-means with our heuristic to demonstrate effectiveness
of our heuristic. For various synthetic and real-world datasets, our heuristic
achieves speed-up of up-to 3 times when compared to efficient variants of
k-means.Comment: 6 pages, Accepted at ECIR 201
Compositional Verification for Autonomous Systems with Deep Learning Components
As autonomy becomes prevalent in many applications, ranging from
recommendation systems to fully autonomous vehicles, there is an increased need
to provide safety guarantees for such systems. The problem is difficult, as
these are large, complex systems which operate in uncertain environments,
requiring data-driven machine-learning components. However, learning techniques
such as Deep Neural Networks, widely used today, are inherently unpredictable
and lack the theoretical foundations to provide strong assurance guarantees. We
present a compositional approach for the scalable, formal verification of
autonomous systems that contain Deep Neural Network components. The approach
uses assume-guarantee reasoning whereby {\em contracts}, encoding the
input-output behavior of individual components, allow the designer to model and
incorporate the behavior of the learning-enabled components working
side-by-side with the other components. We illustrate the approach on an
example taken from the autonomous vehicles domain
Solving -means on High-dimensional Big Data
In recent years, there have been major efforts to develop data stream
algorithms that process inputs in one pass over the data with little memory
requirement. For the -means problem, this has led to the development of
several -approximations (under the assumption that is a
constant), but also to the design of algorithms that are extremely fast in
practice and compute solutions of high accuracy. However, when not only the
length of the stream is high but also the dimensionality of the input points,
then current methods reach their limits.
We propose two algorithms, piecy and piecy-mr that are based on the recently
developed data stream algorithm BICO that can process high dimensional data in
one pass and output a solution of high quality. While piecy is suited for high
dimensional data with a medium number of points, piecy-mr is meant for high
dimensional data that comes in a very long stream. We provide an extensive
experimental study to evaluate piecy and piecy-mr that shows the strength of
the new algorithms.Comment: 23 pages, 9 figures, published at the 14th International Symposium on
Experimental Algorithms - SEA 201
Privacy Preserving Multi-Server k-means Computation over Horizontally Partitioned Data
The k-means clustering is one of the most popular clustering algorithms in
data mining. Recently a lot of research has been concentrated on the algorithm
when the dataset is divided into multiple parties or when the dataset is too
large to be handled by the data owner. In the latter case, usually some servers
are hired to perform the task of clustering. The dataset is divided by the data
owner among the servers who together perform the k-means and return the cluster
labels to the owner. The major challenge in this method is to prevent the
servers from gaining substantial information about the actual data of the
owner. Several algorithms have been designed in the past that provide
cryptographic solutions to perform privacy preserving k-means. We provide a new
method to perform k-means over a large set using multiple servers. Our
technique avoids heavy cryptographic computations and instead we use a simple
randomization technique to preserve the privacy of the data. The k-means
computed has exactly the same efficiency and accuracy as the k-means computed
over the original dataset without any randomization. We argue that our
algorithm is secure against honest but curious and passive adversary.Comment: 19 pages, 4 tables. International Conference on Information Systems
Security. Springer, Cham, 201
Relating Statistical Image Differences and Degradation Features
Document images are degraded through bilevel processes such as scanning, printing, and photocopying. The resulting image degradations can be categorized based either on observable degradation features or on degradation model parameters. The degradation features can be related mathematically to model parameters. In this paper we statistically compare pairs of populations of degraded character images created with different model parameters. The changes in the probability that the characters are from different populations when the model parameters vary correlate with the relationship between observable degradation features and the model parameters. The paper also shows which features have the largest impact on the image
Probing Nonlocal Spatial Correlations in Quantum Gases with Ultra-long-range Rydberg Molecules
We present photo-excitation of ultra-long-range Rydberg molecules as a probe
of spatial correlations in quantum gases. Rydberg molecules can be created with
well-defined internuclear spacing, set by the radius of the outer lobe of the
Rydberg electron wavefunction . By varying the principal quantum number
of the target Rydberg state, the molecular excitation rate can be used to
map the pair-correlation function of the trapped gas . We
demonstrate this with ultracold Sr gases and probe pair-separation length
scales ranging from , which are on the order of the
thermal de Broglie wavelength for temperatures around 1 K. We observe
bunching for a single-component Bose gas of Sr and anti-bunching due to
Pauli exclusion at short distances for a polarized Fermi gas of Sr,
revealing the effects of quantum statistics.Comment: 6 pages, 5 figure
Measuring nonlocal three-body spatial correlations with Rydberg trimers in ultracold quantum gases
We measure nonlocal third-order spatial correlations in non-degenerate
ultracold gases of bosonic (Sr) and spin-polarized fermionic (Sr)
strontium through studies of the formation rates for ultralong-range trimer
Rydberg molecules. The trimer production rate is observed to be very sensitive
to the effects of quantum statistics with a strong enhancement of up to a
factor of six (3!) in the case of bosonic Sr due to bunching, and a
marked reduction for spin-polarized fermionic Sr due to anti-bunching.
The experimental results are compared to theoretical predictions and good
agreement is observed. The present approach opens the way to {\it{in situ}}
studies of higher-order nonlocal spatial correlations in a wide array of
ultracold atomic-gas systems.Comment: 7 pages, 5 figure
- …