32,536 research outputs found
Spatial Evolutionary Generative Adversarial Networks
Generative adversary networks (GANs) suffer from training pathologies such as
instability and mode collapse. These pathologies mainly arise from a lack of
diversity in their adversarial interactions. Evolutionary generative
adversarial networks apply the principles of evolutionary computation to
mitigate these problems. We hybridize two of these approaches that promote
training diversity. One, E-GAN, at each batch, injects mutation diversity by
training the (replicated) generator with three independent objective functions
then selecting the resulting best performing generator for the next batch. The
other, Lipizzaner, injects population diversity by training a two-dimensional
grid of GANs with a distributed evolutionary algorithm that includes neighbor
exchanges of additional training adversaries, performance based selection and
population-based hyper-parameter tuning. We propose to combine mutation and
population approaches to diversity improvement. We contribute a superior
evolutionary GANs training method, Mustangs, that eliminates the single loss
function used across Lipizzaner's grid. Instead, each training round, a loss
function is selected with equal probability, from among the three E-GAN uses.
Experimental analyses on standard benchmarks, MNIST and CelebA, demonstrate
that Mustangs provides a statistically faster training method resulting in more
accurate networks
Forecasting the cost of processing multi-join queries via hashing for main-memory databases (Extended version)
Database management systems (DBMSs) carefully optimize complex multi-join
queries to avoid expensive disk I/O. As servers today feature tens or hundreds
of gigabytes of RAM, a significant fraction of many analytic databases becomes
memory-resident. Even after careful tuning for an in-memory environment, a
linear disk I/O model such as the one implemented in PostgreSQL may make query
response time predictions that are up to 2X slower than the optimal multi-join
query plan over memory-resident data. This paper introduces a memory I/O cost
model to identify good evaluation strategies for complex query plans with
multiple hash-based equi-joins over memory-resident data. The proposed cost
model is carefully validated for accuracy using three different systems,
including an Amazon EC2 instance, to control for hardware-specific differences.
Prior work in parallel query evaluation has advocated right-deep and bushy
trees for multi-join queries due to their greater parallelization and
pipelining potential. A surprising finding is that the conventional wisdom from
shared-nothing disk-based systems does not directly apply to the modern
shared-everything memory hierarchy. As corroborated by our model, the
performance gap between the optimal left-deep and right-deep query plan can
grow to about 10X as the number of joins in the query increases.Comment: 15 pages, 8 figures, extended version of the paper to appear in
SoCC'1
Activity recognition from videos with parallel hypergraph matching on GPUs
In this paper, we propose a method for activity recognition from videos based
on sparse local features and hypergraph matching. We benefit from special
properties of the temporal domain in the data to derive a sequential and fast
graph matching algorithm for GPUs.
Traditionally, graphs and hypergraphs are frequently used to recognize
complex and often non-rigid patterns in computer vision, either through graph
matching or point-set matching with graphs. Most formulations resort to the
minimization of a difficult discrete energy function mixing geometric or
structural terms with data attached terms involving appearance features.
Traditional methods solve this minimization problem approximately, for instance
with spectral techniques.
In this work, instead of solving the problem approximatively, the exact
solution for the optimal assignment is calculated in parallel on GPUs. The
graphical structure is simplified and regularized, which allows to derive an
efficient recursive minimization algorithm. The algorithm distributes
subproblems over the calculation units of a GPU, which solves them in parallel,
allowing the system to run faster than real-time on medium-end GPUs
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