2,113 research outputs found
Higher-order Projected Power Iterations for Scalable Multi-Matching
The matching of multiple objects (e.g. shapes or images) is a fundamental
problem in vision and graphics. In order to robustly handle ambiguities, noise
and repetitive patterns in challenging real-world settings, it is essential to
take geometric consistency between points into account. Computationally, the
multi-matching problem is difficult. It can be phrased as simultaneously
solving multiple (NP-hard) quadratic assignment problems (QAPs) that are
coupled via cycle-consistency constraints. The main limitations of existing
multi-matching methods are that they either ignore geometric consistency and
thus have limited robustness, or they are restricted to small-scale problems
due to their (relatively) high computational cost. We address these
shortcomings by introducing a Higher-order Projected Power Iteration method,
which is (i) efficient and scales to tens of thousands of points, (ii)
straightforward to implement, (iii) able to incorporate geometric consistency,
(iv) guarantees cycle-consistent multi-matchings, and (iv) comes with
theoretical convergence guarantees. Experimentally we show that our approach is
superior to existing methods
Structured Prediction Problem Archive
Structured prediction problems are one of the fundamental tools in machinelearning. In order to facilitate algorithm development for their numericalsolution, we collect in one place a large number of datasets in easy to readformats for a diverse set of problem classes. We provide archival links todatasets, description of the considered problems and problem formats, and ashort summary of problem characteristics including size, number of instancesetc. For reference we also give a non-exhaustive selection of algorithmsproposed in the literature for their solution. We hope that this centralrepository will make benchmarking and comparison to established works easier.We welcome submission of interesting new datasets and algorithms for inclusionin our archive.<br
Structured Prediction Problem Archive
Structured prediction problems are one of the fundamental tools in machine
learning. In order to facilitate algorithm development for their numerical
solution, we collect in one place a large number of datasets in easy to read
formats for a diverse set of problem classes. We provide archival links to
datasets, description of the considered problems and problem formats, and a
short summary of problem characteristics including size, number of instances
etc. For reference we also give a non-exhaustive selection of algorithms
proposed in the literature for their solution. We hope that this central
repository will make benchmarking and comparison to established works easier.
We welcome submission of interesting new datasets and algorithms for inclusion
in our archive.Comment: Added multicast instances from Andres grou
Predicting Instability in Complex Oscillator Networks: Limitations and Potentials of Network Measures and Machine Learning
A central question of network science is how functional properties of systems
arise from their structure. For networked dynamical systems, structure is
typically quantified with network measures. A functional property that is of
theoretical and practical interest for oscillatory systems is the stability of
synchrony to localized perturbations. Recently, Graph Neural Networks (GNNs)
have been shown to predict this stability successfully; at the same time,
network measures have struggled to paint a clear picture. Here we collect 46
relevant network measures and find that no small subset can reliably predict
stability. The performance of GNNs can only be matched by combining all network
measures and nodewise machine learning. However, unlike GNNs, this approach
fails to extrapolate from network ensembles to several real power grid
topologies. This suggests that correlations of network measures and function
may be misleading, and that GNNs capture the causal relationship between
structure and stability substantially better.Comment: 30 pages (16 pages main section), 15 figures, 4 table
Cache Serializability: Reducing Inconsistency in Edge Transactions
Read-only caches are widely used in cloud infrastructures to reduce access
latency and load on backend databases. Operators view coherent caches as
impractical at genuinely large scale and many client-facing caches are updated
in an asynchronous manner with best-effort pipelines. Existing solutions that
support cache consistency are inapplicable to this scenario since they require
a round trip to the database on every cache transaction.
Existing incoherent cache technologies are oblivious to transactional data
access, even if the backend database supports transactions. We propose T-Cache,
a novel caching policy for read-only transactions in which inconsistency is
tolerable (won't cause safety violations) but undesirable (has a cost). T-Cache
improves cache consistency despite asynchronous and unreliable communication
between the cache and the database. We define cache-serializability, a variant
of serializability that is suitable for incoherent caches, and prove that with
unbounded resources T-Cache implements this new specification. With limited
resources, T-Cache allows the system manager to choose a trade-off between
performance and consistency.
Our evaluation shows that T-Cache detects many inconsistencies with only
nominal overhead. We use synthetic workloads to demonstrate the efficacy of
T-Cache when data accesses are clustered and its adaptive reaction to workload
changes. With workloads based on the real-world topologies, T-Cache detects
43-70% of the inconsistencies and increases the rate of consistent transactions
by 33-58%.Comment: Ittay Eyal, Ken Birman, Robbert van Renesse, "Cache Serializability:
Reducing Inconsistency in Edge Transactions," Distributed Computing Systems
(ICDCS), IEEE 35th International Conference on, June~29 2015--July~2 201
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