9,276 research outputs found
Intercultural Polylogues in Philosophy
Statement to Panel "Intercultural Dialogue", 29th Wittgenstein-Conference of the ALWS
Kirchberg am Wechsel, August 11th 2006. Other statements, in the order of contribution, have
been given by: Mohammed Shomali (Qom, Iran), Patrick Riordan SJ (London, UK), and
Eveline Goodman-Thau (Jerusalem, Israel)
Since this is a conference of philosophers about philosophy and matters relevant to
philosophy, I shall not talk about intercultural dialogues in general, nor will I speak about
dialogues in the fields of religion or culture (fields which have to be distinguished, by the
way), dialogues between politicians, etc. My statement will try to concentrate on
intercultural dialogues in philosophy. This means, according to my understanding of
"philosophy", that I have in mind essentially dialogues on ontological, on epistemological,
or on normative questions
The Algebraic Approach to Phase Retrieval and Explicit Inversion at the Identifiability Threshold
We study phase retrieval from magnitude measurements of an unknown signal as
an algebraic estimation problem. Indeed, phase retrieval from rank-one and more
general linear measurements can be treated in an algebraic way. It is verified
that a certain number of generic rank-one or generic linear measurements are
sufficient to enable signal reconstruction for generic signals, and slightly
more generic measurements yield reconstructability for all signals. Our results
solve a few open problems stated in the recent literature. Furthermore, we show
how the algebraic estimation problem can be solved by a closed-form algebraic
estimation technique, termed ideal regression, providing non-asymptotic success
guarantees
Dual-to-kernel learning with ideals
In this paper, we propose a theory which unifies kernel learning and symbolic
algebraic methods. We show that both worlds are inherently dual to each other,
and we use this duality to combine the structure-awareness of algebraic methods
with the efficiency and generality of kernels. The main idea lies in relating
polynomial rings to feature space, and ideals to manifolds, then exploiting
this generative-discriminative duality on kernel matrices. We illustrate this
by proposing two algorithms, IPCA and AVICA, for simultaneous manifold and
feature learning, and test their accuracy on synthetic and real world data.Comment: 15 pages, 1 figur
Using GPI-2 for Distributed Memory Paralleliziation of the Caffe Toolbox to Speed up Deep Neural Network Training
Deep Neural Network (DNN) are currently of great inter- est in research and
application. The training of these net- works is a compute intensive and time
consuming task. To reduce training times to a bearable amount at reasonable
cost we extend the popular Caffe toolbox for DNN with an efficient distributed
memory communication pattern. To achieve good scalability we emphasize the
overlap of computation and communication and prefer fine granu- lar
synchronization patterns over global barriers. To im- plement these
communication patterns we rely on the the Global address space Programming
Interface version 2 (GPI-2) communication library. This interface provides a
light-weight set of asynchronous one-sided communica- tion primitives
supplemented by non-blocking fine gran- ular data synchronization mechanisms.
Therefore, Caf- feGPI is the name of our parallel version of Caffe. First
benchmarks demonstrate better scaling behavior com- pared with other
extensions, e.g., the Intel TM Caffe. Even within a single symmetric
multiprocessing machine with four graphics processing units, the CaffeGPI
scales bet- ter than the standard Caffe toolbox. These first results
demonstrate that the use of standard High Performance Computing (HPC) hardware
is a valid cost saving ap- proach to train large DDNs. I/O is an other
bottleneck to work with DDNs in a standard parallel HPC setting, which we will
consider in more detail in a forthcoming paper
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