37,032 research outputs found
Classification without labels: Learning from mixed samples in high energy physics
Modern machine learning techniques can be used to construct powerful models
for difficult collider physics problems. In many applications, however, these
models are trained on imperfect simulations due to a lack of truth-level
information in the data, which risks the model learning artifacts of the
simulation. In this paper, we introduce the paradigm of classification without
labels (CWoLa) in which a classifier is trained to distinguish statistical
mixtures of classes, which are common in collider physics. Crucially, neither
individual labels nor class proportions are required, yet we prove that the
optimal classifier in the CWoLa paradigm is also the optimal classifier in the
traditional fully-supervised case where all label information is available.
After demonstrating the power of this method in an analytical toy example, we
consider a realistic benchmark for collider physics: distinguishing quark-
versus gluon-initiated jets using mixed quark/gluon training samples. More
generally, CWoLa can be applied to any classification problem where labels or
class proportions are unknown or simulations are unreliable, but statistical
mixtures of the classes are available.Comment: 18 pages, 5 figures; v2: intro extended and references added; v3:
additional discussion to match JHEP versio
The Behavioral Paradox: Why Investor Irrationality Calls for Lighter and Simpler Financial Regulation
It is widely believed that behavioral economics justifies more intrusive regulation of financial markets, because people are not fully rational and need to be protected from their quirks. This Article challenges that belief. Firstly, insofar as people can be helped to make better choices, that goal can usually be achieved through light-touch regulations. Secondly, faulty perceptions about markets seem to be best corrected through market-based solutions. Thirdly, increasing regulation does not seem to solve problems caused by lack of market discipline, pricing inefficiencies, and financial innovation; better results may be achieved with freer markets and simpler rules. Fourthly, regulatory rule makers are subject to imperfect rationality, which tends to reduce the quality of regulatory intervention. Finally, regulatory complexity exacerbates the harmful effects of bounded rationality, whereas simple and stable rules give rise to positive learning effects
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A comparative survey of integrated learning systems
This paper presents the duction framework for unifying the three basic forms of inference - deduction, abduction, and induction - by specifying the possible relationships and influences among them in the context of integrated learning. Special assumptive forms of inference are defined that extend the use of these inference methods, and the properties of these forms are explored. A comparison to a related inference-based learning frame work is made. Finally several existing integrated learning programs are examined in the perspective of the duction framework
Coarse-to-Fine Annotation Enrichment for Semantic Segmentation Learning
Rich high-quality annotated data is critical for semantic segmentation
learning, yet acquiring dense and pixel-wise ground-truth is both labor- and
time-consuming. Coarse annotations (e.g., scribbles, coarse polygons) offer an
economical alternative, with which training phase could hardly generate
satisfactory performance unfortunately. In order to generate high-quality
annotated data with a low time cost for accurate segmentation, in this paper,
we propose a novel annotation enrichment strategy, which expands existing
coarse annotations of training data to a finer scale. Extensive experiments on
the Cityscapes and PASCAL VOC 2012 benchmarks have shown that the neural
networks trained with the enriched annotations from our framework yield a
significant improvement over that trained with the original coarse labels. It
is highly competitive to the performance obtained by using human annotated
dense annotations. The proposed method also outperforms among other
state-of-the-art weakly-supervised segmentation methods.Comment: CIKM 2018 International Conference on Information and Knowledge
Managemen
Learning to Classify from Impure Samples with High-Dimensional Data
A persistent challenge in practical classification tasks is that labeled
training sets are not always available. In particle physics, this challenge is
surmounted by the use of simulations. These simulations accurately reproduce
most features of data, but cannot be trusted to capture all of the complex
correlations exploitable by modern machine learning methods. Recent work in
weakly supervised learning has shown that simple, low-dimensional classifiers
can be trained using only the impure mixtures present in data. Here, we
demonstrate that complex, high-dimensional classifiers can also be trained on
impure mixtures using weak supervision techniques, with performance comparable
to what could be achieved with pure samples. Using weak supervision will
therefore allow us to avoid relying exclusively on simulations for
high-dimensional classification. This work opens the door to a new regime
whereby complex models are trained directly on data, providing direct access to
probe the underlying physics.Comment: 6 pages, 2 tables, 2 figures. v2: updated to match PRD versio
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