119 research outputs found
Representation Learning: A Review and New Perspectives
The success of machine learning algorithms generally depends on data
representation, and we hypothesize that this is because different
representations can entangle and hide more or less the different explanatory
factors of variation behind the data. Although specific domain knowledge can be
used to help design representations, learning with generic priors can also be
used, and the quest for AI is motivating the design of more powerful
representation-learning algorithms implementing such priors. This paper reviews
recent work in the area of unsupervised feature learning and deep learning,
covering advances in probabilistic models, auto-encoders, manifold learning,
and deep networks. This motivates longer-term unanswered questions about the
appropriate objectives for learning good representations, for computing
representations (i.e., inference), and the geometrical connections between
representation learning, density estimation and manifold learning
Triggering Dark Showers with Conditional Dual Auto-Encoders
Auto-encoders (AEs) have the potential to be effective and generic tools for
new physics searches at colliders, requiring little to no model-dependent
assumptions. New hypothetical physics signals can be considered anomalies that
deviate from the well-known background processes generally expected to describe
the whole dataset. We present a search formulated as an anomaly detection (AD)
problem, using an AE to define a criterion to decide about the physics nature
of an event. In this work, we perform an AD search for manifestations of a dark
version of strong force using raw detector images, which are large and very
sparse, without leveraging any physics-based pre-processing or assumption on
the signals. We propose a dual-encoder design which can learn a compact latent
space through conditioning. In the context of multiple AD metrics, we present a
clear improvement over competitive baselines and prior approaches. It is the
first time that an AE is shown to exhibit excellent discrimination against
multiple dark shower models, illustrating the suitability of this method as a
performant, model-independent algorithm to deploy, e.g., in the trigger stage
of LHC experiments such as ATLAS and CMS.Comment: 25 pages, 7 figures, and 11 table
Reducing Redundancy in the Bottleneck Representation of the Autoencoders
Autoencoders are a type of unsupervised neural networks, which can be used to
solve various tasks, e.g., dimensionality reduction, image compression, and
image denoising. An AE has two goals: (i) compress the original input to a
low-dimensional space at the bottleneck of the network topology using an
encoder, (ii) reconstruct the input from the representation at the bottleneck
using a decoder. Both encoder and decoder are optimized jointly by minimizing a
distortion-based loss which implicitly forces the model to keep only those
variations of input data that are required to reconstruct the and to reduce
redundancies. In this paper, we propose a scheme to explicitly penalize feature
redundancies in the bottleneck representation. To this end, we propose an
additional loss term, based on the pair-wise correlation of the neurons, which
complements the standard reconstruction loss forcing the encoder to learn a
more diverse and richer representation of the input. We tested our approach
across different tasks: dimensionality reduction using three different dataset,
image compression using the MNIST dataset, and image denoising using fashion
MNIST. The experimental results show that the proposed loss leads consistently
to superior performance compared to the standard AE loss.Comment: 6 pages,4 figures. The paper is under consideration at Pattern
Recognition Letter
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