1,720,965 research outputs found
DeepRICH: learning deeply Cherenkov detectors
Full text linkImaging Cherenkov detectors are largely used for particle identification (PID) in nuclear and particle physics experiments, where developing fast reconstruction algorithms is becoming of paramount importance to allow for near real time calibration and data quality control, as well as to speed up offline analysis of large amount of data. In this paper we present DeepRICH, a novel deep learning algorithm for fast reconstruction which can be applied to different imaging Cherenkov detectors. The core of our architecture is a generative model which leverages on a custom Variational Auto-encoder (VAE) combined to Maximum Mean Discrepancy (MMD), with a Convolutional Neural Network (CNN) extracting features from the space of the latent variables for classification. A thorough comparison with the simulation/reconstruction package FastDIRC is discussed in the text. DeepRICH has the advantage to bypass low-level details needed to build a likelihood, allowing for a sensitive improvement in computation time at potentially the same reconstruction performance of other established reconstruction algorithms. In the conclusions, we address the implications and potentialities of this work, discussing possible future extensions and generalization. LES
Bayesian Neural Networks With Maximum Mean Discrepancy Regularization
Full text linkBayesian Neural Networks (BNNs) are trained to optimize an entire
distribution over their weights instead of a single set, having significant
advantages in terms of, e.g., interpretability, multi-task learning, and
calibration. Because of the intractability of the resulting optimization
problem, most BNNs are either sampled through Monte Carlo methods, or trained
by minimizing a suitable Evidence Lower BOund (ELBO) on a variational
approximation. In this paper, we propose a variant of the latter, wherein we
replace the Kullback-Leibler divergence in the ELBO term with a Maximum Mean
Discrepancy (MMD) estimator, inspired by recent work in variational inference.
After motivating our proposal based on the properties of the MMD term, we
proceed to show a number of empirical advantages of the proposed formulation
over the state-of-the-art. In particular, our BNNs achieve higher accuracy on
multiple benchmarks, including several image classification tasks. In addition,
they are more robust to the selection of a prior over the weights, and they are
better calibrated. As a second contribution, we provide a new formulation for
estimating the uncertainty on a given prediction, showing it performs in a more
robust fashion against adversarial attacks and the injection of noise over
their inputs, compared to more classical criteria such as the differential
entropy
Pixle: a fast and effective black-box attack based on rearranging pixels
Full text linkRecent research has found that neural networks are vulnerable to several
types of adversarial attacks, where the input samples are modified in such a
way that the model produces a wrong prediction that misclassifies the
adversarial sample. In this paper we focus on black-box adversarial attacks,
that can be performed without knowing the inner structure of the attacked
model, nor the training procedure, and we propose a novel attack that is
capable of correctly attacking a high percentage of samples by rearranging a
small number of pixels within the attacked image. We demonstrate that our
attack works on a large number of datasets and models, that it requires a small
number of iterations, and that the distance between the original sample and the
adversarial one is negligible to the human eye
Continual Learning with Invertible Generative Models
Full text linkCatastrophic forgetting (CF) happens whenever a neural network overwrites
past knowledge while being trained on new tasks. Common techniques to handle CF
include regularization of the weights (using, e.g., their importance on past
tasks), and rehearsal strategies, where the network is constantly re-trained on
past data. Generative models have also been applied for the latter, in order to
have endless sources of data. In this paper, we propose a novel method that
combines the strengths of regularization and generative-based rehearsal
approaches. Our generative model consists of a normalizing flow (NF), a
probabilistic and invertible neural network, trained on the internal embeddings
of the network. By keeping a single NF throughout the training process, we show
that our memory overhead remains constant. In addition, exploiting the
invertibility of the NF, we propose a simple approach to regularize the
network's embeddings with respect to past tasks. We show that our method
performs favorably with respect to state-of-the-art approaches in the
literature, with bounded computational power and memory overheads.Comment: arXiv admin note: substantial text overlap with arXiv:2007.0244
Conditional computation in neural networks: Principles and research trends
Full text linkThis article summarizes principles and ideas from the emerging area of applying conditional computation methods to the design of neural networks. In particular, we focus on neural networks that can dynamically activate or de-activate parts of their computational graph conditionally on their input. Examples include the dynamic selection of, e.g., input tokens, layers (or sets of layers), and sub-modules inside each layer (e.g., channels in a convolutional filter). We first provide a general formalism to describe these techniques in an uniform way. Then, we introduce three notable implementations of these principles: mixture-of-experts (MoEs) networks, token selection mechanisms, and early-exit neural networks. The paper aims to provide a tutorial-like introduction to this growing field. To this end, we analyze the benefits of these modular designs in terms of efficiency, explainability, and transfer learning, with a focus on emerging applicative areas ranging from automated scientific discovery to semantic communication
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Rearranging Pixels is a Powerful Black-Box Attack for RGB and Infrared Deep Learning Models
Full text linkRecent research has found that neural networks for computer vision are vulnerable to several types of external attacks that modify the input of the model, with the malicious intent of producing a misclassification. With the increase in the number of feasible attacks, many defence approaches have been proposed to mitigate the effect of these attacks and protect the models. Mainly, the research on both attack and defence has focused on RGB images, while other domains, such as the infrared domain, are currently underexplored. In this paper, we propose two attacks, and we evaluate them on multiple datasets and neural network models, showing that the results outperform others established attacks, on both RGB as well as infrared domains. In addition, we show that our proposal can be used in an adversarial training protocol to produce more robust models, with respect to both adversarial attacks and natural perturbations that can be applied to input images. Lastly, we study if a successful attack in a domain can be transferred to an aligned image in another domain, without any further tuning
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