2,913 research outputs found
Fast Radio Burst 121102 Pulse Detection and Periodicity: A Machine Learning Approach
We report the detection of 72 new pulses from the repeating fast radio burst
FRB 121102 in Breakthrough Listen C-band (4-8 GHz) observations at the Green
Bank Telescope. The new pulses were found with a convolutional neural network
in data taken on August 26, 2017, where 21 bursts have been previously
detected. Our technique combines neural network detection with dedispersion
verification. For the current application we demonstrate its advantage over a
traditional brute-force dedis- persion algorithm in terms of higher
sensitivity, lower false positive rates, and faster computational speed.
Together with the 21 previously reported pulses, this observa- tion marks the
highest number of FRB 121102 pulses from a single observation, total- ing 93
pulses in five hours, including 45 pulses within the first 30 minutes. The
number of data points reveal trends in pulse fluence, pulse detection rate, and
pulse frequency structure. We introduce a new periodicity search technique,
based on the Rayleigh test, to analyze the time of arrivals, with which we
exclude with 99% confidence pe- riodicity in time of arrivals with periods
larger than 5.1 times the model-dependent time-stamp uncertainty. In
particular, we rule out constant periods >10 ms in the barycentric arrival
times, though intrinsic periodicity in the time of emission remains plausible.Comment: 32 pages, 10 figure
Deep Learning and Music Adversaries
OA Monitor ExerciseOA Monitor ExerciseAn {\em adversary} is essentially an algorithm intent on making a classification system perform in some particular way given an input, e.g., increase the probability of a false negative. Recent work builds adversaries for deep learning systems applied to image object recognition, which exploits the parameters of the system to find the minimal perturbation of the input image such that the network misclassifies it with high confidence. We adapt this approach to construct and deploy an adversary of deep learning systems applied to music content analysis. In our case, however, the input to the systems is magnitude spectral frames, which requires special care in order to produce valid input audio signals from network-derived perturbations. For two different train-test partitionings of two benchmark datasets, and two different deep architectures, we find that this adversary is very effective in defeating the resulting systems. We find the convolutional networks are more robust, however, compared with systems based on a majority vote over individually classified audio frames. Furthermore, we integrate the adversary into the training of new deep systems, but do not find that this improves their resilience against the same adversary
Procedural Noise Adversarial Examples for Black-Box Attacks on Deep Convolutional Networks
Deep Convolutional Networks (DCNs) have been shown to be vulnerable to
adversarial examples---perturbed inputs specifically designed to produce
intentional errors in the learning algorithms at test time. Existing
input-agnostic adversarial perturbations exhibit interesting visual patterns
that are currently unexplained. In this paper, we introduce a structured
approach for generating Universal Adversarial Perturbations (UAPs) with
procedural noise functions. Our approach unveils the systemic vulnerability of
popular DCN models like Inception v3 and YOLO v3, with single noise patterns
able to fool a model on up to 90% of the dataset. Procedural noise allows us to
generate a distribution of UAPs with high universal evasion rates using only a
few parameters. Additionally, we propose Bayesian optimization to efficiently
learn procedural noise parameters to construct inexpensive untargeted black-box
attacks. We demonstrate that it can achieve an average of less than 10 queries
per successful attack, a 100-fold improvement on existing methods. We further
motivate the use of input-agnostic defences to increase the stability of models
to adversarial perturbations. The universality of our attacks suggests that DCN
models may be sensitive to aggregations of low-level class-agnostic features.
These findings give insight on the nature of some universal adversarial
perturbations and how they could be generated in other applications.Comment: 16 pages, 10 figures. In Proceedings of the 2019 ACM SIGSAC
Conference on Computer and Communications Security (CCS '19
Features of hearing: applications of machine learning to uncover the building blocks of hearing
Recent advances in machine learning have instigated a renewed interest in using machine learning approaches to better understand human sensory processing. This line of research is particularly interesting for speech research since speech comprehension is uniquely human, which complicates obtaining detailed neural recordings. In this thesis, I explore how machine learning can be used to uncover new knowledge about the auditory system, with a focus on discovering robust auditory features. The resulting increased understanding of the noise robustness of human hearing may help to better assist those with hearing loss and improve Automatic Speech Recognition (ASR) systems. First, I show how computational neuroscience and machine learning can be combined to generate hypotheses about auditory features. I introduce a neural feature detection model with a modest number of parameters that is compatible with auditory physiology. By testing feature detector variants in a speech classification task, I confirm the importance of both well-studied and lesser-known auditory features. Second, I investigate whether ASR software is a good candidate model of the human auditory system. By comparing several state-of-the-art ASR systems to the results from humans on a range of psychometric experiments, I show that these ASR systems diverge markedly from humans in at least some psychometric tests. This implies that none of these systems act as a strong proxy for human speech recognition, although some may be useful when asking more narrowly defined questions. For neuroscientists, this thesis exemplifies how machine learning can be used to generate new hypotheses about human hearing, while also highlighting the caveats of investigating systems that may work fundamentally differently from the human brain. For machine learning engineers, I point to tangible directions for improving ASR systems. To motivate the continued cross-fertilization between these fields, a toolbox that allows researchers to assess new ASR systems has been released.Open Acces
Neuromorphic Few-Shot Learning: Generalization in Multilayer Physical Neural Networks
Neuromorphic computing leverages the complex dynamics of physical systems for
computation. The field has recently undergone an explosion in the range and
sophistication of implementations, with rapidly improving performance.
Neuromorphic schemes typically employ a single physical system, limiting the
dimensionality and range of available dynamics - restricting strong performance
to a few specific tasks. This is a critical roadblock facing the field,
inhibiting the power and versatility of neuromorphic schemes.
Here, we present a solution. We engineer a diverse suite of nanomagnetic
arrays and show how tuning microstate space and geometry enables a broad range
of dynamics and computing performance. We interconnect arrays in parallel,
series and multilayered neural network architectures, where each network node
is a distinct physical system. This networked approach grants extremely high
dimensionality and enriched dynamics enabling meta-learning to be implemented
on small training sets and exhibiting strong performance across a broad
taskset. We showcase network performance via few-shot learning, rapidly
adapting on-the-fly to previously unseen tasks
MemDA: Forecasting Urban Time Series with Memory-based Drift Adaptation
Urban time series data forecasting featuring significant contributions to
sustainable development is widely studied as an essential task of the smart
city. However, with the dramatic and rapid changes in the world environment,
the assumption that data obey Independent Identically Distribution is
undermined by the subsequent changes in data distribution, known as concept
drift, leading to weak replicability and transferability of the model over
unseen data. To address the issue, previous approaches typically retrain the
model, forcing it to fit the most recent observed data. However, retraining is
problematic in that it leads to model lag, consumption of resources, and model
re-invalidation, causing the drift problem to be not well solved in realistic
scenarios. In this study, we propose a new urban time series prediction model
for the concept drift problem, which encodes the drift by considering the
periodicity in the data and makes on-the-fly adjustments to the model based on
the drift using a meta-dynamic network. Experiments on real-world datasets show
that our design significantly outperforms state-of-the-art methods and can be
well generalized to existing prediction backbones by reducing their sensitivity
to distribution changes.Comment: Accepted by CIKM 202
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