39,319 research outputs found
Interpreting Adversarially Trained Convolutional Neural Networks
We attempt to interpret how adversarially trained convolutional neural
networks (AT-CNNs) recognize objects. We design systematic approaches to
interpret AT-CNNs in both qualitative and quantitative ways and compare them
with normally trained models. Surprisingly, we find that adversarial training
alleviates the texture bias of standard CNNs when trained on object recognition
tasks, and helps CNNs learn a more shape-biased representation. We validate our
hypothesis from two aspects. First, we compare the salience maps of AT-CNNs and
standard CNNs on clean images and images under different transformations. The
comparison could visually show that the prediction of the two types of CNNs is
sensitive to dramatically different types of features. Second, to achieve
quantitative verification, we construct additional test datasets that destroy
either textures or shapes, such as style-transferred version of clean data,
saturated images and patch-shuffled ones, and then evaluate the classification
accuracy of AT-CNNs and normal CNNs on these datasets. Our findings shed some
light on why AT-CNNs are more robust than those normally trained ones and
contribute to a better understanding of adversarial training over CNNs from an
interpretation perspective.Comment: To apper in ICML1
Review of sensors for remote patient monitoring
Remote patient monitoring (RPM) of physiological
measurements can provide an efficient method and high
quality care to patients. The physiological signals
measurement is the initial and the most important factor
in RPM. This paper discusses the characteristics of the
most popular sensors, which are used to obtain vital
clinical signals in prevalent RPM systems.
The sensors discussed in this paper are used to measure
ECG, heart sound, pulse rate, oxygen saturation, blood
pressure and respiration rate, which are treated as the
most important vital data in patient monitoring and
medical examination
Homeostatic plasticity improves signal propagation in continuous time recurrent neural networks
Continuous-time recurrent neural networks (CTRNNs) are potentially an excellent substrate for the generation of adaptive behaviour in artificial autonomous agents. However, node saturation effects in these networks can leave them insensitive to input and stop signals from propagating. Node saturation is related to the problems of hyper-excitation and quiescence in biological nervous systems, which are thought to be avoided through the existence of homeostatic plastic mechanisms. Analogous mechanisms are here implemented in a variety of CTRNN architectures and are shown to increase node sensitivity and improve signal propagation, with implications for robotics. These results lend support to the view that homeostatic plasticity may prevent quiescence and hyper-excitation in biological nervous systems
On controllability of neuronal networks with constraints on the average of control gains
Control gains play an important role in the control of a natural or a technical system since they reflect how much resource is required to optimize a certain control objective. This paper is concerned with the controllability of neuronal networks with constraints on the average value of the control gains injected in driver nodes, which are in accordance with engineering and biological backgrounds. In order to deal with the constraints on control gains, the controllability problem is transformed into a constrained optimization problem (COP). The introduction of the constraints on the control gains unavoidably leads to substantial difficulty in finding feasible as well as refining solutions. As such, a modified dynamic hybrid framework (MDyHF) is developed to solve this COP, based on an adaptive differential evolution and the concept of Pareto dominance. By comparing with statistical methods and several recently reported constrained optimization evolutionary algorithms (COEAs), we show that our proposed MDyHF is competitive and promising in studying the controllability of neuronal networks. Based on the MDyHF, we proceed to show the controlling regions under different levels of constraints. It is revealed that we should allocate the control gains economically when strong constraints are considered. In addition, it is found that as the constraints become more restrictive, the driver nodes are more likely to be selected from the nodes with a large degree. The results and methods presented in this paper will provide useful insights into developing new techniques to control a realistic complex network efficiently
The Neural Representation Benchmark and its Evaluation on Brain and Machine
A key requirement for the development of effective learning representations
is their evaluation and comparison to representations we know to be effective.
In natural sensory domains, the community has viewed the brain as a source of
inspiration and as an implicit benchmark for success. However, it has not been
possible to directly test representational learning algorithms directly against
the representations contained in neural systems. Here, we propose a new
benchmark for visual representations on which we have directly tested the
neural representation in multiple visual cortical areas in macaque (utilizing
data from [Majaj et al., 2012]), and on which any computer vision algorithm
that produces a feature space can be tested. The benchmark measures the
effectiveness of the neural or machine representation by computing the
classification loss on the ordered eigendecomposition of a kernel matrix
[Montavon et al., 2011]. In our analysis we find that the neural representation
in visual area IT is superior to visual area V4. In our analysis of
representational learning algorithms, we find that three-layer models approach
the representational performance of V4 and the algorithm in [Le et al., 2012]
surpasses the performance of V4. Impressively, we find that a recent supervised
algorithm [Krizhevsky et al., 2012] achieves performance comparable to that of
IT for an intermediate level of image variation difficulty, and surpasses IT at
a higher difficulty level. We believe this result represents a major milestone:
it is the first learning algorithm we have found that exceeds our current
estimate of IT representation performance. We hope that this benchmark will
assist the community in matching the representational performance of visual
cortex and will serve as an initial rallying point for further correspondence
between representations derived in brains and machines.Comment: The v1 version contained incorrectly computed kernel analysis curves
and KA-AUC values for V4, IT, and the HT-L3 models. They have been corrected
in this versio
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