58 research outputs found
Electrostatic Field Classifier for Deficient Data
This paper investigates the suitability of recently developed models based on the physical
field phenomena for classification problems with incomplete datasets. An original approach
to exploiting incomplete training data with missing features and labels, involving extensive use
of electrostatic charge analogy, has been proposed. Classification of incomplete patterns has been
investigated using a local dimensionality reduction technique, which aims at exploiting all available
information rather than trying to estimate the missing values. The performance of all proposed
methods has been tested on a number of benchmark datasets for a wide range of missing data scenarios
and compared to the performance of some standard techniques. Several modifications of the
original electrostatic field classifier aiming at improving speed and robustness in higher dimensional
spaces are also discussed
Self-explaining AI as an alternative to interpretable AI
The ability to explain decisions made by AI systems is highly sought after,
especially in domains where human lives are at stake such as medicine or
autonomous vehicles. While it is often possible to approximate the input-output
relations of deep neural networks with a few human-understandable rules, the
discovery of the double descent phenomena suggests that such approximations do
not accurately capture the mechanism by which deep neural networks work. Double
descent indicates that deep neural networks typically operate by smoothly
interpolating between data points rather than by extracting a few high level
rules. As a result, neural networks trained on complex real world data are
inherently hard to interpret and prone to failure if asked to extrapolate. To
show how we might be able to trust AI despite these problems we introduce the
concept of self-explaining AI. Self-explaining AIs are capable of providing a
human-understandable explanation of each decision along with confidence levels
for both the decision and explanation. For this approach to work, it is
important that the explanation actually be related to the decision, ideally
capturing the mechanism used to arrive at the explanation. Finally, we argue it
is important that deep learning based systems include a "warning light" based
on techniques from applicability domain analysis to warn the user if a model is
asked to extrapolate outside its training distribution. For a video
presentation of this talk see https://www.youtube.com/watch?v=Py7PVdcu7WY& .Comment: 10pgs, 2 column forma
Preferred Spatial Frequencies for Human Face Processing Are Associated with Optimal Class Discrimination in the Machine
Psychophysical studies suggest that humans preferentially use a narrow band of low spatial frequencies for face recognition. Here we asked whether artificial face recognition systems have an improved recognition performance at the same spatial frequencies as humans. To this end, we estimated recognition performance over a large database of face images by computing three discriminability measures: Fisher Linear Discriminant Analysis, Non-Parametric Discriminant Analysis, and Mutual Information. In order to address frequency dependence, discriminabilities were measured as a function of (filtered) image size. All three measures revealed a maximum at the same image sizes, where the spatial frequency content corresponds to the psychophysical found frequencies. Our results therefore support the notion that the critical band of spatial frequencies for face recognition in humans and machines follows from inherent properties of face images, and that the use of these frequencies is associated with optimal face recognition performance
Towards Comprehensive Foundations of Computational Intelligence
Abstract. Although computational intelligence (CI) covers a vast variety of different methods it still lacks an integrative theory. Several proposals for CI foundations are discussed: computing and cognition as compression, meta-learning as search in the space of data models, (dis)similarity based methods providing a framework for such meta-learning, and a more general approach based on chains of transformations. Many useful transformations that extract information from features are discussed. Heterogeneous adaptive systems are presented as particular example of transformation-based systems, and the goal of learning is redefined to facilitate creation of simpler data models. The need to understand data structures leads to techniques for logical and prototype-based rule extraction, and to generation of multiple alternative models, while the need to increase predictive power of adaptive models leads to committees of competent models. Learning from partial observations is a natural extension towards reasoning based on perceptions, and an approach to intuitive solving of such problems is presented. Throughout the paper neurocognitive inspirations are frequently used and are especially important in modeling of the higher cognitive functions. Promising directions such as liquid and laminar computing are identified and many open problems presented.
Active Learning with Ensembles for Image Classification
In many real-world tasks of image classification, limited amounts of labeled data are available to train automatic classifiers. Consequently, extensive human expert involvement is required for verification
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