9,014 research outputs found
Interpreting Neural Networks Using Flip Points
Neural networks have been criticized for their lack of easy interpretation,
which undermines confidence in their use for important applications. Here, we
introduce a novel technique, interpreting a trained neural network by
investigating its flip points. A flip point is any point that lies on the
boundary between two output classes: e.g. for a neural network with a binary
yes/no output, a flip point is any input that generates equal scores for "yes"
and "no". The flip point closest to a given input is of particular importance,
and this point is the solution to a well-posed optimization problem. This paper
gives an overview of the uses of flip points and how they are computed. Through
results on standard datasets, we demonstrate how flip points can be used to
provide detailed interpretation of the output produced by a neural network.
Moreover, for a given input, flip points enable us to measure confidence in the
correctness of outputs much more effectively than softmax score. They also
identify influential features of the inputs, identify bias, and find changes in
the input that change the output of the model. We show that distance between an
input and the closest flip point identifies the most influential points in the
training data. Using principal component analysis (PCA) and rank-revealing QR
factorization (RR-QR), the set of directions from each training input to its
closest flip point provides explanations of how a trained neural network
processes an entire dataset: what features are most important for
classification into a given class, which features are most responsible for
particular misclassifications, how an adversary might fool the network, etc.
Although we investigate flip points for neural networks, their usefulness is
actually model-agnostic
The Mode of Computing
The Turing Machine is the paradigmatic case of computing machines, but there
are others, such as Artificial Neural Networks, Table Computing,
Relational-Indeterminate Computing and diverse forms of analogical computing,
each of which based on a particular underlying intuition of the phenomenon of
computing. This variety can be captured in terms of system levels,
re-interpreting and generalizing Newell's hierarchy, which includes the
knowledge level at the top and the symbol level immediately below it. In this
re-interpretation the knowledge level consists of human knowledge and the
symbol level is generalized into a new level that here is called The Mode of
Computing. Natural computing performed by the brains of humans and non-human
animals with a developed enough neural system should be understood in terms of
a hierarchy of system levels too. By analogy from standard computing machinery
there must be a system level above the neural circuitry levels and directly
below the knowledge level that is named here The mode of Natural Computing. A
central question for Cognition is the characterization of this mode. The Mode
of Computing provides a novel perspective on the phenomena of computing,
interpreting, the representational and non-representational views of cognition,
and consciousness.Comment: 35 pages, 8 figure
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