508 research outputs found
Regression Concept Vectors for Bidirectional Explanations in Histopathology
Explanations for deep neural network predictions in terms of domain-related
concepts can be valuable in medical applications, where justifications are
important for confidence in the decision-making. In this work, we propose a
methodology to exploit continuous concept measures as Regression Concept
Vectors (RCVs) in the activation space of a layer. The directional derivative
of the decision function along the RCVs represents the network sensitivity to
increasing values of a given concept measure. When applied to breast cancer
grading, nuclei texture emerges as a relevant concept in the detection of tumor
tissue in breast lymph node samples. We evaluate score robustness and
consistency by statistical analysis.Comment: 9 pages, 3 figures, 3 table
Keyed Non-Parametric Hypothesis Tests
The recent popularity of machine learning calls for a deeper understanding of
AI security. Amongst the numerous AI threats published so far, poisoning
attacks currently attract considerable attention. In a poisoning attack the
opponent partially tampers the dataset used for learning to mislead the
classifier during the testing phase.
This paper proposes a new protection strategy against poisoning attacks. The
technique relies on a new primitive called keyed non-parametric hypothesis
tests allowing to evaluate under adversarial conditions the training input's
conformance with a previously learned distribution . To do so we
use a secret key unknown to the opponent.
Keyed non-parametric hypothesis tests differs from classical tests in that
the secrecy of prevents the opponent from misleading the keyed test
into concluding that a (significantly) tampered dataset belongs to
.Comment: Paper published in NSS 201
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