796 research outputs found
Defense against Universal Adversarial Perturbations
Recent advances in Deep Learning show the existence of image-agnostic
quasi-imperceptible perturbations that when applied to `any' image can fool a
state-of-the-art network classifier to change its prediction about the image
label. These `Universal Adversarial Perturbations' pose a serious threat to the
success of Deep Learning in practice. We present the first dedicated framework
to effectively defend the networks against such perturbations. Our approach
learns a Perturbation Rectifying Network (PRN) as `pre-input' layers to a
targeted model, such that the targeted model needs no modification. The PRN is
learned from real and synthetic image-agnostic perturbations, where an
efficient method to compute the latter is also proposed. A perturbation
detector is separately trained on the Discrete Cosine Transform of the
input-output difference of the PRN. A query image is first passed through the
PRN and verified by the detector. If a perturbation is detected, the output of
the PRN is used for label prediction instead of the actual image. A rigorous
evaluation shows that our framework can defend the network classifiers against
unseen adversarial perturbations in the real-world scenarios with up to 97.5%
success rate. The PRN also generalizes well in the sense that training for one
targeted network defends another network with a comparable success rate.Comment: Accepted in IEEE CVPR 201
The Causal Roadmap and simulation studies to inform the Statistical Analysis Plan for real-data applications
The Causal Roadmap outlines a systematic approach to our research endeavors:
define quantity of interest, evaluate needed assumptions, conduct statistical
estimation, and carefully interpret of results. At the estimation step, it is
essential that the estimation algorithm be chosen thoughtfully for its
theoretical properties and expected performance. Simulations can help
researchers gain a better understanding of an estimator's statistical
performance under conditions unique to the real-data application. This in turn
can inform the rigorous pre-specification of a Statistical Analysis Plan (SAP),
not only stating the estimand (e.g., G-computation formula), the estimator
(e.g., targeted minimum loss-based estimation [TMLE]), and adjustment
variables, but also the implementation of the estimator -- including nuisance
parameter estimation and approach for variance estimation. Doing so helps
ensure valid inference (e.g., 95% confidence intervals with appropriate
coverage). Failing to pre-specify estimation can lead to data dredging and
inflated Type-I error rates
NIPS - Not Even Wrong? A Systematic Review of Empirically Complete Demonstrations of Algorithmic Effectiveness in the Machine Learning and Artificial Intelligence Literature
Objective: To determine the completeness of argumentative steps necessary to
conclude effectiveness of an algorithm in a sample of current ML/AI supervised
learning literature.
Data Sources: Papers published in the Neural Information Processing Systems
(NeurIPS, n\'ee NIPS) journal where the official record showed a 2017 year of
publication.
Eligibility Criteria: Studies reporting a (semi-)supervised model, or
pre-processing fused with (semi-)supervised models for tabular data.
Study Appraisal: Three reviewers applied the assessment criteria to determine
argumentative completeness. The criteria were split into three groups,
including: experiments (e.g real and/or synthetic data), baselines (e.g
uninformed and/or state-of-art) and quantitative comparison (e.g. performance
quantifiers with confidence intervals and formal comparison of the algorithm
against baselines).
Results: Of the 121 eligible manuscripts (from the sample of 679 abstracts),
99\% used real-world data and 29\% used synthetic data. 91\% of manuscripts did
not report an uninformed baseline and 55\% reported a state-of-art baseline.
32\% reported confidence intervals for performance but none provided references
or exposition for how these were calculated. 3\% reported formal comparisons.
Limitations: The use of one journal as the primary information source may not
be representative of all ML/AI literature. However, the NeurIPS conference is
recognised to be amongst the top tier concerning ML/AI studies, so it is
reasonable to consider its corpus to be representative of high-quality
research.
Conclusion: Using the 2017 sample of the NeurIPS supervised learning corpus
as an indicator for the quality and trustworthiness of current ML/AI research,
it appears that complete argumentative chains in demonstrations of algorithmic
effectiveness are rare
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