362 research outputs found
MAT: A Multi-strength Adversarial Training Method to Mitigate Adversarial Attacks
Some recent works revealed that deep neural networks (DNNs) are vulnerable to
so-called adversarial attacks where input examples are intentionally perturbed
to fool DNNs. In this work, we revisit the DNN training process that includes
adversarial examples into the training dataset so as to improve DNN's
resilience to adversarial attacks, namely, adversarial training. Our
experiments show that different adversarial strengths, i.e., perturbation
levels of adversarial examples, have different working zones to resist the
attack. Based on the observation, we propose a multi-strength adversarial
training method (MAT) that combines the adversarial training examples with
different adversarial strengths to defend adversarial attacks. Two training
structures - mixed MAT and parallel MAT - are developed to facilitate the
tradeoffs between training time and memory occupation. Our results show that
MAT can substantially minimize the accuracy degradation of deep learning
systems to adversarial attacks on MNIST, CIFAR-10, CIFAR-100, and SVHN.Comment: 6 pages, 4 figures, 2 table
Photoacoustic computed tomography guided microrobots for targeted navigation in intestines in vivo
Tremendous progress in synthetic micro/nanomotors has been made for potential biomedical applications. However, existing micro/nanomotor platforms are inefficient for deep tissue imaging and motion control in vivo. Here, we present a photoacoustic computed tomography (PACT) guided investigation of micromotors in intestines in vivo. The micromotors enveloped in microcapsules exhibit efficient propulsion in various biofluids once released. PACT has visualized the migration of micromotor capsules toward the targeted regions in real time in vivo. The integration of the developed microrobotic system and PACT enables deep imaging and precise control of the micromotors in vivo
HyperTime: Hyperparameter Optimization for Combating Temporal Distribution Shifts
In this work, we propose a hyperparameter optimization method named
\emph{HyperTime} to find hyperparameters robust to potential temporal
distribution shifts in the unseen test data. Our work is motivated by an
important observation that it is, in many cases, possible to achieve temporally
robust predictive performance via hyperparameter optimization. Based on this
observation, we leverage the `worst-case-oriented' philosophy from the robust
optimization literature to help find such robust hyperparameter configurations.
HyperTime imposes a lexicographic priority order on average validation loss and
worst-case validation loss over chronological validation sets. We perform a
theoretical analysis on the upper bound of the expected test loss, which
reveals the unique advantages of our approach. We also demonstrate the strong
empirical performance of the proposed method on multiple machine learning tasks
with temporal distribution shifts.Comment: 19 pages, 7 figure
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