33 research outputs found
Fidelity Between Unitary Operators and the Generation of Gates Robust Against Off-Resonance Perturbations
We perform a functional expansion of the fidelity between two unitary
matrices in order to find the necessary conditions for the robust
implementation of a target gate. Comparison of these conditions with those
obtained from the Magnus expansion and Dyson series shows that they are
equivalent in first order. By exploiting techniques from robust design
optimization, we account for issues of experimental feasibility by introducing
an additional criterion to the search for control pulses. This search is
accomplished by exploring the competition between the multiple objectives in
the implementation of the NOT gate by means of evolutionary multi-objective
optimization
Improving Model Accuracy for Imbalanced Image Classification Tasks by Adding a Final Batch Normalization Layer: An Empirical Study
Some real-world domains, such as Agriculture and Healthcare, comprise
early-stage disease indications whose recording constitutes a rare event, and
yet, whose precise detection at that stage is critical. In this type of highly
imbalanced classification problems, which encompass complex features, deep
learning (DL) is much needed because of its strong detection capabilities. At
the same time, DL is observed in practice to favor majority over minority
classes and consequently suffer from inaccurate detection of the targeted
early-stage indications. To simulate such scenarios, we artificially generate
skewness (99% vs. 1%) for certain plant types out of the PlantVillage dataset
as a basis for classification of scarce visual cues through transfer learning.
By randomly and unevenly picking healthy and unhealthy samples from certain
plant types to form a training set, we consider a base experiment as
fine-tuning ResNet34 and VGG19 architectures and then testing the model
performance on a balanced dataset of healthy and unhealthy images. We
empirically observe that the initial F1 test score jumps from 0.29 to 0.95 for
the minority class upon adding a final Batch Normalization (BN) layer just
before the output layer in VGG19. We demonstrate that utilizing an additional
BN layer before the output layer in modern CNN architectures has a considerable
impact in terms of minimizing the training time and testing error for minority
classes in highly imbalanced data sets. Moreover, when the final BN is
employed, minimizing the loss function may not be the best way to assure a high
F1 test score for minority classes in such problems. That is, the network might
perform better even if it is not confident enough while making a prediction;
leading to another discussion about why softmax output is not a good
uncertainty measure for DL models.Comment: Accepted for presentation and inclusion in ICPR 2020, the 25th
International Conference on Pattern Recognitio
Runtime Analysis of Probabilistic Crowding and Restricted Tournament Selection for Bimodal Optimisation
Many real optimisation problems lead to multimodal domains and so require the identifi-
cation of multiple optima. Niching methods have been developed to maintain the population
diversity, to investigate many peaks in parallel and to reduce the effect of genetic drift. Using
rigorous runtime analysis, we analyse for the first time two well known niching methods: probabilistic
crowding and restricted tournament selection (RTS). We incorporate both methods
into a (µ+1) EA on the bimodal function Twomax where the goal is to find two optima at
opposite ends of the search space. In probabilistic crowding, the offspring compete with their
parents and the survivor is chosen proportionally to its fitness. On Twomax probabilistic
crowding fails to find any reasonable solution quality even in exponential time. In RTS the
offspring compete against the closest individual amongst w (window size) individuals. We
prove that RTS fails if w is too small, leading to exponential times with high probability.
However, if w is chosen large enough, it finds both optima for Twomax in time O(µn log n)
with high probability. Our theoretical results are accompanied by experimental studies that
match the theoretical results and also shed light on parameters not covered by the theoretical
results