Efficient end-to-end learning for quantizable representations

Embedding representation learning via neural networks is at the core foundation of modern similarity based search. While much effort has been put in developing algorithms for learning binary hamming code representations for search efficiency, this still requires a linear scan of the entire dataset per each query and trades off the search accuracy through binarization. To this end, we consider the problem of directly learning a quantizable embedding representation and the sparse binary hash code end-to-end which can be used to construct an efficient hash table not only providing significant search reduction in the number of data but also achieving the state of the art search accuracy outperforming previous state of the art deep metric learning methods. We also show that finding the optimal sparse binary hash code in a mini-batch can be computed exactly in polynomial time by solving a minimum cost flow problem. Our results on Cifar-100 and on ImageNet datasets show the state of the art search accuracy in precision@k and NMI metrics while providing up to 98X and 478X search speedup respectively over exhaustive linear search. The source code is available at https://github.com/maestrojeong/Deep-Hash-Table-ICML18Comment: Accepted and to appear at ICML 2018. Camera ready versio

Attractiveness of Three Gravid Trap Infusions for Ovipositing Polynesian Tiger Mosquitoes (Aedes polynesiensis) in American Samoa

The Polynesian tiger mosquito, Aedes polynesiensis, is a carrier of filariasis, chikungunya and dengue in American Samoa. The most commonly used tool for monitoring Ae. polynesiensis is the BG Sentinel trap; however, this trap catches relatively few mosquitoes and targets females searching for a blood meal. Recently developed gravid traps targeting females that have already blood-fed and are searching for oviposition sites may be a better alternative. But results of the initial trials of these gravid traps using a weedy grass hay infusion lure were disappointing. This experiment evaluates two alternative infusions made from dried banana leaves and Bermuda grass hay. We hypothesize that the Bermuda grass or banana leaves infusion may be more attractive to Ae. polynesiensis than the weedy grass infusion. Ovicups containing weedy grass hay, dried banana leaves and Bermuda grass infusions were arranged in a randomized complete block design in mixed vegetation on the American Samoa Community College campus. After three days, the egg sheets were collected from the cups, the eggs hatched, and the larvae raised to fourth instar to identify the species. The experiment was repeated three times and the average eggs were compared to determine the most effective infusion. If either the banana leaves or the Bermuda grass infusion proves more attractive than the weedy grass hay infusion, use of the more effective infusion in the new gravid traps may improve their efficacy and provide a valuable new tool for future research and better management of this important disease vector

Parsimonious Black-Box Adversarial Attacks via Efficient Combinatorial Optimization

Solving for adversarial examples with projected gradient descent has been demonstrated to be highly effective in fooling the neural network based classifiers. However, in the black-box setting, the attacker is limited only to the query access to the network and solving for a successful adversarial example becomes much more difficult. To this end, recent methods aim at estimating the true gradient signal based on the input queries but at the cost of excessive queries. We propose an efficient discrete surrogate to the optimization problem which does not require estimating the gradient and consequently becomes free of the first order update hyperparameters to tune. Our experiments on Cifar-10 and ImageNet show the state of the art black-box attack performance with significant reduction in the required queries compared to a number of recently proposed methods. The source code is available at https://github.com/snu-mllab/parsimonious-blackbox-attack.Comment: Accepted and to appear at ICML 201