Search Data Structure Learning

In our modern world, an enormous amount of data surrounds us, and we are rarely interested in more than a handful of data points at once. It is like searching for needles in a haystack, and in many cases, there is no better algorithm than a random search, which might not be viable. Previously proposed algorithms for efficient database access are made for particular applications such as finding the min/max, finding all points within a range or finding the k-nearest neighbours. Consequently, there is a lack of versatility concerning what we can search when it comes to a gigantic database. In this work, we propose Search Data Structure Learning (SDSL), a generalization of the standard Search Data Structure (SDS) in which the machine has to learn how to search in the database. To evaluate approaches in this field, we propose a novel metric called Sequential Search Work Ratio (SSWR), a natural way of measuring a search's efficiency and quality. Finally, we inaugurate the field with the Efficient Learnable Binary Access (ELBA), a family of models for Search Data Structure Learning. It requires a means to train two parametric functions and a search data structure for binary codes. For the training, we developed a novel loss function, the F-beta Loss. For the SDS, we describe the Multi-Bernoulli Search (MBS), a novel approach for probabilistic binary codes. Finally, we exhibit the F-beta Loss and the MBS synergy by experimentally showing that it is at least twice as better than using the alternative loss functions of MIHash and HashNet and twenty times better than with another SDS based on the Hamming radius.