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Interesting Computational Complexity Question

@machinelearnbot

Thus, on average, you need M(k) SUM{ j * L(k,j) } n/(n-k) shots to create p(k), where the sum is over all positive integers j 0, 1, 2... The total number of operations is thus (on average) SUM{M(k)} n * {1 1/2 1/3 ... 1/n}, where the sum is on k 0,1,...,n-1. This turns out to be O(n log n). Note that in terms of data structure, we need an auxiliary array A of size n, initialized to 0. When p(k) is created (in the last, successful shot at iteration k), we update A as follows: A[p(k)] 1. This array is used to check if p(k) is different from p(0), p(1), ..., p(k-1), or in other words, if A[p(k)] 0.


Exploring Models and Data for Image Question Answering

Neural Information Processing Systems

This work aims to address the problem of image-based question-answering (QA) with new models and datasets. In our work, we propose to use neural networks and visual semantic embeddings, without intermediate stages such as object detection and image segmentation, to predict answers to simple questions about images. Our model performs 1.8 times better than the only published results on an existing image QA dataset. We also present a question generation algorithm that converts image descriptions, which are widely available, into QA form. We used this algorithm to produce an order-of-magnitude larger dataset, with more evenly distributed answers.


What are some good questions to ask when assessing an ML model?

#artificialintelligence

A "good" ML model is context specific (e.g., high ROC score is not sufficient to declare a model is a great classifier for your particular use case). Some of this you can ask the person who developed the model, but others are things you need to ask before using the model. And possibly many more depending on how you plan to use the model (e.g., run time complexity, memory complexity, distributed or single core, uses data you can't get, etc)


Generalized Consistent Query Answering under Existential Rules

AAAI Conferences

Previous work has proposed consistent query answering as a way to resolve inconsistencies in ontologies. In these approaches to consistent query answering, however, only inconsistencies due to errors in the underlying database are considered. In this paper, we additionally assume that ontological axioms may be erroneous, and that some database atoms and ontological axioms may not be removed to resolve inconsistencies. This problem is especially well suited in debugging mappings between distributed ontologies. We define two different semantics, one where ontological axioms as a whole are ignored to resolve an inconsistency, and one where only some of their instances are ignored. We then give a precise picture of the complexity of consistent query answering under these two semantics when ontological axioms are encoded as different classes of existential rules. In the course of this, we also close two open complexity problems in standard consistent query answering under existential rules.


The Stanford Question Answering Dataset

@machinelearnbot

Stanford Question Answering Dataset (SQuAD) is a new reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage. With 100,000 question-answer pairs on 500 articles, SQuAD is significantly larger than previous reading comprehension datasets. We've built a few resources to help you get started with the dataset. Download a copy of the dataset (distributed under the CC BY-SA 4.0 license): To evaluate your models, we have also made available the evaluation script we will use for official evaluation, along with a sample prediction file that the script will take as input. To run the evaluation, use python evaluate-v1.1.py