Proceedings of the ... International Conference on Machine Learning and Applications. International Conference on Machine Learning and Applications最新文献

There is a growing need for fast and accurate methods for testing developmental neurotoxicity across several chemical exposure sources. Current approaches, such as in vivo animal studies, and assays of animal and human primary cell cultures, suffer from challenges related to time, cost, and applicability to human physiology. Prior work has demonstrated success employing machine learning to predict developmental neurotoxicity using gene expression data collected from human 3D tissue models exposed to various compounds. The 3D model is biologically similar to developing neural structures, but its complexity necessitates extensive expertise and effort to employ. By instead focusing solely on constructing an assay of developmental neurotoxicity, we propose that a simpler 2D tissue model may prove sufficient. We thus compare the accuracy of predictive models trained on data from a 2D tissue model with those trained on data from a 3D tissue model, and find the 2D model to be substantially more accurate. Furthermore, we find the 2D model to be more robust under stringent gene set selection, whereas the 3D model suffers substantial accuracy degradation. While both approaches have advantages and disadvantages, we propose that our described 2D approach could be a valuable tool for decision makers when prioritizing neurotoxicity screening.

Individuals with cochlear implants (CIs) experience more difficulty understanding speech in reverberant environ-ments than normal hearing listeners. As a result, recent research has targeted mitigating the effects of late reverberant signal reflections in CIs by using a machine learning approach to detect and delete affected segments in the CI stimulus pattern. Previous work has trained electrode-specific classification models to mitigate late reverberant signal reflections based on features extracted from only the acoustic activity within the electrode of interest. Since adjacent CI electrodes tend to be activated concurrently during speech, we hypothesized that incorporating additional information from the other electrode channels, termed cross-channel information, as features could improve classification performance. Cross-channel information extracted in real-world conditions will likely contain errors that will impact classification performance. To simulate extracting cross-channel information in realistic conditions, we developed a graphical model based on the Ising model to systematically introduce errors to specific types of cross-channel information. The Ising-like model allows us to add errors while maintaining the important geometric information contained in cross-channel information, which is due to the spectro-temporal structure of speech. Results suggest the potential utility of leveraging cross-channel information to improve the performance of the reverberation mitigation algorithm from the baseline channel-based features, even when the cross-channel information contains errors.

Document retrieval (DR) forms an important component in end-to-end question-answering (QA) systems where particular answers are sought for well-formed questions. DR in the QA scenario is also useful by itself even without a more involved natural language processing component to extract exact answers from the retrieved documents. This latter step may simply be done by humans like in traditional search engines granted the retrieved documents contain the answer. In this paper, we take advantage of datasets made available through the BioASQ end-to-end QA shared task series and build an effective biomedical DR system that relies on relevant answer snippets in the BioASQ training datasets. At the core of our approach is a question-answer sentence matching neural network that learns a measure of relevance of a sentence to an input question in the form of a matching score. In addition to this matching score feature, we also exploit two auxiliary features for scoring document relevance: the name of the journal in which a document is published and the presence/absence of semantic relations (subject-predicate-object triples) in a candidate answer sentence connecting entities mentioned in the question. We rerank our baseline sequential dependence model scores using these three additional features weighted via adaptive random research and other learning-to-rank methods. Our full system placed 2nd in the final batch of Phase A (DR) of task B (QA) in BioASQ 2018. Our ablation experiments highlight the significance of the neural matching network component in the full system.