103 research outputs found
Improving classification accuracy of feedforward neural networks for spiking neuromorphic chips
Deep Neural Networks (DNN) achieve human level performance in many image
analytics tasks but DNNs are mostly deployed to GPU platforms that consume a
considerable amount of power. New hardware platforms using lower precision
arithmetic achieve drastic reductions in power consumption. More recently,
brain-inspired spiking neuromorphic chips have achieved even lower power
consumption, on the order of milliwatts, while still offering real-time
processing.
However, for deploying DNNs to energy efficient neuromorphic chips the
incompatibility between continuous neurons and synaptic weights of traditional
DNNs, discrete spiking neurons and synapses of neuromorphic chips need to be
overcome. Previous work has achieved this by training a network to learn
continuous probabilities, before it is deployed to a neuromorphic architecture,
such as IBM TrueNorth Neurosynaptic System, by random sampling these
probabilities.
The main contribution of this paper is a new learning algorithm that learns a
TrueNorth configuration ready for deployment. We achieve this by training
directly a binary hardware crossbar that accommodates the TrueNorth axon
configuration constrains and we propose a different neuron model.
Results of our approach trained on electroencephalogram (EEG) data show a
significant improvement with previous work (76% vs 86% accuracy) while
maintaining state of the art performance on the MNIST handwritten data set.Comment: IJCAI-2017. arXiv admin note: text overlap with arXiv:1605.0774
Modeling Long-term Dependencies and Short-term Correlations in Patient Journey Data with Temporal Attention Networks for Health Prediction
Building models for health prediction based on Electronic Health Records
(EHR) has become an active research area. EHR patient journey data consists of
patient time-ordered clinical events/visits from patients. Most existing
studies focus on modeling long-term dependencies between visits, without
explicitly taking short-term correlations between consecutive visits into
account, where irregular time intervals, incorporated as auxiliary information,
are fed into health prediction models to capture latent progressive patterns of
patient journeys. We present a novel deep neural network with four modules to
take into account the contributions of various variables for health prediction:
i) the Stacked Attention module strengthens the deep semantics in clinical
events within each patient journey and generates visit embeddings, ii) the
Short-Term Temporal Attention module models short-term correlations between
consecutive visit embeddings while capturing the impact of time intervals
within those visit embeddings, iii) the Long-Term Temporal Attention module
models long-term dependencies between visit embeddings while capturing the
impact of time intervals within those visit embeddings, iv) and finally, the
Coupled Attention module adaptively aggregates the outputs of Short-Term
Temporal Attention and Long-Term Temporal Attention modules to make health
predictions. Experimental results on MIMIC-III demonstrate superior predictive
accuracy of our model compared to existing state-of-the-art methods, as well as
the interpretability and robustness of this approach. Furthermore, we found
that modeling short-term correlations contributes to local priors generation,
leading to improved predictive modeling of patient journeys.Comment: 10 pages, 4 figures, accepted at ACM BCB 202
Mejora de un corpus extraĂdo automáticamente para desambiguar tĂ©rminos del UMLS Metathesaurus
Anotar a mano un conjunto de ejemplos para entrenar mĂ©todos de aprendizaje automático para desambiguar anotaciones con conceptos del UMLS Metathesaurus no es posible debido a su elevado coste. En este artĂculo, evaluamos dos mĂ©todos para mejorar la calidad de un corpus obtenido de manera automática. El primer mĂ©todo busca tĂ©rminos especĂficos y el segundo filtra falsos positivos. La combinaciĂłn de los dos mĂ©todos obtiene una mejora de 6% en F-measure y un 8% en recall, comparado con el corpus original extraĂdo de manera automática.Manually annotated data is expensive, so manually covering a large terminological resource like the UMLS Metathesaurus is infeasible. In this paper, we evaluate two approaches used to improve the quality of an automatically extracted corpus to train statistical learners to performWSD. The first one contributes to more specific terms while the second filters out false positives. Using both approaches, we have obtained an improvement on the original automatic extracted corpus of approximately 6% in F-measure and 8% in recall
Measuring prediction capacity of individual verbs for the identification of protein interactions
AbstractMotivation: The identification of events such as protein–protein interactions (PPIs) from the scientific literature is a complex task. One of the reasons is that there is no formal syntax to denote such relations in the scientific literature. Nonetheless, it is important to understand such relational event representations to improve information extraction solutions (e.g., for gene regulatory events).In this study, we analyze publicly available protein interaction corpora (AIMed, BioInfer, BioCreAtIve II) to determine the scope of verbs used to denote protein interactions and to measure their predictive capacity for the identification of PPI events. Our analysis is based on syntactical language patterns. This restriction has the advantage that the verb mention is used as the independent variable in the experiments enabling comparability of results in the usage of the verbs. The initial selection of verbs has been generated from a systematic analysis of the scientific literature and existing corpora for PPIs.We distinguish modifying interactions (MIs) such as posttranslational modifications (PTMs) from non-modifying interactions (NMIs) and assumed that MIs have a higher predictive capacity due to stronger scientific evidence proving the interaction. We found that MIs are less frequent in the corpus but can be extracted at the same precision levels as PPIs. A significant portion of correct PPI reportings in the BioCreAtIve II corpus use the verb “associate”, which semantically does not prove a relation.The performance of every monitored verb is listed and allows the selection of specific verbs to improve the performance of PPI extraction solutions. Programmatic access to the text processing modules is available online (www.ebi.ac.uk/webservices/whatizit/info.jsf) and the full analysis of Medline abstracts will be made through the Web pages of the Rebholz group
Grey-box Adversarial Attack And Defence For Sentiment Classification
We introduce a grey-box adversarial attack and defence framework for
sentiment classification. We address the issues of differentiability, label
preservation and input reconstruction for adversarial attack and defence in one
unified framework. Our results show that once trained, the attacking model is
capable of generating high-quality adversarial examples substantially faster
(one order of magnitude less in time) than state-of-the-art attacking methods.
These examples also preserve the original sentiment according to human
evaluation. Additionally, our framework produces an improved classifier that is
robust in defending against multiple adversarial attacking methods. Code is
available at: https://github.com/ibm-aur-nlp/adv-def-text-dist
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