9,730 research outputs found
iSeqQC: a tool for expression-based quality control in RNA sequencing.
BACKGROUND: Quality Control in any high-throughput sequencing technology is a critical step, which if overlooked can compromise an experiment and the resulting conclusions. A number of methods exist to identify biases during sequencing or alignment, yet not many tools exist to interpret biases due to outliers.
RESULTS: Hence, we developed iSeqQC, an expression-based QC tool that detects outliers either produced due to variable laboratory conditions or due to dissimilarity within a phenotypic group. iSeqQC implements various statistical approaches including unsupervised clustering, agglomerative hierarchical clustering and correlation coefficients to provide insight into outliers. It can be utilized through command-line (Github: https://github.com/gkumar09/iSeqQC) or web-interface (http://cancerwebpa.jefferson.edu/iSeqQC). A local shiny installation can also be obtained from github (https://github.com/gkumar09/iSeqQC).
CONCLUSION: iSeqQC is a fast, light-weight, expression-based QC tool that detects outliers by implementing various statistical approaches
Handwriting styles: benchmarks and evaluation metrics
Evaluating the style of handwriting generation is a challenging problem,
since it is not well defined. It is a key component in order to develop in
developing systems with more personalized experiences with humans. In this
paper, we propose baseline benchmarks, in order to set anchors to estimate the
relative quality of different handwriting style methods. This will be done
using deep learning techniques, which have shown remarkable results in
different machine learning tasks, learning classification, regression, and most
relevant to our work, generating temporal sequences. We discuss the challenges
associated with evaluating our methods, which is related to evaluation of
generative models in general. We then propose evaluation metrics, which we find
relevant to this problem, and we discuss how we evaluate the evaluation
metrics. In this study, we use IRON-OFF dataset. To the best of our knowledge,
there is no work done before in generating handwriting (either in terms of
methodology or the performance metrics), our in exploring styles using this
dataset.Comment: Submitted to IEEE International Workshop on Deep and Transfer
Learning (DTL 2018
BinaryConnect: Training Deep Neural Networks with binary weights during propagations
Deep Neural Networks (DNN) have achieved state-of-the-art results in a wide
range of tasks, with the best results obtained with large training sets and
large models. In the past, GPUs enabled these breakthroughs because of their
greater computational speed. In the future, faster computation at both training
and test time is likely to be crucial for further progress and for consumer
applications on low-power devices. As a result, there is much interest in
research and development of dedicated hardware for Deep Learning (DL). Binary
weights, i.e., weights which are constrained to only two possible values (e.g.
-1 or 1), would bring great benefits to specialized DL hardware by replacing
many multiply-accumulate operations by simple accumulations, as multipliers are
the most space and power-hungry components of the digital implementation of
neural networks. We introduce BinaryConnect, a method which consists in
training a DNN with binary weights during the forward and backward
propagations, while retaining precision of the stored weights in which
gradients are accumulated. Like other dropout schemes, we show that
BinaryConnect acts as regularizer and we obtain near state-of-the-art results
with BinaryConnect on the permutation-invariant MNIST, CIFAR-10 and SVHN.Comment: Accepted at NIPS 2015, 9 pages, 3 figure
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