12,807 research outputs found
The age of data-driven proteomics : how machine learning enables novel workflows
A lot of energy in the field of proteomics is dedicated to the application of challenging experimental workflows, which include metaproteomics, proteogenomics, data independent acquisition (DIA), non-specific proteolysis, immunopeptidomics, and open modification searches. These workflows are all challenging because of ambiguity in the identification stage; they either expand the search space and thus increase the ambiguity of identifications, or, in the case of DIA, they generate data that is inherently more ambiguous. In this context, machine learning-based predictive models are now generating considerable excitement in the field of proteomics because these predictive models hold great potential to drastically reduce the ambiguity in the identification process of the above-mentioned workflows. Indeed, the field has already produced classical machine learning and deep learning models to predict almost every aspect of a liquid chromatography-mass spectrometry (LC-MS) experiment. Yet despite all the excitement, thorough integration of predictive models in these challenging LC-MS workflows is still limited, and further improvements to the modeling and validation procedures can still be made. In this viewpoint we therefore point out highly promising recent machine learning developments in proteomics, alongside some of the remaining challenges
Document Informed Neural Autoregressive Topic Models with Distributional Prior
We address two challenges in topic models: (1) Context information around
words helps in determining their actual meaning, e.g., "networks" used in the
contexts "artificial neural networks" vs. "biological neuron networks".
Generative topic models infer topic-word distributions, taking no or only
little context into account. Here, we extend a neural autoregressive topic
model to exploit the full context information around words in a document in a
language modeling fashion. The proposed model is named as iDocNADE. (2) Due to
the small number of word occurrences (i.e., lack of context) in short text and
data sparsity in a corpus of few documents, the application of topic models is
challenging on such texts. Therefore, we propose a simple and efficient way of
incorporating external knowledge into neural autoregressive topic models: we
use embeddings as a distributional prior. The proposed variants are named as
DocNADEe and iDocNADEe.
We present novel neural autoregressive topic model variants that consistently
outperform state-of-the-art generative topic models in terms of generalization,
interpretability (topic coherence) and applicability (retrieval and
classification) over 7 long-text and 8 short-text datasets from diverse
domains.Comment: AAAI2019. arXiv admin note: substantial text overlap with
arXiv:1808.0379
ARTIFICIAL NEURAL NETWORKS AND THEIR APPLICATIONS IN BUSINESS
In modern software implementations of artificial neural networks the approach inspired by biology has more or less been abandoned for a more practical approach based on statistics and signal processing. In some of these systems, neural networks, or parts of neural networks (such as artificial neurons), are used as components in larger systems that combine both adaptive and non-adaptive elements. There are many problems which are solved with neural networks, especially in business and economic domains.neuron, neural networks, artificial intelligence, feed-forward neural networks, classification
Echo State Networks: analysis, training and predictive control
The goal of this paper is to investigate the theoretical properties, the
training algorithm, and the predictive control applications of Echo State
Networks (ESNs), a particular kind of Recurrent Neural Networks. First, a
condition guaranteeing incremetal global asymptotic stability is devised. Then,
a modified training algorithm allowing for dimensionality reduction of ESNs is
presented. Eventually, a model predictive controller is designed to solve the
tracking problem, relying on ESNs as the model of the system. Numerical results
concerning the predictive control of a nonlinear process for pH neutralization
confirm the effectiveness of the proposed algorithms for the identification,
dimensionality reduction, and the control design for ESNs.Comment: 6 pages,5 figures, submitted to European Control Conference (ECC
Community detection with spiking neural networks for neuromorphic hardware
We present results related to the performance of an algorithm for community
detection which incorporates event-driven computation. We define a mapping
which takes a graph G to a system of spiking neurons. Using a fully connected
spiking neuron system, with both inhibitory and excitatory synaptic
connections, the firing patterns of neurons within the same community can be
distinguished from firing patterns of neurons in different communities. On a
random graph with 128 vertices and known community structure we show that by
using binary decoding and a Hamming-distance based metric, individual
communities can be identified from spike train similarities. Using bipolar
decoding and finite rate thresholding, we verify that inhibitory connections
prevent the spread of spiking patterns.Comment: Conference paper presented at ORNL Neuromorphic Workshop 2017, 7
pages, 6 figure
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