###### convolutional neural network

- Our method purely relies on DNA sequences to predict enhancers in an end-to-end manner by using a deep convolutional neural network (CNN). (biomedcentral.com)
- You will learn how to build Convolutional Neural Network (CNN) architectures with these blocks and how to quickly solve a new task using so-called pre-trained models. (coursera.org)
- A Siamese Hierarchical Convolutional Neural Network (SHCNN), which integrates local and more global representations of a message, is first presented to estimate the conversation-level similarity between closely posted messages. (fxpal.com)
- Alexnet is the name of the Convolutional Neural Network architecture that won the ILSVRC 2012 competition by a large margin and was responsible for a resurgence of interest in CNNs for Image Recognition. (wildml.com)

###### Networks

- The goal of this course is to give learners basic understanding of modern neural networks and their applications in computer vision and natural language understanding. (coursera.org)
- The course starts with a recap of linear models and discussion of stochastic optimization methods that are crucial for training deep neural networks. (coursera.org)
- Learners will study all popular building blocks of neural networks including fully connected layers, convolutional and recurrent layers. (coursera.org)
- Graves, A.: Neural Networks. (springer.com)
- In the experiments, word-RNNs (Recurrent Neural Networks) show good results for both cases, while character-based RNNs (char-RNNs) only succeed to learn chord progressions. (archive.org)
- Sequence modeling with neural networks has lead to powerful models of symbolic music data. (archive.org)
- Here we investigate how artificial neural networks can be trained on a large corpus of melodies and turned into automated music composers able to generate new melodies coherent with the style they have been trained on. (archive.org)
- We employ gated recurrent unit networks that have been shown to be particularly efficient in learning complex. (archive.org)
- Chang Xu, Gang Wang, Xiaoguang Liu, Tie-Yan Liu, Health Status Assessment and Failure Prediction for Hard Drives with Recurrent Neural Networks , IEEE Transactions on Computers , 2016. (microsoft.com)
- The connection with these and convolutional neural networks is suggestive for the same reason. (danmackinlay.name)
- Alex Graves Generating Sequences With Recurrent Neural Networks , generates handwriting. (danmackinlay.name)
- Charming connection with my other research into acoustics , what I would call "Gerzon allpass" filters are now hip for use neural networks because of favourable normalisation characteristics. (danmackinlay.name)
- Kalman fiters, but rebranded in the fine neural networks tradition of taking something uncontroversial from another field and putting the word "neural" in front. (danmackinlay.name)
- Neural Networks , 21(5), 786-795. (danmackinlay.name)
- Neural networks are computing systems with interconnected nodes that work much like neurons in the human brain. (sas.com)
- The application of neural networks to artificial intelligence (AI). (sas.com)
- However, over time, researchers shifted their focus to using neural networks to match specific tasks, leading to deviations from a strictly biological approach. (sas.com)
- As structured and unstructured data sizes increased to big data levels, people developed deep learning systems, which are essentially neural networks with many layers. (sas.com)
- Why are neural networks important? (sas.com)
- Neural networks are also ideally suited to help people solve complex problems in real-life situations. (sas.com)
- Our first goal for these neural networks, or models, is to achieve human-level accuracy. (sas.com)
- There are different kinds of deep neural networks - and each has advantages and disadvantages, depending upon the use. (sas.com)
- Convolutional neural networks (CNNs) contain five types of layers: input, convolution, pooling, fully connected and output. (sas.com)
- Convolutional neural networks have popularized image classification and object detection. (sas.com)
- Recurrent neural networks (RNNs) use sequential information such as time-stamped data from a sensor device or a spoken sentence, composed of a sequence of terms. (sas.com)
- Distributed representations, simple recurrent networks, and grammatical structure. (springer.com)
- Automatic text classification is usually done by using a prelabeled training set and applying various machine learning methods such as naive Bayes, support vector machines, artificial neural networks, or hybrid approaches that combine various machine learning methods to improve the efficiency of classification. (meta-guide.com)
- Although a large body of literature has been devoted to the role of O 2 in the CNS, how neural networks function during long-term exposures to low but physiological O 2 partial pressure (P o 2 ) has never been studied. (jneurosci.org)
- In conclusion, we propose that (1) O 2 has specific neuromodulator-like actions in the CNS and that (2) the physiological role of this reduction of activity and energy expenditure could be a key adaptation for tolerating low but physiological P o 2 in sensitive neural networks. (jneurosci.org)
- This raises the question as to how neural networks operate at low but physiological P o 2 . (jneurosci.org)
- IDSIA's scientific co-director Juergen Schmidhuber has been interviewed by Neue Zürcher Zeitung on Artificial Intelligence, Deep Neural Networks, and what the future beholds. (idsia.ch)
- We recently open-sourced a new neural networks library called Brainstorm, developed over the past year at the Swiss AI Lab IDSIA by PhD students Klaus Greff and Rupesh Srivastava. (idsia.ch)
- Brainstorm is designed to make neural networks fast, flexible and fun. (idsia.ch)
- Brainstorm already has a robust base feature set, including support for recurrent neural networks (RNNs) such as LSTM, Clockwork, 2D Convolution/Pooling and Highway layers on CPU/GPU. (idsia.ch)
- The Recurrent Neural Networks developed by the Dalle Molle Institute for Artificial Intelligence (IDSIA, USI-SUPSI) constitute the foundations of an important technological innovation announced recently by Google through an official release (http://googleresearch.blogspot.ch/2015/09/google-voice-search-faster-and-more.html) and financed by the Swiss National Fund for scientific research. (idsia.ch)
- Working in the space of context vectors generated by sequence-to-sequence recurrent neural networks, this simple and domain-agnostic technique is demonstrated to be effective for both static and sequential data. (perimeterinstitute.ca)
- Unsurprisingly, Deep Learning (DL) was by far the most popular research topic, with about every fourth of more than 2,500 submitted papers (and 568 accepted papers) dealing with deep neural networks. (aylien.com)
- Yann LeCun muses in his keynote that the development of GANs parallels the history of neural networks themselves: They were poorly understood and hard to get to work in the beginning and only took off once researchers figured out the right tricks and learned how to make them work. (aylien.com)
- The purpose of this post is to give students of neural networks an intuition about the functioning of recurrent neural networks and purpose and structure of a prominent RNN variation, LSTMs. (skymind.ai)
- Research shows them to be one of the most powerful and useful type of neural network, alongside the attention mechanism and memory networks . (skymind.ai)
- Since recurrent networks possess a certain type of memory, and memory is also part of the human condition, we'll make repeated analogies to memory in the brain. (skymind.ai)
- Recurrent networks, on the other hand, take as their input not just the current input example they see, but also what they have perceived previously in time. (skymind.ai)
- So recurrent networks have two sources of input, the present and the recent past, which combine to determine how they respond to new data, much as we do in life. (skymind.ai)
- Recurrent networks are distinguished from feedforward networks by that feedback loop connected to their past decisions, ingesting their own outputs moment after moment as input. (skymind.ai)
- It is often said that recurrent networks have memory. (skymind.ai)
- 2 Adding memory to neural networks has a purpose: There is information in the sequence itself, and recurrent nets use it to perform tasks that feedforward networks can't. (skymind.ai)
- Although previous researchers have explored the potential of this network to solve combinatorial optimization problems or store reoccurring activity patterns as attractors of its deterministic dynamics, a basic open problem is to design a family of Hopfield networks with a number of noise-tolerant memories that grows exponentially with neural population size. (readbyqxmd.com)
- In this work, we present an approach for learning attribute representations with convolutional neural networks(CNNs). (springer.com)
- We then construct a variety of neural networks with different architectures and show the usefulness of such techniques as max-pooling and batch normalization in our method. (biomedcentral.com)
- Typically, these reviews consider RNNs that are artificial neural networks (aRNN) useful in technological applications. (scholarpedia.org)
- To complement these contributions, the present summary focuses on biological recurrent neural networks (bRNN) that are found in the brain . (scholarpedia.org)
- 0\). The McCulloch-Pitts model had an influence far beyond the field of neural networks through its influence on von Neumann's development of the digital computer. (scholarpedia.org)
- Artificial neural networks, mimicking a behavior of biological neurons, have been a powerful tool for pattern recognition, and have been utilized in recent years with improved performance over traditional statistical learning tools. (engineeringjobs4u.co.uk)
- Feed-forward neural networks perform well for computer vision and speech recognition by picking up various spatial features of data, while recurrent neural networks are powerful tools that allow learning dependencies in sequential data, such as text and music. (engineeringjobs4u.co.uk)
- To allow Neural Networks to learn complex decision boundaries, we apply a nonlinear activation function to some of its layers. (wildml.com)
- Affine layers are often added on top of the outputs of Convolutional Neural Networks or Recurrent Neural Networks before making a final prediction. (wildml.com)
- Alexnet was introduced in ImageNet Classification with Deep Convolutional Neural Networks . (wildml.com)
- Average-Pooling is a pooling technique used in Convolutional Neural Networks for Image Recognition. (wildml.com)
- Neural networks applications by Patrick K. Simpson, Institute of Electrical and Electronics Engineers. (google.com)
- Recently it has been addressed using neural networks, in particular by Neural Turing Machines (NTMs). (arxiv.org)
- To achieve these results we introduce a technique for training deep recurrent networks: parameter sharing relaxation. (arxiv.org)
- Otherwise, read on for examples of how autograd for Torch makes writing neural networks much simpler and cleaner. (twitter.com)
- For more complex graphs, like recurrent networks, the nn container approach can become quite tedious. (twitter.com)
- The edited book aims to reflect the latest progresses made in different areas of neural computation, including theoretical neural computation, biologically plausible neural modeling, computational cognitive science, artificial neural networks - architectures and learning algorithms and their applications in real-world problems. (springer.com)

###### LSTM

- This paper presents a model based on Recurrent Neural Network architecture, in particular LSTM, for modeling the behavior of Large Hadron Collider superconducting magnets. (springer.com)
- We present a generative long short-term memory (LSTM) recurrent neural network (RNN) for combinatorial de novo peptide design. (readbyqxmd.com)

###### RNNs

- All data is considered sequential, and RNNs are first-class citizens. (idsia.ch)

###### architectures

- You will learn about several Recurrent Neural Network (RNN) architectures and how to apply them for different tasks with sequential input/output. (coursera.org)
- Conventional neural network architectures are often simplistic feed forward or recurrent models where the timing of events is not important to the processing being done. (cerfnet.com)

###### neurons

- In a traditional recurrent neural network, during the gradient back-propagation phase, the gradient signal can end up being multiplied a large number of times (as many as the number of timesteps) by the weight matrix associated with the connections between the neurons of the recurrent hidden layer. (danmackinlay.name)
- They wrote a seminal paper on how neurons may work and modeled their ideas by creating a simple neural network using electrical circuits. (sas.com)
- A neural network model is presented which extends Hopfield's model by adding hidden neurons. (ubc.ca)
- The resulting model remains fully recurrent, and still learns by prescriptive Hebbian learning, but the hidden neurons give it power and flexibility which were not available in Hopfield's original network. (ubc.ca)
- The Hopfield recurrent neural network is a classical auto-associative model of memory, in which collections of symmetrically coupled McCulloch-Pitts binary neurons interact to perform emergent computation. (readbyqxmd.com)
- A recurrent neural network (RNN) is any network whose neurons send feedback signals to each other. (scholarpedia.org)

###### gated recurrent unit

- It is based on a type of convolutional gated recurrent unit and, like the NTM, is computationally universal. (arxiv.org)

###### prediction

- This is a simple framework for time-series prediction of the time to the next event applicable when we have any or all of the problems of continuous or discrete time, right censoring, recurrent events, temporal patterns, time varying covariates or time series of varying lengths. (chalmers.se)
- Martinsson2017, author={Martinsson, Egil}, title={WTTE-RNN : Weibull Time To Event Recurrent Neural Network A model for sequential prediction of time-to-event in the case of discrete or continuous censored data, recurrent events or time-varying covariates}, abstract={In this thesis we propose a new model for predicting time to events: the Weibull Time To Event RNN. (chalmers.se)

###### biologically plausible

- Applied Neurodynamics …is developing platforms that will support the detailed investigation of the behavior of neural assemblies with biologically plausible dynamics. (cerfnet.com)

###### feedforward

- To understand recurrent nets, first you have to understand the basics of feedforward nets . (skymind.ai)
- Backpropagation is an algorithm to efficiently calculate the gradients in a Neural Network, or more generally, a feedforward computational graph. (wildml.com)

###### distributed neural network

- namely, Hebbian-style learning in a partially recurrent distributed neural network. (royalsocietypublishing.org)
- Moreover, interference effects between chunks will follow a similarity gradient typical of other distributed neural network memory systems. (royalsocietypublishing.org)

###### representations

- In this paper, we propose a deep Long Short Term Memory recurrent neural network model with a memory/attention mechanism, for the successive Point-of-Interest recommendation problem, that captures both the sequential, and temporal/spatial characteristics into its learned representations. (springer.com)

###### computational

- The original goal of the neural network approach was to create a computational system that could solve problems like a human brain. (sas.com)
- Indeed, recent neurophysiological, behavioural and computational studies show that sequential sentence structure has considerable explanatory power and that hierarchical processing is often not involved. (royalsocietypublishing.org)
- The discovery of normalization in value coding provides a computational framework for examining decision-related neural activity. (jneurosci.org)

###### patterns

- Recurrent nets are a type of artificial neural network designed to recognize patterns in sequences of data, such as text, genomes, handwriting, the spoken word, or numerical times series data emanating from sensors, stock markets and government agencies. (skymind.ai)
- RNN models capture patterns in sequential data and generate new data instances from the learned context. (readbyqxmd.com)
- What turned out to be most fruitful in learning the context of this complicated customer behavior, however, was a neural network model that could detect usage patterns indicative of sellers experiencing a problem. (engineeringjobs4u.co.uk)
- The similarity of sparseness patterns for both neural events, and distinct spread of activity may reflect similarity of local processing, and differences in the flow of information through cortical circuits, respectively. (pubmedcentralcanada.ca)

###### data

- Then max pooling is used to get optimal features from the entire encoding sequential data. (biomedcentral.com)
- Dr. Taylor's research focuses on statistical machine learning, with an emphasis on deep learning and sequential data. (perimeterinstitute.ca)
- Continuous-nonlinear network laws typically arose from an analysis of behavioral or neural data. (scholarpedia.org)

###### biological

- Current work is focused on design of efficient electronic embodiments of the key elements of biological neural codes and how they are processed. (cerfnet.com)

###### spatial

- Anderson (1968) initially described his intuitions about neural pattern recognition using a spatial cross-correlation function. (scholarpedia.org)

###### Advances

- Advances in Neural Information Processing Systems 25, pp. 1097-1105. (springer.com)

###### cognitive

- If linguistic phenomena can be explained by sequential rather than hierarchical structure, this will have considerable impact in a wide range of fields, such as linguistics, ethology, cognitive neuroscience, psychology and computer science. (royalsocietypublishing.org)
- From different perspectives, neural computation provides an alternative methodology to understand brain functions and cognitive process and to solve challenging real-world problems effectively. (springer.com)
- Cognitive therapy displayed a similar prophylactic effect to maintenance medication.9 The criterion for recurrent depression (at least 1 previous episode of depression) was different, however, from that endorsed by Frank et al5 ( 3 episodes of unipolar depression, with the immediately preceding episode being no more than 2 years before the onset of the present episode). (scribd.com)

###### behavior

- Just as human memory circulates invisibly within a body, affecting our behavior without revealing its full shape, information circulates in the hidden states of recurrent nets. (skymind.ai)
- Neurodynamics …is a term used here to represent a level of abstraction in the study of information processing in neural network activity, and to use this perspective to bridge from neuroscience to conscious experience and behavior. (cerfnet.com)

###### graphs

- 20 ] proposed a sequential generation approach for graphs. (biomedcentral.com)

###### temporal

- Despite the central importance of temporal processing, its underlying neural mechanisms remain unknown. (jneurosci.org)

###### model

- The proposed model estimates the distribution of time to the next event as having a discrete or continuous Weibull distribution with parameters being the output of a recurrent neural network. (chalmers.se)
- Specifically, the subway network features such as the number of passing stations, waiting time, and transfer times are extracted and a recurrent neural network model is employed to model user behaviors. (hindawi.com)
- We propose a dependency-based deep neural network model for DDI extraction. (biomedcentral.com)
- Recurrent Neural Network Model for Constructive Peptide Design. (readbyqxmd.com)
- An Autoencoder is a Neural Network model whose goal is to predict the input itself, typically through a "bottleneck" somewhere in the network. (wildml.com)
- Neural network model for automatic traffic incident detection by Hojjat Adeli, Ohio. (google.com)
- define a trainable model, a 2-layer neural network, to classify 100-dim -- vectors into 10 categories, using a cross-entropy loss: model = nn.Sequential() model:add( nn.Linear(100, 200) ) model:add( nn.Tanh() ) model:add( nn.Linear(200, 10) ) loss = nn.CrossEntropyCriterion() -- then nn provides methods to compute the gradients. (twitter.com)
- In addition, we demonstrate the generation of multiple reliable, long-lasting sequences in a recurrent network model. (jneurosci.org)

###### nets

- Artificial Neural Nets. (indigo.ca)
- Customer Reviews of Artificial Neural Nets. (indigo.ca)
- Neural nets by United States. (google.com)

###### processes

- Yet, the neural processes controlling access to awareness are still very much unclear. (ugent.be)

###### neuron

- A memory cell is composed of four main elements: an input gate, a neuron with a self-recurrent connection (a connection to itself), a forget gate and an output gate. (danmackinlay.name)

###### computation

- Normalization is a widespread neural computation, mediating divisive gain control in sensory processing and implementing a context-dependent value code in decision-related frontal and parietal cortices. (jneurosci.org)
- Pastor-Bernier and Cisek, 2011 ), where action value is encoded within the context of available options, reinforces the prevalent notion that normalization is a canonical neural computation ( Carandini and Heeger, 2012 ). (jneurosci.org)
- Trend in Neural Computation includes twenty chapters either contributed from leading experts or formed by extending well selected papers presented in the 2005 International Conference on Natural Computation. (springer.com)
- Researchers, graduate students and industrial practitioners in the broad areas of neural computation would benefit from the state-of-the-art work collected in this book. (springer.com)

###### Experiments

- This removes the need for artificial perfusion during in vitro experiments and allows us to study neural network operation at low but physiological P o 2 by simply superfusing the STG in artificial glass vessels. (jneurosci.org)

###### major depressi

- To challenge participants, sad mood was induced with keywords of personal negative life events in individuals with remitted depression [recurrent major depressive disorder (rMDD), n = and matched healthy controls (HCs, n = 30) during functional magnetic resonance imaging. (readbyqxmd.com)

###### arise

- Here, we hypothesize that both transient dynamics and sustained delay-period value coding arise from a recurrent normalization circuit. (jneurosci.org)
- This pattern may arise from recurrent systems such as the hippocampal CA3 region or the entorhinal cortex. (jneurosci.org)

###### mechanism

- These results suggest that a single network mechanism can explain both transient and sustained decision activity, emphasizing the importance of a dynamic view of normalization in neural coding. (jneurosci.org)

###### deep

- In the course project learner will implement deep neural network for the task of image captioning which solves the problem of giving a text description for an input image. (coursera.org)
- In recent years, deep neural network based models have been developed to address such needs and they have made significant progress in relation identification. (biomedcentral.com)
- This module is an introduction to the concept of a deep neural network. (coursera.org)
- This complex neural organization gives rise to a massive signal-processing capability, but almost all of the output from the cerebellar cortex passes through a set of small deep nuclei lying in the white matter interior of the cerebellum. (wikipedia.org)

###### Elman

- Here's a diagram of an early, simple recurrent net proposed by Elman , where the BTSXPE at the bottom of the drawing represents the input example in the current moment, and CONTEXT UNIT represents the output of the previous moment. (skymind.ai)

###### pattern recognition

- The syllogism of Western logic ("All men are mortal…") is a case study in verbalized sequential pattern recognition at multiple levels of encoding. (cerfnet.com)

###### context

- Most important is the potential to extend this idea to context dependent sequential activation of neural emsembles to account for serial order in thought and action . (cerfnet.com)

###### widespread

- Conscious perception is robustly associated with sustained, recurrent interactions between widespread cortical regions. (ugent.be)

###### spontaneous

- Abstract The neural mechanisms underlying the spontaneous, stimulus-independent emergence of intentions and decisions to act are poorly understood. (cyberleninka.org)
- Spontaneous activity plays an important role in the function of neural circuits. (pubmedcentralcanada.ca)

###### dynamics

- Concepts from linear system theory were adapted to represent some aspects of neural dynamics, including solutions of simultaneous linear equations \(Y = AX\) using matrix theory, and concepts about cross-correlation. (scholarpedia.org)
- We propose that neural dynamics provides a critical link to understanding the biophysical basis of value normalization. (jneurosci.org)

###### models

- We consider more expressive non-Markov models, thereby requiring approximate sampling which we provide in the form of an efficient sequential Monte. (archive.org)
- See Sequential Neural Models with Stochastic Layers . (danmackinlay.name)

###### sensory

- Decoding continuous hind limb joint angles from sensory recordings of neural system provides a feedback for closed-loop control of hind limb movement using functional electrical stimulation. (readbyqxmd.com)

###### representation

- Recent evidence suggests that certain canonical neural computations play a crucial role in the neural representation of value. (jneurosci.org)
- Similar to the address-event representation (AER) and virtual wires, developed at Caltech and U Delaware, respectively, Applied Neurodynamics in 1989 independently developed a communication scheme for neural event messages called the space-time attribute code (STA, 15). (cerfnet.com)

###### Predicting

- Neural network application for predicting the impact of trip reduction strategies by Philip L. Winters, M. Pietrzyk, University of South Florida. (google.com)

###### approach

- The aim of this study was to test the effectiveness of this approach in patients with recurrent depression ( 3 episodes of depression). (scribd.com)

###### crucial

- NC/IC discrimination is crucial for clinical BCIs, particularly when they provide neural control over complex effectors such as exoskeletons. (fichier-pdf.fr)

###### possess

- Sentences trivially possess sequential structure, whereas hierarchical structure is only revealed through certain kinds of linguistic analysis. (royalsocietypublishing.org)