###### implementing recurrent neural n

- Hardware accelerator templates and design frameworks for implementing recurrent neural networks (RNNs) and variants thereof are described. (patents.com)

###### networks

- Recurrent neural networks are a type of neural net that maintain internal memory of the inputs they've seen before, so they can learn about time-dependent structures in streams of data. (infoq.com)
- The increasing accuracy of deep neural networks for solving problems such as speech and image recognition has stoked attention and research devoted to deep learning and AI more generally. (infoq.com)
- This article introduces neural networks, including brief descriptions of feed-forward neural networks and recurrent neural networks, and describes how to build a recurrent neural network that detects anomalies in time series data. (infoq.com)
- There's something magical about Recurrent Neural Networks (RNNs). (pearltrees.com)
- Recurrent Neural Networks (RNNs) model dynamic processing in the brain. (mpg.de)
- 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)
- Recurrent neural networks (RNNs) have been a prominent concept within artificial intelligence. (complex-systems.com)
- They are inspired by biological neural networks (BNNs) and provide an intuitive and abstract representation of how BNNs work. (complex-systems.com)
- Derived from the more generic artificial neural networks (ANNs), the recurrent ones are meant to be used for temporal tasks, such as speech recognition, because they are capable of memorizing historic input. (complex-systems.com)
- We discuss relations between Residual Networks (ResNet), Recurrent Neural Networks (RNNs) and the primate visual cortex. (mit.edu)
- Recurrent neural networks (RNNs) have shown promising performance for language modeling. (arxiv.org)
- Using neural networks to automatically generate text is appealing because they can be trained through examples with no need to manually specify what should be said when. (slideshare.net)
- In this talk, we will provide an overview of the existing algorithms used in neural text generation, such as sequence2sequence models, reinforcement learning, variational methods, and generative adversarial networks. (slideshare.net)
- convert points in that meaning space into language (proto-write) This method uses neural networks, so we call it neural text generation. (slideshare.net)
- A Random Walk Through EMNLP 2017 http://approximatelycorrect.com/2017/09/26/a-random-walk-through-emnlp-2017/ Deep Visual-Semantic Alignments for Generating Image Descriptions http://cs.stanford.edu/people/karpathy/deepimagesent/ The beautiful: as our neural networks get richer, the meaning space will get closer to being able to represent the concepts a small child can understand, and we will get closer to human-level literacy. (slideshare.net)
- Can you give a visual explanation for the back propagation algorithm for neural networks? (codingvideos.net)
- Then, it utilizes deep neural networks (DNNs) and recurrent neural networks (RNNs) to extract the latent features of items and users, respectively. (mdpi.com)
- You will: - Understand how to build and train Recurrent Neural Networks (RNNs), and commonly-used variants such as GRUs and LSTMs. (coursera.org)
- Recurrent Neural Networks (RNNs) have become the state-of-the-art choice for extracting patterns from temporal sequences. (arxiv.org)
- Title: Biologically plausible deep learning for recurrent spiking neural networks. (berkeley.edu)
- In the same vein, we propose a different model for learning in recurrent neural networks (RNNs), known as McCulloch-Pitts processes. (berkeley.edu)
- Keras developers can now use the high-performance MXNet deep learning engine for distributed training of convolutional neural networks (CNNs) and recurrent neural networks (RNNs). (amazon.com)
- At the beginning of the 2000s, a specific type of Recurrent Neural Networks (RNNs) was developed with the name Echo State Network (ESN). (springer.com)
- Schmidhuber, J.: Deep learning in neural networks: an overview. (springer.com)
- Jaeger, H.: The "echo state" approach to analysing and training recurrent neural networks. (springer.com)
- Basterrech, S., Rubino, G.: Echo state queueing networks: a combination of reservoir computing and random neural networks. (springer.com)
- Manjunath, G., Jaeger, H.: Echo state property linked to an input: exploring a fundamental characteristic of recurrent neural networks. (springer.com)
- Neural Networks: Tricks of the Trade. (springer.com)
- In this work, we present a system that performs emotion recognition on video data using both convolutional neural networks (CNNs) and recurrent neural networks (RNNs). (mit.edu)
- 1) Deep Neural Networks, 2) WebSocket/Http and 3) GStreamer. (google.com)
- Automatic Broadcast Radio Subtitle Generation (audio to *.srt) using Deep Neural Networks. (google.com)
- To approach the goal of establishing an End-to-End speech synthesis system, we propose to use character-level recurrent neural networks (RNNs) to directly convert input character sequences into latent linguistic feature vectors. (google.com)
- 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)
- In this work we developed machine learning algorithms for sleep classification: random forest (RF) classification based on features and artificial neural networks (ANNs) working both with features and raw data. (frontiersin.org)
- Our study revealed that deep neural networks (DNNs) working with raw data performed better than feature-based methods. (frontiersin.org)
- In this retrospective study, Recurrent Neural Networks (RNNs), specifically Long Short Term Memory (LSTM) networks, were used for forecasting patient-reported clinical seizures and epileptiform activity from data obtained using the NeuroPace® RNS® System. (aesnet.org)
- We have demonstrated that recurrent neural networks are able to learn and forecast patterns of epileptiform activity from ECoG recordings. (aesnet.org)
- In this thesis, we study online learning with Recurrent Neural Networks (RNNs). (bilkent.edu.tr)
- 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)
- Analog Circuits for Neural Networks: Analog VLSI Neural Learning Circuits (H.C. Card). (barnesandnoble.com)
- Digital Implementations of Neural Networks: A VLSI Pipelined Neuroemulator (J.G. DelgadoFrias et al. (barnesandnoble.com)
- Neural Networks on Multiprocessor Systems and Applications: VLSIImplementation of Associative Memory Systems for Neural Information Processing (A. König, M. Glesner). (barnesandnoble.com)
- A Dataflow Approach for Neural Networks (J.G. DelgadoFrias et al. (barnesandnoble.com)
- Fuzzy logic, neural networks and evolutionary computing techniques are the main tools used. (barnesandnoble.com)
- Modern neural networks are very powerful predictive models, but they are often incapable of recognizing when their predictions may be wrong. (midl.amsterdam)
- I will discuss a method of learning confidence estimates for neural networks that is simple to implement, computationally efficient and produces intuitively interpretable outputs. (midl.amsterdam)
- Recent advances in deep convolutional neural networks (CNNs) have shown significant successes in medical image analysis and particularly in computational histopathology. (midl.amsterdam)
- The relationships between TFs and potential target gene clusters were examined by training recurrent neural networks whose topologies mimic the NMs to which the TFs are classified. (biomedcentral.com)
- Attempts to improve these early AI applications by enhancing the expertise of the physician have been performed by combining of expert systems where knowledge embedded in the system is internally represented by means of frames and rules with artificial neural networks (ANNs) . (deepdyve.com)
- 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)
- To do this, Thoutt used a type of AI now typical for the creation of literature and computers' comprehension of natural languages called "recurrent neural networks" or "RNNs. (themillions.com)
- Creating original text is a more difficult matter, as Angela Fan, of Facebook AI Research, tells New Scientist: "[AI programs] write in a very simplistic way, deciding word by word what to say next…staying on topic is quite difficult for neural model networks because they have no explicit memory. (themillions.com)
- Stochastic effects on convergence dynamics of reaction-diffusion Cohen-Grossberg neural networks (CGNNs) with delays are studied. (springeropen.com)
- In the recent years, the problems of stability of delayed neural networks have received much attention due to its potential application in associative memories, pattern recognition and optimization. (springeropen.com)
- As pointed out by Haykin [ 18 ] that in real nervous systems synaptic transmission is a noisy process brought on by random fluctuations from the release of neurotransmitters and other probabilistic causes, it is of significant importance to consider stochastic effects for neural networks. (springeropen.com)
- In recent years, the dynamic behavior of stochastic neural networks, especially the stability of stochastic neural networks, has become a hot study topic. (springeropen.com)
- In the factual operations, on other hand, diffusion phenomena could not be ignored in neural networks and electric circuits once electrons transport in a nonuniform electromagnetic field. (springeropen.com)
- The delayed neural networks with diffusion terms can commonly be expressed by partial functional differential equation (PFDE). (springeropen.com)
- To study the stability of delayed reaction-diffusion neural networks, for instance, see [ 24 - 31 ], and references therein. (springeropen.com)
- 32 , 33 ] have studied the problem of the almost sure exponential stability and the moment exponential stability of an equilibrium solution for stochastic reaction-diffusion recurrent neural networks with continuously distributed delays and constant delays, respectively. (springeropen.com)
- In [ 36 ], the problem of stochastic exponential stability of the delayed reaction-diffusion recurrent neural networks with Markovian jumping parameters have been investigated. (springeropen.com)
- The term neural networks and deep learning often get thrown around haphazardly. (sflscientific.com)
- There are extensions to the typical neural networks that exhibit adaptive structure as well. (sflscientific.com)
- Our method efficiently applies asymmetric convolutional neural networks (ACNNs) combined with bidirectional long short-term memory (BLSTM) neural networks to predict PSS, leveraging the feature vector dimension of the protein feature matrix. (biomedcentral.com)
- the BLSTM neural networks capture the long-distance interdependencies between amino-acids. (biomedcentral.com)
- Long-term Recurrent Convolutional Networks for Visual Recognition and Description, CVPR 2015. (slideshare.net)
- Video paragraph captioning using hierarchical recurrent neural networks. (slideshare.net)
- Connectionist Temporal Classification: Labelling Unsegmented Sequence Data with Recurrent Neural Networks. (slideshare.net)
- 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)

###### convolutional neural network

- 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)

###### 2016

- 5. 5 (Slides by Marc Bolaños) Pingbo Pan, Zhongwen Xu, Yi Yang,Fei Wu,Yueting Zhuang Hierarchical Recurrent Neural Encoder for Video Representation with Application to Captioning, CVPR 2016. (slideshare.net)

###### back-propagation

- However, traditional training of RNNs using back-propagation through time often suffers from overfitting. (arxiv.org)

###### build a neural network

- To make our discussion concrete, we'll show how to build a neural network using Deeplearning4j, a popular open-source deep-learning library for the JVM. (infoq.com)
- build a neural network to drawing the mapping from X to Y. (coursera.org)

###### nodes

- Neural nets are a type of machine learning model that mimic biological neurons-data comes in through an input layer and flows through nodes with various activation thresholds. (infoq.com)
- A knowledge graph is traversed by receiving a knowledge graph at a deep neural network, the knowledge graph including a plurality of nodes connected by a. (patents.com)
- An artificial neural network consists of a set of nodes, connected by adaptive weights. (sflscientific.com)

###### deep

- Every Noise at Once deep symphonic black metal progressive uplifting trance vintage french electronic Generating Stories about Images Generating Stories about Images Recurrent neural network for generating stories about images Stories are a fundamental human tool that we use to communicate thought. (pearltrees.com)
- Deep neural conversational modeling, especially the sequence to sequence method, is a new approach for spoken dialogue system research. (google.com)
- In this paper, we propose a neural conditional random field (NCRF) deep learning framework to detect cancer metastasis in WSIs. (midl.amsterdam)
- We propose a novel deep neural network method, called DeepACLSTM, to predict 8-category PSS from protein sequence features and profile features. (biomedcentral.com)
- 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)

###### algorithm

- Backpropagation is an algorithm to efficiently calculate the gradients in a Neural Network, or more generally, a feedforward computational graph. (wildml.com)

###### LSTMs

- It has several variants including LSTMs, GRUs and Bidirectional RNNs, which you are going to learn about in this section. (coursera.org)
- As opposed to traditional models for RNNs (such as LSTMs) which are based on continuous-valued neurons operating in discrete time, our model consists of discrete-valued (spiking) neurons operating in continuous time. (berkeley.edu)

###### Bayesian

- We reinterpret standard RNNs as generative models for dynamic stimuli and show that 'recognizing RNNs' resulting from Bayesian inversion of these models combine many important aspects of brain function. (mpg.de)

###### CNNs

- It's popular for its fast and easy prototyping of CNNs and RNNs. (amazon.com)

###### algorithms

- These are machine-learning algorithms that mirror the neural pathways of the human brain, meaning that sequences are linked and have the ability to loop back to previous information, and therefore, inform the next sequence. (themillions.com)

###### Sequences

- Further, Recurrent Neural Nets have some notion of state, being able to "remember" past sequences. (sflscientific.com)

###### sequential

- The work herein provides a method of mapping binary inputs from the task onto the automata and a recurrent architecture for handling the sequential aspects. (complex-systems.com)
- All data is considered sequential, and RNNs are first-class citizens. (idsia.ch)
- You will learn about several Recurrent Neural Network (RNN) architectures and how to apply them for different tasks with sequential input/output. (coursera.org)

###### Captioning

- I still remember when I trained my first recurrent network for Image Captioning. (pearltrees.com)

###### artificial neural

- What is bias in artificial neural network? (codingvideos.net)

###### Classification

- Our results demonstrate the utility of neural network architectures for the classification of sleep. (frontiersin.org)

###### NIPS

- The Conference on Neural Information Processing Systems (NIPS) is one of the two top conferences in machine learning. (aylien.com)

###### nets

- For this blog post, we give a heuristic description of what neural nets are and how they can be used to solve many of today's problems. (sflscientific.com)
- Neural Nets were originally inspired in the 1940s by the human central nervous system. (sflscientific.com)
- With the advent of big data to learn from and computers to perform computations, there has been renewed in Neural Nets. (sflscientific.com)
- These are known as recurrent neural nets (RNNs) that we shall discuss in a subsequent entry. (sflscientific.com)

###### framework

- Maass, W., Natschläger, T., Markram, H.: Real-time computing without stable states: a new framework for a neural computation based on perturbations. (springer.com)
- Following the valence-arousal space framework, two approaches a neural network (NN) model that could predict the valence-arousal ratings of Chinese words. (google.com)

###### neuron

- Our group develops (i) computational models for cognitive processes like decision making, and recognition and learning of complex spatiotemporal patterns, and (ii) neuronal models at several spatiotemporal scales, e.g. neural mass or single neuron models. (mpg.de)

###### stochastic

- This paper leverages recent advances in stochastic gradient Markov Chain Monte Carlo (also appropriate for large training sets) to learn weight uncertainty in RNNs. (arxiv.org)

###### parameters

- The reservoir has two main parameters that impact the accuracy of the model: the reservoir size (number of neurons in the RNN) and the spectral radius of the hidden-hidden recurrent weight matrix. (springer.com)

###### model

- 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)

###### train

- What made this result so shocking at the time was that the common wisdom was that RNNs were supposed to be difficult to train (with more experience I've in fact reached the opposite conclusion). (pearltrees.com)

###### time

- Fast forward about a year: I'm training RNNs all the time and I've witnessed their power and robustness many times, and yet their magical outputs still find ways of amusing me. (pearltrees.com)

###### standard

- Now, one thing you could do is try to use a standard neural network for this task. (coursera.org)
- In many ways, this is the "standard" layer of a Neural Network. (wildml.com)

###### support

- Keras-MXNet currently has experimental support for RNNs. (amazon.com)

###### network architecture

- A Low Latency Digital Neural Network Architecture (W. Fornaciari, F. Salice). (barnesandnoble.com)

###### fully-connected

- A fully-connected layer in a Neural Network. (wildml.com)

###### activation

- What is the role of the activation function in a neural network? (codingvideos.net)