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