###### networks

- W.-H. Chen, X. Lu and D.-Y. Liang, Global exponential stability for discrete-time neural networks with variable delays,, Physics Letters A , 358 (2006), 186. (aimsciences.org)
- Firstly, we provide an overview of ML techniques including artificial neural networks (ANNs), support vector machines (SVMs), decision trees (DTs), and k -nearest neighbors ( k -NNs). (mdpi.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)
- Neural networks present a fundamentally different model of computation from the conventional sequential digital model, for which conventional hardware is typically poorly matched. (springer.com)
- We'll read An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling by @shaojieb , @zicokolter , and Koltun. (toronto.edu)
- Research papers, preprints, introductory textbook on neural networks and robotics. (transit-port.net)
- Research and publications on evolution of dynamical nervous systems (continuous-time recurrent neural networks) for autonomous agents, dynamics of adaptive behaviour, biologically-inspired robotics and other topics. (transit-port.net)
- A new model of a dynamic system which includes continuous time delays has been introduced, and its utility demonstrated in the evolution of networks for the solution of simple learning behaviours. (bl.uk)
- The application of these tools to a range of examples has been explored, from Gene Regulatory Networks (GRNs) to robot control and neural networks. (bl.uk)
- It has been shown that delayed dynamic neural systems are at least as capable as traditional Continuous Time Recurrent Neural Networks (CTRNNs) and show significant performance improvements in the control of robot gaits. (bl.uk)
- Two classes of dynamical recurrent neural networks, Continuous Time Recurrent Neural Networks (CTRNNs) (Yamauchi and Beer, 1994) and Plastic Neural Networks (PNNs) (Floreano and Urzelai, 2000) are compared on two behavioral tasks aimed at exploring their capabilities to display reinforcement-learning like behaviors and adaptation to unpredictable environmental changes. (epfl.ch)
- This paper explores the capabilities of continuous time recurrent neural networks (CTRNNs) to display reinforcement learning-like abilities on a set of T-Maze and double T-Maze navigation tasks, where the robot has to locate and "remember'' the position of a reward-zone. (epfl.ch)
- 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)
- Shrinkage Degree in L2-Rescale Boosting for Regression[J]. IEEE Transactions on Neural Networks & Learning Systems, 2017, 28(8):1851-1864. (xjtu.edu.cn)
- 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)
- The present invention relates, in general, to neural networks, and more particularly to adaptation of pretrained neural networks. (google.com)
- This paper is concerned with the robust dissipativity problem for interval recurrent neural networks (IRNNs) with general activation functions, and continuous time-varying delay, and infinity distributed time delay. (hindawi.com)
- Neural networks have been a subject of intense research activities over the past few decades due to their wide applications in many areas such as signal processing, pattern recognition, associative memories, parallel computation, and optimization solution. (hindawi.com)
- Therefore, increasing attention has been paid to the problem of stability analysis of neural networks with time-varying delays, and recently a lot of research works have been reported for delayed neural networks and system (see [ 1 - 17 ] and references therein). (hindawi.com)
- It is also possible that there is no equilibrium point in some situations, especially for interval recurrent neural networks with infinity distributed delays. (hindawi.com)
- But what we can know is that the orbits of the neural networks will always enter into a bounded region and stay there from then on. (hindawi.com)
- Generally speaking, the goal of study on globally dissipative for neural networks is to determine globally attractive sets. (hindawi.com)
- Therefore, many initial findings on the global dissipativity [ 18 , 22 - 30 ] or Lagrange stability [ 31 - 36 ] analysis of neural networks have been reported. (hindawi.com)
- However, neural networks usually have a spatial extent due to the presence of a multitude of parallel pathways with a variety of axon sizes and lengths. (hindawi.com)
- A novel reconstruction technique based in Artificial Neural Networks (ANN) is proposed. (mdpi.com)

###### 2017

- Multimodal 2D+3D Facial Expression Recognition with Deep Fusion Convolutional Neural Network[J]. IEEE Transactions on Multimedia, 2017, PP(99):1-1. (xjtu.edu.cn)
- Gong T, Xu Z, Chen H. Generalization Analysis of Fredholm Kernel Regularized Classifiers[J]. Neural Computation, 2017, 29(7):1879-1901. (xjtu.edu.cn)
- A Preference-Based Multiobjective Evolutionary Approach for Sparse Optimization[J]. IEEE Trans Neural Netw Learn Syst, 2017, PP(99):1-16. (xjtu.edu.cn)

###### Hopfield

- 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)

###### 2016

- 5. 5 (Slides by Marc BolaĆ±os) 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)

###### Convolutional

- Recurrent Convolutional Neural Network Regression for Continuous Pain Intensity Estimation in Video , by Zhou et al. (toronto.edu)

###### neurons

- The results from this study suggest that continuous synaptic plasticity in both excitatory neurons and interneurons could play a critical role in the formation, stability, and maintenance of functional maps in the cortex. (frontiersin.org)
- The spatial and temporal properties of a distributed pattern of neural activity in V1 with differentially tuned responses of individual neurons to features of visual space such as orientation, spatial frequency, and direction of motion were first recognized by Hubel and Wiesel (1962 , 1963 , 1968 , 2005) . (frontiersin.org)
- Using microelectrodes and neuroanatomical tracers, they established that the neural activity of a population of neurons with such differentiation represented functional maps . (frontiersin.org)
- 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)

###### artificial

- Recurrent quantum neural network (RQNN) is an artificial neural network model which can perform stochastic filtering without any prior knowledge of the signal and noise. (springer.com)
- For example, the ABCpred [ 10 ] was developed using artificial neural network method. (biomedcentral.com)

###### adaptive

- A method for adapting a decision directed adaptive neural network (10). (google.com)
- The prior art speech recognition systems include: hidden Markov models, pure neural network architectures, hybrid systems, and adaptive hybrid systems. (google.com)

###### stability

- The stability and maintenance of the formed maps with continuous synaptic plasticity is enabled by homeostasis caused by inhibitory plasticity. (frontiersin.org)

###### Frontiers

- Extending work published in ACM Computing Frontiers 2010 with on-chip testing, simulation results indicate the viability of SpiNNaker for large-scale neural modelling, while emphasizing the need for effective burst management and network mapping. (springer.com)

###### Recognition

- A neural network is essentially a pattern recognition apparatus. (google.com)

###### Controllers

- Neural controllers are evolved in simulation and in the simple case evaluated on a real robot. (epfl.ch)

###### biologically-inspired

- This Thesis explores the introduction of continuous time delays into biologically inspired dynamic control systems. (bl.uk)

###### Simulation

- This parallel multiprocessor employs an asynchronous event-driven model that uses interrupt-generating dedicated hardware on the chip to support real-time neural simulation. (springer.com)

###### discontinuous

- Two types of B-cell epitopes have been defined: linear (continuous) and conformational (discontinuous). (biomedcentral.com)
- Based on structural characteristics, B-cell epitopes can be categorized into two types: linear (continuous) epitopes and conformational (discontinuous) epitopes. (biomedcentral.com)

###### prediction

- Its input sequences ranged from 10 to 20 amino acids on the experimental design, and the best performance was achieved 65.93% prediction accuracy when ABCpred model was trained using recurrent neural network with a peptide dataset of 16 amino acids in length (ABC16). (biomedcentral.com)

###### models

- Having established the viability of the system for real-time operation, we use two exemplar neural models to illustrate how to implement efficient event-handling service routines that mitigate the problem of burstiness in the traffic. (springer.com)
- See Sequential Neural Models with Stochastic Layers . (danmackinlay.name)

###### Evaluation

- Scope of the study is to use retrospective data for training and evaluation of a deep recurrent neural network based system for predicting the onset of hypoglycemic event at least 20 min ahead in time. (clinicaltrials.gov)

###### system

- Ultimately, the goal is the creation of a library-based development system that can translate a high-level neural model from any description environment into an efficient SpiNNaker instantiation. (springer.com)
- The complete system represents a general-purpose platform that can generate an arbitrary neural network and run it with hardware speed and scale. (springer.com)
- 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)
- Software for controlling processes in a heterogeneous semiconductor manufacturing environment may include a wafer-centric database, a real-time scheduler using a neural network, and a graphical user interface displaying simulated operation of the system. (google.com)

###### time

- Cauwenberghs G.: An analog VLSI recurrent neural network learning a continuous-time trajectory. (springer.com)

###### Analysis

- Neural correlates of visuospatial bias in patients with left hemisphere stroke: a causal functional contribution analysis based on game theory, Neuropsychologia , epub ahead of print. (crossmodal-learning.org)

###### approach

- SpiNNaker introduces a different approach, the "neuromimetic" architecture, that maintains the neural optimisation of dedicated chips while offering FPGA-like universal configurability. (springer.com)
- However, from a practical point of view, it is not always the case that the neural network trajectories will approach a single equilibrium point that is the equilibrium point will be unstable. (hindawi.com)

###### decision tree

- We'll be reading Distilling a Neural Network Into a Soft Decision Tree . (toronto.edu)

###### Experimental

- Experimental demonstration of long-distance continuous-variable quantum key distribution. (springer.com)

###### Data

- To analyse this phenomenon, the investigators use continuous glucose monitoring (CGM) and flash glucose monitoring (FGM) of diabetic patients and compare CGM-/FGM data of the last seven days prior to the consultation with the CGM-/FGM data of days 8-28 prior to the consultation. (clinicaltrials.gov)

###### memory

- Previous studies have shown that there are many common structures between the neural network of pain and memory, and the main structure in the pain network is also part of the memory network. (readbyqxmd.com)
- 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)

###### Keywords

- 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)

###### classical

- Field test of classical symmetric encryption with continuous variable quantum key distribution. (springer.com)

###### variable

- Becir, A., El-Orany, F.A.A., Wahiddin, M.R.B.: Continuous-variable quantum key distribution protocols with eight-state discrete modulation. (springer.com)

###### different

- Syllable duration was modelled using a three-layer neural network that was trained and tested on different portions of the database. (assta.org)