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In this paper, an approach to evaluating game states of a collectible card game Hearthstone is described. A deep neural network is employed to predict the probability of winning associated with a given game state. Encoding the game state as an input vector is based on another neural network, an autoencoder with a sparsity-inducing loss. The autoencoder encodes minion information in a sparse-like fashion...
Modeling the activity of an ensemble of neurons can provide critical insights into the workings of the brain. In this work we examine if learning based signal modeling can contribute to a high quality modeling of neuronal signal data. To that end, we employ the sparse coding and dictionary learning schemes for capturing the behavior of neuronal responses into a small number of representative prototypical...
Spike generation and routing is typically the most energy-demanding operation in neuromorphic hardware built using spiking neurons. Spiking neural networks running on neuromorphic hardware, however, often use rate-coding where the neurons spike rate is treated as the information-carrying quantity. Rate-coding is a highly inefficient coding scheme with minimal information content in each spike, which...
Neural machine learning methods, such as deep neural networks (DNN), have achieved remarkable success in a number of complex data processing tasks. These methods have arguably had their strongest impact on tasks such as image and audio processing — data processing domains in which humans have long held clear advantages over conventional algorithms. In contrast to biological neural systems, which are...
The complex mechanical structure and working characteristics of crane determine it is a kind of construction machinery with larger risk factors. In order to ensure the safety and reliability of the crane during operation process, also avoid serious failure which affects the efficiency and progress of the engineering project, this paper uses discrete Hopfield neural network approach to evaluate and...
Stochastic computing has been adopted in various fields to improve the power efficiency of systems. Recent work showed that DNN based on stochastic computing can greatly reduce the power consumption. However, stochastic computing has a limitation of high latency overhead as it computes values only one bit per cycle. This paper proposes a new scheme to improve the latency of DNN implementation based...
Time-based Spiking Neural Network (SNN) has recently received increased attentions in neuromorphic computing system designs due to more bio-plausibility and better energy-efficiency. However, unleashing its potentials in realistic cognitive applications is facing significant challenges such as inefficient information representations and impractical learnings. In this work, we aim for exploring a practical...
A lot of artifiicial neural networks were proposed by scientists over the last time. Each of them can cope with the tasks of limited difficulty level, determined by their properties and capabilities. The aim of this paper is to outline difference of them and to define their positive and negative sites in different tasks of identification and control.
Building applications that are cognizant of temporal and spatial changes in human behaviour under a one-class learning restriction represents a requirement for many user centric systems. We are particularly motivated to demonstrate the utility of algorithms for the self identification of smart phones. A framework is designed to quantify: (i) the dissimilarity in behaviours among any two users, (ii)...
Stereo matching is a popular technique to infer depth from two or more images and wealth of methods have been proposed to deal with this problem. Despite these efforts, finding accurate stereo correspondences is still an open problem. The strengths and weaknesses of existing methods are often complementary and in this paper, motivated by recent trends in this field, we exploit this fact by proposing...
How to estimate a macaque's moving finger position through neuron spikes in its mortor cortex is an issue about neural decoding. For the issue, most of existing methods is a supervised training algorithm and require supervised data to obtain the relationship between the spikes and the finger's moving position. Therefore, the performance of the existing methods depends on the training data. This paper...
The growing demand for smarter high-performance embedded systems leads to the integration of multiple functionalities in on-chip systems with tens (even hundreds) of cores. This trend opens a very challenging question about the optimal resource allocation in those manycore systems. Answering this question is key to meet the performance and energy requirements. This paper deals with a learning technique...
We propose a novel computationally efficient hierarchical dictionary learning (HDL) approach for data-driven unmixing and functional connectivity analysis of functional magnetic resonance imaging (fMRI) data. It is shown that by simultaneously exploiting the sparsity of the spatial brain maps and the incoherence among their evolution in time or task functions, one can achieve better performance while...
Approximate Nearest Neighbor (ANN) search for indexing and retrieval has become very popular with the recent growth of the databases in both size and dimension. In this paper, we propose a novel method for fast approximate distance calculation among the compressed samples. Inspiring from Kohonen's self-organizing maps, we propose a structured hierarchical quantization scheme in order to compress database...
Spike sorting is the problem of identifying and clustering neurons spiking activity from recorded extracellular electro-physiological data. This is important for experimental neuroscience. Existing approaches to solve this problem consist of three steps: spike detection, feature extraction, and clustering. In our method, we use Fisher discriminant based dictionary learning to learn dictionary, whose...
This paper proposes a neural network model and learning algorithm that can be applied to encode words. The model realizes the function of words encoding and decoding which can be applied to text encryption/decryption and word-based compression. The model is based on Deep Belief Networks (DBNs) and it differs from traditional DBNs in that it is asymmetric structured and the output of it is a binary...
A big challenge of reservoir-based Recurrent Neural Networks (RNNs) is the optimization of the connection weights within the network so that the network performance is optimal for the intended task of temporal sequence recognition. One particular RNN called the Self-Organizing Recurrent Network (SORN) avoids the mathematical normalization required after each initialization. Instead, three types of...
This paper presents an efficient DNN design with stochastic computing. Observing that directly adopting stochastic computing to DNN has some challenges including random error fluctuation, range limitation, and overhead in accumulation, we address these problems by removing near-zero weights, applying weight-scaling, and integrating the activation function with the accumulator. The approach allows...
This paper presents algorithm and digital hardware design, inspired by biological spiking neural networks, to perform unsupervised, online spike-clustering with high accuracy and low-power consumption in the context of deep-brain sensing and stimulation systems. The proposed hardware contains 1220 digital neurons and 4.86k latch-based synapses, and achieves the average sorting accuracy of 91% whereas...
This paper investigates Stochastic Local Search (SLS) algorithms for training neural networks with threshold activation functions. and proposes a novel technique, called Binary Learning Machine (BLM). BLM acts by changing individual bits in the binary representation of each weight and picking improving moves.
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