Predictive Encoding of Contextual Relationships for Perceptual Inference, Interpolation and Prediction

We propose a new neurally-inspired model that can learn to encode the global relationship context of visual events across time and space and to use the contextual information to modulate the analysis by synthesis process in a predictive coding framework. The model learns latent contextual representations by maximizing the predictability of visual events based on local and global contextual information through both top-down and bottom-up processes. In contrast to standard predictive coding models, the prediction error in this model is used to update the contextual representation but does not alter the feedforward input for the next layer, and is thus more consistent with neurophysiological observations. We establish the computational feasibility of this model by demonstrating its ability in several aspects. We show that our model can outperform state-of-art performances of gated Boltzmann machines (GBM) in estimation of contextual information. Our model can also interpolate missing events or predict future events in image sequences while simultaneously estimating contextual information. We show it achieves state-of-art performances in terms of prediction accuracy in a variety of tasks and possesses the ability to interpolate missing frames, a function that is lacking in GBM

A Neural Model for Self Organizing Feature Detectors and Classifiers in a Network Hierarchy

Many models of early cortical processing have shown how local learning rules can produce efficient, sparse-distributed codes in which nodes have responses that are statistically independent and low probability. However, it is not known how to develop a useful hierarchical representation, containing sparse-distributed codes at each level of the hierarchy, that incorporates predictive feedback from the environment. We take a step in that direction by proposing a biologically plausible neural network model that develops receptive fields, and learns to make class predictions, with or without the help of environmental feedback. The model is a new type of predictive adaptive resonance theory network called Receptive Field ARTMAP, or RAM. RAM self organizes internal category nodes that are tuned to activity distributions in topographic input maps. Each receptive field is composed of multiple weight fields that are adapted via local, on-line learning, to form smooth receptive ftelds that reflect; the statistics of the activity distributions in the input maps. When RAM generates incorrect predictions, its vigilance is raised, amplifying subtractive inhibition and sharpening receptive fields until the error is corrected. Evaluation on several classification benchmarks shows that RAM outperforms a related (but neurally implausible) model called Gaussian ARTMAP, as well as several standard neural network and statistical classifters. A topographic version of RAM is proposed, which is capable of self organizing hierarchical representations. Topographic RAM is a model for receptive field development at any level of the cortical hierarchy, and provides explanations for a variety of perceptual learning data.Defense Advanced Research Projects Agency and Office of Naval Research (N00014-95-1-0409

Towards an Intelligent Framework for Pressure-based 3D Curve Drawing

Pen pressure is an input channel typically available in tablet pen device. To date, little attention has been paid to the use of pressure in the domain of graphical interaction, its usage largely limited to drawing and painting program, typically for varying brush characteristic such as stroke width, opacity and color. In this paper, we explore the use of pressure in 3D curve drawing. The act of controlling pressure using pen, pencil and brush in real life appears effortless, but to mimic this natural ability to control pressure using a pressure sensitive pen in the realm of electronic medium is difficult. Previous pressure based interaction work have proposed various signal processing techniques to improve the accuracy in pressure control, but a one-for-all signal processing solution tend not to work for different curve types. We propose instead a framework which applies signal processing techniques tuned to individual curve type. A neural network classifier is used as a curve classifier. Based on the classification, a custom combination of signal processing techniques is then applied. Results obtained point to the feasibility and advantage of the approach.Comment: This paper was rejected from GI 2014. Comment from the chief reviewer:All reviewers noted that the ideas behind this paper were promising, but felt that research was not quite sufficiently developed...Although all agreed that this idea is insightful and has the potential to lead to a valuable contribution,... the idea is not yet sufficiently developed to warrant publicatio

Deep Belief Networks for Recognizing Handwriting Captured by Leap Motion Controller

Leap Motion controller is an input device that can track hands and fingers position quickly and precisely. In some gaming environment, a need may arise to capture letters written in the air by Leap Motion, which cannot be directly done right now. In this paper, we propose an approach to capture and recognize which letter has been drawn by the user with Leap Motion. This approach is based on Deep Belief Networks (DBN) with Resilient Backpropagation (Rprop) fine-tuning. To assess the performance of our proposed approach, we conduct experiments involving 30,000 samples of handwritten capital letters, 8,000 of which are to be recognized. Our experiments indicate that DBN with Rprop achieves an accuracy of 99.71%, which is better than DBN with Backpropagation or Multi-Layer Perceptron (MLP), either with Backpropagation or with Rprop. Our experiments also show that Rprop makes the process of fine-tuning significantly faster and results in a much more accurate recognition compared to ordinary Backpropagation. The time needed to recognize a letter is in the order of 5,000 microseconds, which is excellent even for online gaming experience

Navigation as a Source of Geometric Knowledge: Young Children’s Use of Length, Angle, Distance, and Direction in a Reorientation Task

Geometry is one of the highest achievements of our species, but its foundations are obscure. Consistent with longstanding suggestions that geometrical knowledge is rooted in processes guiding navigation, the present study examines potential sources of geometrical knowledge in the navigation processes by which young children establish their sense of orientation. Past research reveals that children reorient both by the shape of the surface layout and the shapes of distinctive landmarks, but it fails to clarify what shape properties children use. The present study explores 2-year-old children’s sensitivity to angle, length, distance and direction by testing disoriented children’s search in a variety of fragmented rhombic and rectangular environments. Children reoriented themselves in accord with surface distances and directions, but they failed to use surface lengths or corner angles either for directional reorientation or as local landmarks. Thus, navigating children navigate by some but not all of the abstract properties captured by formal Euclidean geometry. While navigation systems may contribute to children’s developing geometric understanding, they likely are not the sole source of abstract geometric intuitions.Psycholog

A high performance neural network javascript library

Master's Project (M.S.) University of Alaska Fairbanks, 2015This report covers Intellect.js, a new high-performance Artificial Neural Network (ANN) library written in JavaScript and intended for use within a web browser. The library is designed to be easy to use, whilst remaining highly customizable and flexible. A brief history of JavaScript and ANNs is presented, along with decisions made while developing Intellectjs. Lastly, performance benchmarks are provided, including comparisons with existing ANN libraries written in JavaScript. Appendices include a code listing, usage examples, and complete performance data. Intellect.js is available on GitHub under the MIT License. https://github.com/sutekidayo/intellect.j

Deep Representation Learning and Prediction for Forest Wildfires

An average of 8000 forest wildfires occurs each year in Canada burning an average of 2.5M ha/year as reported by the Government of Canada. Given the current rate of climate change, this number is expected to increase each year. Being able to predict how the fires spread would play a critical role in fire risk management. However, given the complexity of the natural processes that influence a fire system, most of the models used for simulating wildfires are computationally expensive and need a high variety of information about the environmental parameters to be able to give good performances. Deep learning algorithms allow computers to learn from experience and understand the world in terms of a hierarchy of concepts, with each concept defined in terms of its relation to simpler concepts. We propose a deep learning predictor that uses a Deep Convolutional Auto-Encoder to learn the key structures of a forest wildfire spread from images and a Long Short Term Memory to predict the next phase of the fire. We divided the predictor problem in three phases: find a dataset of wildfires, learning the essential structure of forest fire, and predict the next image. We first present the simulated wildfires dataset and the algorithm we applied on it to make it more suitable to the model. Then we present the Deep Forest Wildfire Auto-Encoder and its implementation using the Caffe framework. Particular attention is given to the design considerations and to the best practice used to implement the model. We also present the design of the Deep Forest Wildfire Predictor, and some possible future variations of it