1014578
Learning Curves for Stochastic Gradient Descent in Linear Feedforward Networks
Gradient-following learning methods can encounter problems of implementation in many applications, and stochastic variants are sometimes used to overcome these difficulties. We analyze three online training methods used with a linear perceptron: direct gradient descent, node perturbation, and weight perturbation. Learning speed is defined as the rate of exponential decay in the learning curves. When the scalar parameter that controls the size of weight updates is chosen to maximize learning speed, node perturbation is slower than direct gradient descent by a factor equal to the number of output units; weight perturbation is slower still by an additional factor equal to the number of input units. Parallel perturbation allows faster learning than sequential perturbation, by a factor that does not depend on network size. We also characterize how uncertainty in quantities used in the stochastic updates affects the learning curves. This study suggests that in practice, weight perturbation may be slow for large networks, and node perturbation can have performance comparable to that of direct gradient descent when there are few output units. However, these statements depend on the specifics of the learning problem, such as the input distribution and the target function, and are not universally applicable.
9393077
Spike-based Learning Rules and Stabilization of Persistent Neural Activity
We analyze the conditions under which synaptic learning rules based on action potential timing can be approximated by learning rules based on firing rates. In particular, we consider a form of plasticity in which synapses depress when a presynaptic spike is followed by a postsynaptic spike, and potentiate with the opposite temporal ordering. Such differential anti-Hebbian plasticity can be approximated under certain conditions by a learning rule that depends on the time derivative of the postsynaptic firing rate. Such a learning rule acts to stabilize persistent neural activity patterns in recurrent neural networks.
307226
Selectively Grouping Neurons in Recurrent Networks of Lateral Inhibition
Winner-take-all networks have been proposed to underlie many of the brain's fundamental computational abilities. However, notmuchisknown about how to extend the grouping of potential winners in these networks beyond single neuron or uniformly arranged groups of neurons. We show that competition between arbitrary groups of neurons can be realized by organizing lateral inhibition in linear threshold networks. Given a collection of potentially overlapping groups (with the exception of some degenerate cases), the lateral inhibition results in network dynamics such that any permitted set of neurons that can be coactivated by some input at a stable steady state is contained in one of the groups. The information about the input is preserved in this operation. The activity level of a neuron in a permitted set corresponds to its stimulus strength, amplified by some constant. Sets of neurons that are not part of a group cannot be coactivated by any input at a stable steady state. We analyze the storage capacity of such a network for random groupsthe number of random groups the network can store as permitted sets without creating too many spurious ones. In this framework, we calculate the optimal sparsity of the groups (maximizing group entropy). We find that for dense inputs, the optimal sparsity is unphysiologically small. However, when the inputs and the groups are equally sparse, we derive a more plausible optimal sparsity. We believe our results are the first steps toward attractor theories in hybrid analog-digital networks.
11201868
Equivalence of Backpropagation and Contrastive Hebbian Learning in a Layered Network
Backpropagation and contrastive Hebbian learning are two methods of training networks with hidden neurons. Backpropagation computes an error signal for the output neurons and spreads it over the hidden neurons. Contrastive Hebbian learning involves clamping the output neurons at desired values and letting the effect spread through feedback connections over the entire network. To investigate the relationship between these two forms of learning, we consider a special case in which they are identical: a multilayer perceptron with linear output units, to which weak feedback connections have been added. In this case, the change in network state caused by clamping the output neurons turns out to be the same as the error signal spread by backpropagation, except for a scalar prefactor. This suggests that the functionality of backpropagation can be realized alternatively by a Hebbian-type learning algorithm, which is suitable for implementation in biological networks.
7144646
Learning Winner-take-all Competition Between Groups of Neurons in Lateral Inhibitory Networks
It has long been known that lateral inhibition in neural networks can lead to a winner-take-all competition, so that only a single neuron is active at a steady state. Here we show how to organize lateral inhibition so that groups of neurons compete to be active. Given a collection of potentially overlapping groups, the inhibitory connectivity is set by a formula that can be interpreted as arising from a simple learning rule. Our analysis demonstrates that such inhibition generally results in winner-take-all competition between the given groups, with the exception of some degenerate cases. In a broader context, the network serves as a particular illustration of the general distinction between permitted and forbidden sets, which was introduced recently. From this viewpoint, the computational function of our network is to store and retrieve memories as permitted sets of coactive neurons.
2399803
Exact solution of the nonlinear dynamics of recurrent neural mechanisms for direction selectivity
Abstract Different theoretical models have tried to investigate the feasibility of recurrent neural mechanisms for achieving direction selectivity in the visual cortex. The mathematical analysis of such models has been restricted so far to the case of purely linear networks. We present an exact analytical solution of the nonlinear dynamics of a class of direction selective recurrent neural models with threshold nonlinearity. Our mathematical analysis shows that such networks have form-stable stimulus-locked traveling pulse solutions that are appropriate for modeling the responses of direction selective cortical neurons. Our analysis shows also that the stability of such solutions can break down giving raise to a different class of solutions (“lurching activity waves”) that are characterized by a specific spatio-temporal periodicity. These solutions cannot arise in models for direction selectivity with purely linear spatio-temporal filtering.
10678156
Detecting The Sufficient Display Resolution For Image Browsing
In image browsing, the resolution greatly affects user’s experience. If an image is down-scaled too much, a considerable amount of information within it will be lost. In this paper, we studied the problem of "What is a sufficient display resolution or scale for an image or an image region?" This problem arises in many real-life applications including image browsing on mobile devices, image adaptation and progressive image delivery. Kullback-Leibler (K-L) distance is employed to measure the information loss and the sufficient display scale is selected based on the information loss curve during image down-sampling. Since the images are presented to viewers finally, some visual characteristics are also taken into account to ensure the precision of the measurement. A user study was carried out to evaluate the performance of our approach. Experimental results show that the approach is in good accord with human perception.
14585963
Effective browsing of web image search results
The rapid development of web image search engines has enabled users to search hundred million of images available on the Web. However, due to the unsatisfactory performance of current search technologies, people still need to spend much time in navigating through the large number of result pages to find images of their interest. In this paper, we analyze the user information needs for web image search results browsing and propose to employ a similarity-based organization to present the search results. A user study is carried out to compare our approach with a ranking-based list interface and a cluster-based interface. Experimental results show that similarity-based presentation can help users to explore image search results more naturally and efficiently
10740927
Photo-to-search: using multimodal queries to search the web from mobile devices
Nowadays, mobile phones with the digital camera are getting more and more popular. With necessary technologies, they are possible to become a powerful tool to search the Web on the go. Most Web search engines only support text queries. Therefore, users have to convert their information needs into words. However, it is sometimes difficult to describe the needs in text and the text input is inconvenient on small devices. To solve the problem, we propose a system named Photo-to-Search which allows users to input multimodal queries. Particularly, we study queries with captured images and optional text messages in this paper. For example, the user can simply take a photo of the flower and input a few terms like "flower". Textually relevant Web images are retrieved according to the query terms. Afterwards, the snapped picture is compared with these images by the CBIR (Content Based Image Retrieval) method. According to the context of the visually similar images, related key phrases are extracted. Finally, the search results are returned in multiple forms. Our system can also search for very similar images on the Web, such as movie posters or photos of film stars, to find related information. Experimental results on the large scale data showed our system achieved satisfactory efficiency and performance.
27158603
A simulation-based optimization method for production-distribution network design
Production-distribution network design is a critical decision that has significant impacts on a supply chain's long-term performance. Supply chain dynamics, such as demand fluctuation, and transportation instability, are omitted in most mathematical models due to tractability. We present a simulation-based optimization method in this paper for multi-criteria production-distribution network design. The method consists of a multiobjective optimizer and a simulation module. The optimizer, based on a multiobjective genetic algorithm, is used to direct the search for compromised solutions regarding to various criteria. Candidate solutions are evaluated by the discrete-event simulation module, developed in a flexible manner that enables automatic simulation of various supply chain structures without human intervention. The method is applied to a case study from automotive industry. A set of Pareto-optimal solutions are obtained, including decisions on the open/close decisions on facilities, order splitting ratios and inventory control policies.
15110783
Detecting dominant locations from search queries
Accurately and effectively detecting the locations where search queries are truly about has huge potential impact on increasing search relevance. In this paper, we define a search query's dominant location (QDL) and propose a solution to correctly detect it. QDL is geographical location(s) associated with a query in collective human knowledge, i.e., one or few prominent locations agreed by majority of people who know the answer to the query. QDL is a subjective and collective attribute of search queries and we are able to detect QDLs from both queries containing geographical location names and queries not containing them. The key challenges to QDL detection include false positive suppression (not all contained location names in queries mean geographical locations), and detecting implied locations by the context of the query. In our solution, a query is recursively broken into atomic tokens according to its most popular web usage for reducing false positives. If we do not find a dominant location in this step, we mine the top search results and/or query logs (with different approaches discussed in this paper) to discover implicit query locations. Our large-scale experiments on recent MSN Search queries show that our query location detection solution has consistent high accuracy for all query frequency ranges.