Multiple Object Tracking

On June 23rd, we discussed “Explaining human multiple object tracking as resource-constrained approximate inference in a dynamic probabilistic model” by Edward Vul, Michael C Frank, George Alvarez, and Joshua B Tenenbaum.

    This paper proposes a dynamic probabilistic model as an ideal observer for multiple object tracking.  The ideal observer model uses a set of n Kalman filters to track n objects.  Observations at each time step are composed of n pairs of position and velocity values.  However, inference must be performed in order to associate each observation pair with the appropriate object identity (Kalman filter).

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Context-dependent computation by recurrent dynamics in prefrontal cortex

The past two lab meetings have focused on understanding the regression subspace analysis performed in Mante et al. 2013.

Context-Dependent Computation by Recurrent Dynamics in Prefrontal Cortex
Mante, Sussillo, Shenoy & Newsome; Nature 2013

The paper explores the hypothesis that context-dependent behavior is the result of early selection. However, they find no evidence for early selection mechanisms. Instead, they demonstrate that populations of neurons in the PFC can be projected into a neural state space in which the features of the task are separable regardless of context.

As we study larger populations of neurons in increasingly complex tasks, we must address the problem of how to meaningfully visualize and interrogate our data. This is a difficult challenge and Mante et al. 2013 provides a compelling approach.

Lab Meeting 6/25/2013: Restricted Boltzmann Machines and Restricted Boltzmann Forests

Lab meeting focused on the tractability of using Restricted Boltzmann Machines (RBMs) in modelling and an extension of the RBM known as the Restricted Boltzmann Forest (RBF) as presented in Larochelle, Bengio and Turian (2010). This post will review the tractibility of RBMs and briefly discuss the main contribution of RBFs.

Restricted Boltzmann Machine (RBM)

An RBM (originally Harmonium, Smolensky 1986) is a generative neural network with two groups of neurons (hidden h; and visible x) that form a bipartite graph.

We can begin by noting the disadvantages in computation complexity of using an RBM (and other factor models).  The probability distribution over the RBM is calculated as follows:

eq1

In general, q(x) is relatively efficient to compute. However, the computational complexity/cost of calculating  Z limits the power of this model. In the case of the RBM Z is:

eq2

In this form Z must be computed for all values of x and thus is extremely prohibitive, however Z can be factorized:

eq3

In this form, Z is independent of x and thus only needs to be calculated once, reducing the overall cost of calculating p(x). This allows the RBM to grow much larger in its visible layer without an exponential penalty in computational cost. However there are limits on the size of the RBM in terms of the number of hidden units it may have.

Restricted Boltzmann Forests (RBFs)

Restricted Boltzmann Forests were designed to alleviate some of the computational limitations on hidden units encountered with RBMs. RBFs still have the same general structure, but RBFs place additional restrictions on the hidden units. The restrictions require that the hidden units be organized into n trees of depth d, such that if a hidden unit is active, its right
subtree must be inactive and its left may be active. Similarly if a hidden unit is inactive, its left subtree must be inactive and its right subtree may be active. These restrictions result in fewer possible configurations of the hidden units. Thus, the cost of calculating Z is reduced. Consider an RBM with 20 hidden units. Z would require summation over 2^20 possible configurations (settings of 0 or 1) of the hidden units. An RBF with 5 trees of depth 3 would have 75 hidden units and the same computational cost (number of possible configurations) as an RBM with 20 hidden units.

I have left out many of the details regarding RBFs, including the procedures for performing learning and calculating p(x), all of which can be found in Larochelle, Bengio & Turian (2010).

References

  • Hinton, Geoffrey. “A practical guide to training restricted Boltzmann machines.” Momentum 9.1 (2010).
  • Larochelle, Hugo, Yoshua Bengio, and Joseph Turian. “Tractable multivariate binary density estimation and the restricted Boltzmann forest.” Neural computation 22.9 (2010): 2285-2307.
  • Smolensky, Paul. “Information processing in dynamical systems: Foundations of harmony theory.” (1986): 194.