Edo Collins

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Deep learning presents notorious computational challenges. These challenges include, but are not limited to, the non-convexity of learning objectives and estimating the quantities needed for optimization algorithms, such as gradients. While we do not address the non-convexity, we present an optimization solution that exploits the so far unused “geometry” in(More)
Interest in deep probabilistic graphical models has increased in recent years, due to their state-of-the-art performance on many machine learning applications. Such models are typically trained with the stochastic gradient method, which can take a significant number of iterations to converge. Since the computational cost of gradient estimation is(More)
Table 1: Parameter Settings for Learning RBMs. RMSprop parameters chosen to match [5]. SGD parameters chosen to match [26]. SSD and A-SSD stepsizes and geometries chosen to match [1]. The stepsize on W is given for the RBM. λ corresponds to the damping factor in the history terms in the ADA and RMS methods. Projections refers to the numbers of projections(More)
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