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We present an accelerated gradient method for non-convex optimization problems with Lip-schitz continuous first and second derivatives. The method requires time O(−7/4 log(1//)) to find an-stationary point, meaning a point x such that ∇f (x) ≤. The method improves upon the O(−2) complexity of gradient descent and provides the additional second-order(More)
We use smoothed analysis techniques to provide guarantees on the training loss of Multilayer Neural Networks (MNNs) at differentiable local minima. Specifically, we examine MNNs with piecewise linear activation functions, quadratic loss and a single output, under mild over-parametrization. We prove that for a MNN with one hidden layer, the training error is(More)
  • M Shani, U Nudel, +7 authors D Yaffe
  • 1981
Plasmids p749, p106, and p150 contain cDNA inserts complementary to rat skeletal muscle actin mRNA. Nucleotide sequence analysis indicates the following sequence relationships: p749 specifies codons 171 to 360; p150 specifies codons 357 to 374 together with 120 nucleotides of the 3'-non-translated region; p106 specifies the last actin amino acid codon, the(More)
Partial matching of geometric structures is important in computer vision , pattern recognition and shape analysis applications. The problem consists of matching similar parts of shapes that may be dissimilar as a whole. Recently, it was proposed to consider partial similarity as a multi-criterion optimization problem trying to simultaneously maximize the(More)
We compare the maximum achievable rates in single-carrier (SC) and orthogonal frequency-division multiplexing (OFDM) modulation schemes, under the practical assumptions of independent identically distributed finite alphabet inputs and linear intersymbol interference with additive Gaussian noise. We show that the Shamai-Laroia approximation serves as a(More)
We generalize the geometric discount of finite discounted cost Markov Decision Processes to " exponentially representable " discount functions, prove existence of optimal policies which are stationary from some time N onward, and provide an algorithm for their computation. Outside this class, optimal " N-stationary " policies in general do not exist.
We investigate the existance of simple policies in finite discounted cost Markov Decision Processes, when the discount factor is not constant. We introduce a class called " exponentially representable " discount functions. Within this class we prove existence of optimal policies which are eventually stationary—from some time N onward, and provide an(More)
We consider mean squared estimation with lookahead of a continuous-time signal corrupted by additive white Gaussian noise. We show that the mutual information rate function, i.e., the mutual information rate as function of the signal-to-noise ratio (SNR), does not, in general, determine the minimum mean squared error (MMSE) with fixed finite lookahead, in(More)
We consider the discrete-time intersymbol interference (ISI) channel model, with additive Gaussian noise and fixed independent identically distributed inputs. In this setting, we investigate the expression put forth by Shamai and Laroia as a conjectured lower bound for the input-output mutual information after application of a minimum mean-square error(More)
We consider mean squared estimation with lookahead of a continuous-time signal corrupted by additive white Gaussian noise. We investigate the connections between lookahead in estimation, and information under this model. We show that the mutual information rate function, i.e., the mutual information rate as function of the signal-to-noise ratio (SNR) does(More)