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Heterogeneous Bitwidth Binarization in Convolutional Neural Networks
Joshua Fromm, Shwetak Patel, Matthai Philipose
Neural Information Processing Systems http://nips.cc/
Variance Reduced Policy Evaluation with Smooth Function Approximation
Hoi-To Wai, Mingyi Hong, Zhuoran Yang, Zhaoran Wang, Kexin Tang
Neural Information Processing Systems http://nips.cc/
Is Deeper Better only when Shallow is Good?
Eran Malach, Shai Shalev-Shwartz
While current works account for the importance ofdepth for the expressivepower ofneural-networks, itremains an open question whether these benefits are exploited during a gradient-based optimization process.
TemporalEffectiveBatchNormalizationinSpiking NeuralNetworks
Furthermore, the temporal dynamics of spiking neuron is tightly in connection with the neuronal parameters.
Simple and Fast Distillation of Diffusion Models Zhenyu Zhou 1,2 Defang Chen 3 Can Wang 1,2 Chun Chen
To achieve both efficient and high-quality synthesis, various distillation-based accelerated sampling methods have been developed recently.
Learning a Warping Distance from Unlabeled Time Series Using Sequence Autoencoders
Neural Information Processing Systems http://nips.cc/
GloballyConvergentNewtonMethodsfor Ill-conditionedGeneralizedSelf-concordantLosses
Second, in the non-parametric machine learning setting, we provide an explicit algorithm combining the previous scheme with Nyström projection techniques, andprovethatitachievesoptimal generalization bounds with atime complexity of orderO(ndfλ), a memory complexity of orderO(df2λ) and no dependence on the condition number, generalizing the results known for leastsquaresregression.Here nisthenumberofobservationsand dfλ istheassociated degrees of freedom.