但是如果卡不够,应用又没有时间限制,那么LIME是比gradient-based methods有优势的,因为不用反向传播,GPU只要走前向就可以了。 经过实验,LIME的采样点在1000个左右时在LLAMA-2的解释上效果很好,前提是sequence不能太长。当采样点足够时,LIME的效果比IG更好。注意,如果要把LIME用在language上,那么每个word的所有...
doi:US20070005313 A1Vladimir SevastyanovOleg ShaposhnikovUSUS20070005313 Aug 18, 2006 Jan 4, 2007 Vladimir Sevastyanov Gradient-based methods for multi-objective optimization
iterative methodsleast-squares approximationregularisationReproducing kernel Hilbert spaces (RKHS) provide a unified framework for the solution of a number of function approximation and signal estimation problems. A significant problem with RKHS methods for real applications is the poor scaling properties of...
We study the data deletion problem for convex models. By leveraging techniques from convex optimization and reservoir sampling, we give the first data deletion algorithms that are able to handle an arbitrarily long sequence of adversarial updates while promising both per-deletion run-time and steady-...
The problem of explaining complex machine learning models, including Deep Neural Networks, has gained increasing attention over the last few years. While several methods have been proposed to explain network predictions, the definition itself of explanat
and the final result tends to be affected by initial conditions. In the non-parameterization methods, thresholds is determined by solving an optimization problem with objective functions such as between-class variance (Otsu, 1979), one-dimensional entropy (Kapur et al., 1985, Pun, 1980), two-...
Marzouk. Gradient-based stochastic optimization methods in Bayesian experimental design. International Journal for Uncertainty Quantification, 4(6):479-510, 2014.X. Huan and Y. M. Marzouk, Gradient-Based Stochastic Optimization Methods in Bayesian Experimental Design, Int. J. Uncertain. Quantif., 4...
Theoretical or Mathematical/ autoregressive moving average processes convergence gradient methods identification nonlinear systems recursive estimation search problems stochastic processes/ iterative gradient-based identification algorithm recursive stochastic gradient-based identification algorithm Hammerstein nonlinear ARMA...
Methods marked with (*) are implemented as modified chain-rule, as better explained inTowards better understanding of gradient-based attribution methods for Deep Neural Networks, Anconaet al, ICLR 2018. As such, the result might be slightly different from the original implementation. ...
In the first talk, we provide a tutorial overview of most of the main approaches currently used for carrying out simulation optimization, which includes stochastic approximation, response surface methodology, and sample average approximation, as well as some random search methods. Simple examples will ...