83 skills found · Page 1 of 3
simpeg / SimpegSimulation and Parameter Estimation in Geophysics - A python package for simulation and gradient based parameter estimation in the context of geophysical applications.
FranxYao / Deep Generative Models For Natural Language ProcessingDGMs for NLP. A roadmap.
shelfwise / ReceptivefieldGradient based receptive field estimation for Convolutional Neural Networks
duvenaud / RelaxOptimizing control variates for black-box gradient estimation
alessandroferrari / BING ObjectnessPython implementation of BING Objectness method from "BING: Binarized Normed Gradients for Objectness Estimation at 300fps".
airoldilab / SgdAn R package for large scale estimation with stochastic gradient descent
ben-hayes / Sinusoidal Gradient DescentExperiments from the paper "Sinusoidal Frequency Estimation by Gradient Descent"
juntang-zhuang / Torch ACArepo for paper: Adaptive Checkpoint Adjoint (ACA) method for gradient estimation in neural ODE
jhornauer / GrUMoDepthGradient-based Uncertainty for Monocular Depth Estimation (ECCV 2022)
gradientpm / GvpmGradient-domain Volumetric Photon Density Estimation, SIGGRAPH 2018
davidmarttila / Vocal Tract GradVocal Tract Area Estimation by Gradient Descent
uclaml / PadamPartially Adaptive Momentum Estimation method in the paper "Closing the Generalization Gap of Adaptive Gradient Methods in Training Deep Neural Networks" (accepted by IJCAI 2020)
thjashin / Spectral Stein GradCode for "A Spectral Approach to Gradient Estimation for Implicit Distributions" (ICML'18)
Titan-Tong / ScaledGDScaled Gradient Descent for Low-rank Matrix and Tensor Estimation
jjbrophy47 / IbugInstance-based uncertainty estimation for gradient-boosted regression trees
sanjaykariyappa / MAZEImplementation of the paper "MAZE: Data-Free Model Stealing Attack Using Zeroth-Order Gradient Estimation".
lucadellalib / Bdl Rul SvgdBayesian deep learning for remaining useful life estimation via Stein variational gradient descent
jjbrophy47 / Tree InfluenceInfluence Estimation for Gradient-Boosted Decision Trees
metinaktas / Acoustic Direction Finding Using Single Acoustic Vector Sensor Under High ReverberationWe propose a novel and robust method for acoustic direction finding, which is solely based on acoustic pressure and pressure gradient measurements from single Acoustic Vector Sensor (AVS). We do not make any stochastic and sparseness assumptions regarding the signal source and the environmental characteristics. Hence, our method can be applied to a wide range of wideband acoustic signals including the speech and noise-like signals in various environments. Our method identifies the “clean” time frequency bins that are not distorted by multipath signals and noise, and estimates the 2D-DOA angles at only those identified bins. Moreover, the identification of the clean bins and the corresponding DOA estimation are performed jointly in one framework in a computationally highly efficient manner. We mathematically and experimentally show that the false detection rate of the proposed method is zero, i.e., none of the time-frequency bins with multiple sources are wrongly labeled as single-source, when the source directions do not coincide. Therefore, our method is significantly more reliable and robust compared to the competing state-of-the-art methods that perform the time-frequency bin selection and the DOA estimation separately. The proposed method, for performed simulations, estimates the source direction with high accuracy (less than 1 degree error) even under significantly high reverberation conditions.
yun-liu / BINGBING: Binarized Normed Gradients for Objectness Estimation at 300fps
