parameter estimation
342 papers with code • 0 benchmarks • 0 datasets
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Libraries
Use these libraries to find parameter estimation models and implementationsMost implemented papers
Variational Autoencoders for Collaborative Filtering
This non-linear probabilistic model enables us to go beyond the limited modeling capacity of linear factor models which still largely dominate collaborative filtering research. We introduce a generative model with multinomial likelihood and use Bayesian inference for parameter estimation.
Convolutional neural network architecture for geometric matching
ignacio-rocco/cnngeometric_pytorch
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CVPR 2017
We address the problem of determining correspondences between two images in agreement with a geometric model such as an affine or thin-plate spline transformation, and estimating its parameters.
Stable Architectures for Deep Neural Networks
While our new architectures restrict the solution space, several numerical experiments show their competitiveness with state-of-the-art networks.
Delayed Sampling and Automatic Rao-Blackwellization of Probabilistic Programs
pyro-ppl/funsor
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For inference with Sequential Monte Carlo, this automatically yields improvements such as locally-optimal proposals and Rao-Blackwellization.
NeRF--: Neural Radiance Fields Without Known Camera Parameters
Considering the problem of novel view synthesis (NVS) from only a set of 2D images, we simplify the training process of Neural Radiance Field (NeRF) on forward-facing scenes by removing the requirement of known or pre-computed camera parameters, including both intrinsics and 6DoF poses.
Contemporary Symbolic Regression Methods and their Relative Performance
We assess 14 symbolic regression methods and 7 machine learning methods on a set of 252 diverse regression problems.
Accurate parameter estimation for Bayesian Network Classifiers using Hierarchical Dirichlet Processes
The main result of this paper is to show that improved parameter estimation allows BNCs to outperform leading learning methods such as Random Forest for both 0-1 loss and RMSE, albeit just on categorical datasets.
Notes on Noise Contrastive Estimation and Negative Sampling
Estimating the parameters of probabilistic models of language such as maxent models and probabilistic neural models is computationally difficult since it involves evaluating partition functions by summing over an entire vocabulary, which may be millions of word types in size.
Variational Autoencoders for Collaborative Filtering
We introduce a generative model with multinomial likelihood and use Bayesian inference for parameter estimation.
Orthogonal Statistical Learning
We provide non-asymptotic excess risk guarantees for statistical learning in a setting where the population risk with respect to which we evaluate the target parameter depends on an unknown nuisance parameter that must be estimated from data.
