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Causal Effect Identification in a Sub-Population with Latent Variables Amir Mohammad Abouei
Causal inference, i.e., understanding the effect of an intervention in a stochastic system, is a key focus of research in statistics and machine learning [
Change-pointDetectionforSparseandDense FunctionalDatainGeneralDimensions
We study the problem of change-point detection and localisation for functional data sequentially observed on a generald-dimensional space, where we allow thefunctional curvestobeeither sparsely ordensely sampled.
GENO -- GENeric Optimization for Classical Machine Learning
Soeren Laue, Matthias Mitterreiter, Joachim Giesen
Neural Information Processing Systems http://nips.cc/
Actively Testing Y our Model While It Learns: Realizing Label-Efficient Learning in Practice Dayou Yu
In active learning (AL), we focus on reducing the data annotation cost from the model training perspective. However, "testing", which often refers to the model
Synaptic Strength For Convolutional Neural Network
CHEN LIN, Zhao Zhong, Wu Wei, Junjie Yan
ModernCNNscanreachhundreds of millions of parameters and billions of operations, which makes it difficult to deploy. To alleviate aforementioned problem, various methods have been proposed to increase the efficiency of CNNs.
Learning Multiple Markov Chains via Adaptive Allocation
Mohammad Sadegh Talebi, Odalric-Ambrym Maillard
Neural Information Processing Systems http://nips.cc/
Practical and Asymptotically Exact Conditional Sampling in Diffusion Models
Conditional sampling is an essential primitive in the machine learning toolkit.
Practical and Asymptotically Exact Conditional Sampling in Diffusion Models
Conditional sampling is an essential primitive in the machine learning toolkit.