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Found 7 papers, 0 papers with code
Publicly Detectable Watermarking for Language Models
We construct the first provable watermarking scheme for language models with public detectability or verifiability: we use a private key for watermarking and a public key for watermark detection.
RDFC-GAN: RGB-Depth Fusion CycleGAN for Indoor Depth Completion
Raw depth images captured in indoor scenarios frequently exhibit extensive missing values due to the inherent limitations of the sensors and environments.
Overparameterization from Computational Constraints
In particular, for computationally bounded learners, we extend the recent result of Bubeck and Sellke [NeurIPS'2021] which shows that robust models might need more parameters, to the computational regime and show that bounded learners could provably need an even larger number of parameters.
RGB-Depth Fusion GAN for Indoor Depth Completion
In this paper, we design a novel two-branch end-to-end fusion network, which takes a pair of RGB and incomplete depth images as input to predict a dense and completed depth map.
Online Feature Screening for Data Streams with Concept Drift
Online screening methods are one of the categories of online feature selection methods.
Are screening methods useful in feature selection? An empirical study
Such an evaluation is needed to compare them with each other and also to answer whether they are at all useful, or a learning algorithm could do a better job without them.
A Novel Framework for Online Supervised Learning with Feature Selection
Current online learning methods suffer issues such as lower convergence rates and limited capability to recover the support of the true features compared to their offline counterparts.
