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Found 10 papers, 4 papers with code
Neural-Hidden-CRF: A Robust Weakly-Supervised Sequence Labeler
In Neural-Hidden-CRF, we can capitalize on the powerful language model BERT or other deep models to provide rich contextual semantic knowledge to the latent ground truth sequence, and use the hidden CRF layer to capture the internal label dependencies.
Learning from Noisy Crowd Labels with Logics
This paper explores the integration of symbolic logic knowledge into deep neural networks for learning from noisy crowd labels.
UFNRec: Utilizing False Negative Samples for Sequential Recommendation
To this end, we propose a novel method that can Utilize False Negative samples for sequential Recommendation (UFNRec) to improve model performance.
A spatial-temporal short-term traffic flow prediction model based on dynamical-learning graph convolution mechanism
Location-GCN solves this problem by adding a new learnable matrix into the GCN mechanism, using the absolute value of this matrix to represent the distinct influence levels among different nodes.
Concentration of Random Feature Matrices in High-Dimensions
The spectra of random feature matrices provide essential information on the conditioning of the linear system used in random feature regression problems and are thus connected to the consistency and generalization of random feature models.
Conditioning of Random Feature Matrices: Double Descent and Generalization Error
In particular, we show that if the complexity ratio $\frac{N}{m}$ where $N$ is the number of neurons and $m$ is the number of data samples scales like $\log^{-1}(N)$ or $\log(m)$, then the random feature matrix is well-conditioned.
Learning from Multiple Annotators by Incorporating Instance Features
Learning from multiple annotators aims to induce a high-quality classifier from training instances, where each of them is associated with a set of possibly noisy labels provided by multiple annotators under the influence of their varying abilities and own biases.
CelebA-Spoof Challenge 2020 on Face Anti-Spoofing: Methods and Results
It is the largest face anti-spoofing dataset in terms of the numbers of the data and the subjects.
Bootstrapping Face Detection with Hard Negative Examples
Recently significant performance improvement in face detection was made possible by deeply trained convolutional networks.
Feature Selection with Redundancy-complementariness Dispersion
Feature selection has attracted significant attention in data mining and machine learning in the past decades.
