Search Results for author:
Found 10 papers, 5 papers with code
SURE: SUrvey REcipes for building reliable and robust deep networks
In this paper, we revisit techniques for uncertainty estimation within deep neural networks and consolidate a suite of techniques to enhance their reliability.
Ranked #1 on
Learning with noisy labels
on ANIMAL
Self-Attention through Kernel-Eigen Pair Sparse Variational Gaussian Processes
In this work, we propose Kernel-Eigen Pair Sparse Variational Gaussian Processes (KEP-SVGP) for building uncertainty-aware self-attention where the asymmetry of attention kernels is tackled by Kernel SVD (KSVD) and a reduced complexity is acquired.
Primal-Attention: Self-attention through Asymmetric Kernel SVD in Primal Representation
To the best of our knowledge, this is the first work that provides a primal-dual representation for the asymmetric kernel in self-attention and successfully applies it to modeling and optimization.
Ranked #2 on
Offline RL
on D4RL
Jigsaw-ViT: Learning Jigsaw Puzzles in Vision Transformer
In this paper, we explore solving jigsaw puzzle as a self-supervised auxiliary loss in ViT for image classification, named Jigsaw-ViT.
Ranked #1 on
Learning with noisy labels
on ANIMAL
Compressing Features for Learning with Noisy Labels
This decomposition provides three insights: (i) it shows that over-fitting is indeed an issue for learning with noisy labels; (ii) through an information bottleneck formulation, it explains why the proposed feature compression helps in combating label noise; (iii) it gives explanations on the performance boost brought by incorporating compression regularization into Co-teaching.
Ranked #10 on
Image Classification
on Clothing1M
(using extra training data)
Boosting Co-teaching with Compression Regularization for Label Noise
On Clothing1M, our approach obtains 74. 9% accuracy which is slightly better than that of DivideMix.
Ranked #12 on
Image Classification
on Clothing1M
(using extra training data)
Fast Learning in Reproducing Kernel Krein Spaces via Signed Measures
In this paper, we attempt to solve a long-lasting open question for non-positive definite (non-PD) kernels in machine learning community: can a given non-PD kernel be decomposed into the difference of two PD kernels (termed as positive decomposition)?
Two-stage Best-scored Random Forest for Large-scale Regression
We propose a novel method designed for large-scale regression problems, namely the two-stage best-scored random forest (TBRF).
DSTP-RNN: a dual-stage two-phase attention-based recurrent neural networks for long-term and multivariate time series prediction
The key to solve this problem is to capture the spatial correlations at the same time, the spatio-temporal relationships at different times and the long-term dependence of the temporal relationships between different series.
Real time expert system for anomaly detection of aerators based on computer vision technology and existing surveillance cameras
To tackle these aforementioned challenges, we propose a real-time expert system based on computer vision technology and existing surveillance cameras for anomaly detection of aerators, which consists of two modules, i. e., object region detection and working state detection.
Anomaly Detection
Cultural Vocal Bursts Intensity Prediction
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