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Found 18 papers, 2 papers with code
Beyond Night Visibility: Adaptive Multi-Scale Fusion of Infrared and Visible Images
Thereby, we obtain an image free of low light and light effects, which improves the performance of nighttime object detection.
Large Language Models for Forecasting and Anomaly Detection: A Systematic Literature Review
This systematic literature review comprehensively examines the application of Large Language Models (LLMs) in forecasting and anomaly detection, highlighting the current state of research, inherent challenges, and prospective future directions.
PAC-Bayesian Adversarially Robust Generalization Bounds for Graph Neural Network
As corollaries, we derive better PAC-Bayesian robust generalization bounds for GCN in the standard setting, which improve the bounds in (Liao et al., 2020) by avoiding exponential dependence on the maximum node degree.
On Convergence of Adam for Stochastic Optimization under Relaxed Assumptions
The Adaptive Momentum Estimation (Adam) algorithm is highly effective in training various deep learning tasks.
High Probability Convergence of Adam Under Unbounded Gradients and Affine Variance Noise
To overcome these limitations, we provide a deep analysis and show that Adam could converge to the stationary point in high probability with a rate of $\mathcal{O}\left({\rm poly}(\log T)/\sqrt{T}\right)$ under coordinate-wise "affine" variance noise, not requiring any bounded gradient assumption and any problem-dependent knowledge in prior to tune hyper-parameters.
Unsupervised detection of small hyperreflective features in ultrahigh resolution optical coherence tomography
Recent advances in optical coherence tomography such as the development of high speed ultrahigh resolution scanners and corresponding signal processing techniques may reveal new potential biomarkers in retinal diseases.
LMQFormer: A Laplace-Prior-Guided Mask Query Transformer for Lightweight Snow Removal
Secondly, we design a Mask Query Transformer (MQFormer) to remove snow with the coarse mask, where we use two parallel encoders and a hybrid decoder to learn extensive snow features under lightweight requirements.
Ranked #1 on
Snow Removal
on SRRS
Kernel Conjugate Gradient Methods with Random Projections
We propose and study kernel conjugate gradient methods (KCGM) with random projections for least-squares regression over a separable Hilbert space.
Optimal Distributed Learning with Multi-pass Stochastic Gradient Methods
We study generalization properties of distributed algorithms in the setting of nonparametric regression over a reproducing kernel Hilbert space (RKHS).
Optimal Rates of Sketched-regularized Algorithms for Least-Squares Regression over Hilbert Spaces
We investigate regularized algorithms combining with projection for least-squares regression problem over a Hilbert space, covering nonparametric regression over a reproducing kernel Hilbert space.
Optimal Convergence for Distributed Learning with Stochastic Gradient Methods and Spectral Algorithms
We then extend our results to spectral-regularization algorithms (SRA), including kernel ridge regression (KRR), kernel principal component analysis, and gradient methods.
Optimal Rates for Spectral Algorithms with Least-Squares Regression over Hilbert Spaces
In this paper, we study regression problems over a separable Hilbert space with the square loss, covering non-parametric regression over a reproducing kernel Hilbert space.
Optimal Rates for Learning with Nyström Stochastic Gradient Methods
In the setting of nonparametric regression, we propose and study a combination of stochastic gradient methods with Nystr\"om subsampling, allowing multiple passes over the data and mini-batches.
Generalization Properties of Doubly Stochastic Learning Algorithms
In this paper, we provide an in-depth theoretical analysis for different variants of doubly stochastic learning algorithms within the setting of nonparametric regression in a reproducing kernel Hilbert space and considering the square loss.
Optimal Learning for Multi-pass Stochastic Gradient Methods
We analyze the learning properties of the stochastic gradient method when multiple passes over the data and mini-batches are allowed.
Optimal Rates for Multi-pass Stochastic Gradient Methods
As a byproduct, we derive optimal convergence results for batch gradient methods (even in the non-attainable cases).
Generalization Properties and Implicit Regularization for Multiple Passes SGM
We study the generalization properties of stochastic gradient methods for learning with convex loss functions and linearly parameterized functions.
Iterative Regularization for Learning with Convex Loss Functions
We consider the problem of supervised learning with convex loss functions and propose a new form of iterative regularization based on the subgradient method.
