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Found 32 papers, 11 papers with code
Addressing Concept Shift in Online Time Series Forecasting: Detect-then-Adapt
For the state-of-the-art (SOTA) model, the MSE is reduced by $33. 3\%$.
Debiasing Multimodal Large Language Models
In the realms of computer vision and natural language processing, Large Vision-Language Models (LVLMs) have become indispensable tools, proficient in generating textual descriptions based on visual inputs.
Attention as Robust Representation for Time Series Forecasting
Time series forecasting is essential for many practical applications, with the adoption of transformer-based models on the rise due to their impressive performance in NLP and CV.
DiffsFormer: A Diffusion Transformer on Stock Factor Augmentation
Our research demonstrates the efficacy of leveraging AIGS and the DiffsFormer architecture to mitigate data scarcity in stock forecasting tasks.
Model-free Test Time Adaptation for Out-Of-Distribution Detection
We demonstrate the effectiveness of \abbr through comprehensive experiments on multiple OOD detection benchmarks, extensive empirical studies show that \abbr significantly improves the performance of OOD detection over state-of-the-art methods.
One Fits All: Universal Time Series Analysis by Pretrained LM and Specially Designed Adaptors
Despite the impressive achievements of pre-trained models in the fields of natural language processing (NLP) and computer vision (CV), progress in the domain of time series analysis has been limited.
Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlook
In this survey, we offer a comprehensive and up-to-date review of large models tailored (or adapted) for time series and spatio-temporal data, spanning four key facets: data types, model categories, model scopes, and application areas/tasks.
OneNet: Enhancing Time Series Forecasting Models under Concept Drift by Online Ensembling
Online updating of time series forecasting models aims to address the concept drifting problem by efficiently updating forecasting models based on streaming data.
Cross-LKTCN: Modern Convolution Utilizing Cross-Variable Dependency for Multivariate Time Series Forecasting Dependency for Multivariate Time Series Forecasting
The past few years have witnessed the rapid development in multivariate time series forecasting.
AdaNPC: Exploring Non-Parametric Classifier for Test-Time Adaptation
In particular, when the adaptation target is a series of domains, the adaptation accuracy of AdaNPC is 50% higher than advanced TTA methods.
One Fits All:Power General Time Series Analysis by Pretrained LM
The main challenge that blocks the development of pre-trained model for time series analysis is the lack of a large amount of data for training.
Free Lunch for Domain Adversarial Training: Environment Label Smoothing
A fundamental challenge for machine learning models is how to generalize learned models for out-of-distribution (OOD) data.
Ranked #5 on
Domain Adaptation
on Office-Home
Counterfactual-based Saliency Map: Towards Visual Contrastive Explanations for Neural Networks
Considering the insufficient study on such complex causal questions, we make the first attempt to explain different causal questions by contrastive explanations in a unified framework, ie., Counterfactual Contrastive Explanation (CCE), which visually and intuitively explains the aforementioned questions via a novel positive-negative saliency-based explanation scheme.
Siamese Transition Masked Autoencoders as Uniform Unsupervised Visual Anomaly Detector
The extensive experiments conducted demonstrate that the proposed ST-MAE method can advance state-of-the-art performance on multiple benchmarks across application scenarios with a superior inference efficiency, which exhibits great potential to be the uniform model for unsupervised visual anomaly detection.
TreeDRNet:A Robust Deep Model for Long Term Time Series Forecasting
Various deep learning models, especially some latest Transformer-based approaches, have greatly improved the state-of-art performance for long-term time series forecasting. However, those transformer-based models suffer a severe deterioration performance with prolonged input length, which prohibits them from using extended historical info. Moreover, these methods tend to handle complex examples in long-term forecasting with increased model complexity, which often leads to a significant increase in computation and less robustness in performance(e. g., overfitting).
A Feature Memory Rearrangement Network for Visual Inspection of Textured Surface Defects Toward Edge Intelligent Manufacturing
Subsequently, a contrastive-learning-based memory feature module (CMFM) is proposed to obtain discriminative representations and construct a normal feature memory bank in the latent space, which can be employed as a substitute for defects and fast anomaly scores at the patch level.
FiLM: Frequency improved Legendre Memory Model for Long-term Time Series Forecasting
Recent studies have shown that deep learning models such as RNNs and Transformers have brought significant performance gains for long-term forecasting of time series because they effectively utilize historical information.
Ranked #3 on
Time Series Forecasting
on ETTh2 (96) Univariate
Epipolar Focus Spectrum: A Novel Light Field Representation and Application in Dense-view Reconstruction
Existing light field representations, such as epipolar plane image (EPI) and sub-aperture images, do not consider the structural characteristics across the views, so they usually require additional disparity and spatial structure cues for follow-up tasks.
Stereo Unstructured Magnification: Multiple Homography Image for View Synthesis
A two-stage network is developed for novel view synthesis.
Progressive Backdoor Erasing via connecting Backdoor and Adversarial Attacks
Deep neural networks (DNNs) are known to be vulnerable to both backdoor attacks as well as adversarial attacks.
FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecasting
Although Transformer-based methods have significantly improved state-of-the-art results for long-term series forecasting, they are not only computationally expensive but more importantly, are unable to capture the global view of time series (e. g. overall trend).
Fast and Unsupervised Action Boundary Detection for Action Segmentation
To deal with the great number of untrimmed videos produced every day, we propose an efficient unsupervised action segmentation method by detecting boundaries, named action boundary detection (ABD).
SIDNet: Learning Shading-aware Illumination Descriptor for Image Harmonization
Before solving these two sub-problems, we first learn a shading-aware illumination descriptor via a well-designed neural rendering framework, of which the key is a shading bases module that generates multiple shading bases from the foreground image.
Scaled ReLU Matters for Training Vision Transformers
In this paper, we further investigate this problem and extend the above conclusion: only early convolutions do not help for stable training, but the scaled ReLU operation in the \textit{convolutional stem} (\textit{conv-stem}) matters.
Hierarchical contagions in the interdependent financial network
We derive the default cascade model and the fire-sale spillover model in a unified interdependent framework.
KVT: k-NN Attention for Boosting Vision Transformers
A key component in vision transformers is the fully-connected self-attention which is more powerful than CNNs in modelling long range dependencies.
Deep Anti-aliasing of Whole Focal Stack Using Slice Spectrum
Based on these two observations, we propose a learning-based FSS reconstruction approach for one-time aliasing removing over the whole focal stack.
Energy-Efficient Power Allocation and Q-Learning-Based Relay Selection for Relay-Aided D2D Communication
However, energy consumption is a critical issue for the D2D communication, especially for D2D relay networks.
Time Series Data Augmentation for Deep Learning: A Survey
In this paper, we systematically review different data augmentation methods for time series.
Online Learning and Decision-Making under Generalized Linear Model with High-Dimensional Data
We propose a minimax concave penalized multi-armed bandit algorithm under generalized linear model (G-MCP-Bandit) for a decision-maker facing high-dimensional data in an online learning and decision-making process.
Minimax Concave Penalized Multi-Armed Bandit Model with High-Dimensional Covariates
In addition, we develop a linear approximation method, the 2-step Weighted Lasso procedure, to identify the MCP estimator for the MCP-Bandit algorithm under non-i. i. d.
Real-time Egocentric Gesture Recognition on Mobile Head Mounted Displays
Mobile virtual reality (VR) head mounted displays (HMD) have become popular among consumers in recent years.
