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Found 11 papers, 6 papers with code
SOFTS: Efficient Multivariate Time Series Forecasting with Series-Core Fusion
Multivariate time series forecasting plays a crucial role in various fields such as finance, traffic management, energy, and healthcare.
QACP: An Annotated Question Answering Dataset for Assisting Chinese Python Programming Learners
In online learning platforms, particularly in rapidly growing computer programming courses, addressing the thousands of students' learning queries requires considerable human cost.
Twice Class Bias Correction for Imbalanced Semi-Supervised Learning
Differing from traditional semi-supervised learning, class-imbalanced semi-supervised learning presents two distinct challenges: (1) The imbalanced distribution of training samples leads to model bias towards certain classes, and (2) the distribution of unlabeled samples is unknown and potentially distinct from that of labeled samples, which further contributes to class bias in the pseudo-labels during training.
Learning Robust Precipitation Forecaster by Temporal Frame Interpolation
This achievement not only underscores the effectiveness of our methodologies but also establishes a new standard for deep learning applications in weather forecasting.
The Capacity and Robustness Trade-off: Revisiting the Channel Independent Strategy for Multivariate Time Series Forecasting
Our results conclude that the CD approach has higher capacity but often lacks robustness to accurately predict distributionally drifted time series.
On Pseudo-Labeling for Class-Mismatch Semi-Supervised Learning
Based on the findings, we propose to improve PL in class-mismatched SSL with two components -- Re-balanced Pseudo-Labeling (RPL) and Semantic Exploration Clustering (SEC).
Augmentation Component Analysis: Modeling Similarity via the Augmentation Overlaps
Self-supervised learning aims to learn a embedding space where semantically similar samples are close.
Abnormal Signal Recognition with Time-Frequency Spectrogram: A Deep Learning Approach
With the increasingly complex and changeable electromagnetic environment, wireless communication systems are facing jamming and abnormal signal injection, which significantly affects the normal operation of a communication system.
MotionInput v2.0 supporting DirectX: A modular library of open-source gesture-based machine learning and computer vision methods for interacting and controlling existing software with a webcam
We also introduce a series of bespoke gesture recognition classifications as DirectInput triggers, including gestures for idle states, auto calibration, depth capture from a 2D RGB webcam stream and tracking of facial motions such as mouth motions, winking, and head direction with rotation.
Revisiting Unsupervised Meta-Learning via the Characteristics of Few-Shot Tasks
Meta-learning has become a practical approach towards few-shot image classification, where "a strategy to learn a classifier" is meta-learned on labeled base classes and can be applied to tasks with novel classes.
Unsupervised Few-Shot Image Classification
Unsupervised Few-Shot Learning
Accelerating drug repurposing for COVID-19 via modeling drug mechanism of action with large scale gene-expression profiles
This work reported one state-of-the-art machine learning method to identify drug uses based on the cell image features of 1024 drugs generated in the LINCS program.
