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Found 22 papers, 7 papers with code
A Dataset of Open-Domain Question Answering with Multiple-Span Answers
Multi-span answer extraction, also known as the task of multi-span question answering (MSQA), is critical for real-world applications, as it requires extracting multiple pieces of information from a text to answer complex questions.
Pathformer: Multi-scale Transformers with Adaptive Pathways for Time Series Forecasting
Multi-scale division divides the time series into different temporal resolutions using patches of various sizes.
Explaining Time Series via Contrastive and Locally Sparse Perturbations
Explaining multivariate time series is a compound challenge, as it requires identifying important locations in the time series and matching complex temporal patterns.
Multimodal Urban Areas of Interest Generation via Remote Sensing Imagery and Geographical Prior
Unlike conventional AOI generation methods, such as the Road-cut method that segments road networks at various levels, our approach diverges from semantic segmentation algorithms that depend on pixel-level classification.
SkySense: A Multi-Modal Remote Sensing Foundation Model Towards Universal Interpretation for Earth Observation Imagery
Prior studies on Remote Sensing Foundation Model (RSFM) reveal immense potential towards a generic model for Earth Observation.
Learning Multi-Pattern Normalities in the Frequency Domain for Efficient Time Series Anomaly Detection
Anomaly detection significantly enhances the robustness of cloud systems.
RCAgent: Cloud Root Cause Analysis by Autonomous Agents with Tool-Augmented Large Language Models
Large language model (LLM) applications in cloud root cause analysis (RCA) have been actively explored recently.
LARA: A Light and Anti-overfitting Retraining Approach for Unsupervised Time Series Anomaly Detection
Retraining a large neural network model with limited data is vulnerable to overfitting.
Large Language Models Leverage External Knowledge to Extend Clinical Insight Beyond Language Boundaries
We systematically evaluate LLMs in the Chinese medical context and develop a novel in-context learning framework to enhance their performance.
Simultaneously Short- and Long-Term Temporal Modeling for Semi-Supervised Video Semantic Segmentation
In this work, we propose a novel feature enhancement network to simultaneously model short- and long-term temporal correlation.
Conformal Off-policy Prediction
Off-policy evaluation is critical in a number of applications where new policies need to be evaluated offline before online deployment.
KRNet: Towards Efficient Knowledge Replay
However, the number of stored latent codes in autoencoder increases linearly with the scale of data and the trained encoder is redundant for the replaying stage.
Ridgeless Regression with Random Features
Recent theoretical studies illustrated that kernel ridgeless regression can guarantee good generalization ability without an explicit regularization.
NetRCA: An Effective Network Fault Cause Localization Algorithm
Localizing the root cause of network faults is crucial to network operation and maintenance.
CloudRCA: A Root Cause Analysis Framework for Cloud Computing Platforms
As business of Alibaba expands across the world among various industries, higher standards are imposed on the service quality and reliability of big data cloud computing platforms which constitute the infrastructure of Alibaba Cloud.
EllipseNet: Anchor-Free Ellipse Detection for Automatic Cardiac Biometrics in Fetal Echocardiography
In this paper, we present an anchor-free ellipse detection network, namely EllipseNet, which detects the cardiac and thoracic regions in ellipse and automatically calculates the CTR and cardiac axis for fetal cardiac biometrics in 4-chamber view.
Divide-and-Assemble: Learning Block-wise Memory for Unsupervised Anomaly Detection
Surprisingly, by varying the granularity of division on feature maps, we are able to modulate the reconstruction capability of the model for both normal and abnormal samples.
RobustPeriod: Time-Frequency Mining for Robust Multiple Periodicity Detection
Periodicity detection is a crucial step in time series tasks, including monitoring and forecasting of metrics in many areas, such as IoT applications and self-driving database management system.
Fast Task Adaptation for Few-Shot Learning
The key lies in generalization of prior knowledge learned from large-scale base classes and fast adaptation of the classifier to novel classes.
Learning Incremental Triplet Margin for Person Re-identification
In particular, we propose a novel multi-stage training strategy which learns incremental triplet margin and improves triplet loss effectively.
Cascade Region Proposal and Global Context for Deep Object Detection
Deep region-based object detector consists of a region proposal step and a deep object recognition step.
Single-Image Crowd Counting via Multi-Column Convolutional Neural Network
To this end, we have proposed a simple but effective Multi-column Convolutional Neural Network (MCNN) architecture to map the image to its crowd density map.
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