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Found 26 papers, 12 papers with code
Universal Time-Series Representation Learning: A Survey
Time-series data exists in every corner of real-world systems and services, ranging from satellites in the sky to wearable devices on human bodies.
Adaptive Shortcut Debiasing for Online Continual Learning
We propose a novel framework DropTop that suppresses the shortcut bias in online continual learning (OCL) while being adaptive to the varying degree of the shortcut bias incurred by continuously changing environment.
Toward Robustness in Multi-label Classification: A Data Augmentation Strategy against Imbalance and Noise
Multi-label classification poses challenges due to imbalanced and noisy labels in training data.
One Size Fits All for Semantic Shifts: Adaptive Prompt Tuning for Continual Learning
We identify the inadequacy of universal and specific prompting in handling these dynamic shifts.
Active Prompt Learning in Vision Language Models
In response to this inquiry, we observe that (1) simply applying a conventional active learning framework to pre-trained VLMs even may degrade performance compared to random selection because of the class imbalance in labeling candidates, and (2) the knowledge of VLMs can provide hints for achieving the balance before labeling.
Context-Aware Deep Time-Series Decomposition for Anomaly Detection in Businesses
Detecting anomalies in time series has become increasingly challenging as data collection technology develops, especially in realworld communication services, which require contextual information for precise prediction.
AnoViz: A Visual Inspection Tool of Anomalies in Multivariate Time Series
Here, we show that AnoViz streamlines the process of finding a potential cause of an anomaly with a deeper analysis of anomalous instances, giving explainability to any anomaly detector.
MG-TAR: Multi-View Graph Convolutional Networks for Traffic Accident Risk Prediction
Due to the continuing colossal socio-economic losses caused by traffic accidents, it is of prime importance to precisely forecast the traffic accident risk to reduce future accidents.
Meta-Query-Net: Resolving Purity-Informativeness Dilemma in Open-set Active Learning
Unlabeled data examples awaiting annotations contain open-set noise inevitably.
Adaptive Model Pooling for Online Deep Anomaly Detection from a Complex Evolving Data Stream
Online anomaly detection from a data stream is critical for the safety and security of many applications but is facing severe challenges due to complex and evolving data streams from IoT devices and cloud-based infrastructures.
Meta-Learning for Online Update of Recommender Systems
It is featured with an adaptive learning rate for each parameter-interaction pair for inducing a recommender to quickly learn users' up-to-date interest.
Data Collection and Quality Challenges in Deep Learning: A Data-Centric AI Perspective
In this survey, we study the research landscape for data collection and data quality primarily for deep learning applications.
Task-Agnostic Undesirable Feature Deactivation Using Out-of-Distribution Data
A deep neural network (DNN) has achieved great success in many machine learning tasks by virtue of its high expressive power.
Coherence-based Label Propagation over Time Series for Accelerated Active Learning
Time-series data are ubiquitous these days, but lack of the labels in time-series data is regarded as a hurdle for its broad applicability.
DF-TAR: A Deep Fusion Network for Citywide Traffic Accident Risk Prediction with Dangerous Driving Behavior
Because traffic accidents cause huge social and economic losses, it is of prime importance to precisely predict the traffic accident risk for reducing future accidents.
Accurate and Fast Federated Learning via IID and Communication-Aware Grouping
Federated learning has emerged as a new paradigm of collaborative machine learning; however, it has also faced several challenges such as non-independent and identically distributed(IID) data and high communication cost.
Robust Learning by Self-Transition for Handling Noisy Labels
In the seeding phase, the network is updated using all the samples to collect a seed of clean samples.
TornadoAggregate: Accurate and Scalable Federated Learning via the Ring-Based Architecture
Federated learning has emerged as a new paradigm of collaborative machine learning; however, many prior studies have used global aggregation along a star topology without much consideration of the communication scalability or the diurnal property relied on clients' local time variety.
Learning from Noisy Labels with Deep Neural Networks: A Survey
Deep learning has achieved remarkable success in numerous domains with help from large amounts of big data.
How does Early Stopping Help Generalization against Label Noise?
In this paper, we claim that such overfitting can be avoided by "early stopping" training a deep neural network before the noisy labels are severely memorized.
Carpe Diem, Seize the Samples Uncertain "At the Moment" for Adaptive Batch Selection
Compared with existing batch selection methods, the results showed that Recency Bias reduced the test error by up to 20. 97% in a fixed wall-clock training time.
MLAT: Metric Learning for kNN in Streaming Time Series
Learning a good distance measure for distance-based classification in time series leads to significant performance improvement in many tasks.
Prestopping: How Does Early Stopping Help Generalization Against Label Noise?
In this paper, we claim that such overfitting can be avoided by "early stopping" training a deep neural network before the noisy labels are severely memorized.
SELFIE: Refurbishing Unclean Samples for Robust Deep Learning
Owing to the extremely high expressive power of deep neural networks, their side effect is to totally memorize training data even when the labels are extremely noisy.
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Learning with noisy labels
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Ada-Boundary: Accelerating the DNN Training via Adaptive Boundary Batch Selection
Neural networks can converge faster with help from a smarter batch selection strategy.
Augmenting Recurrent Neural Networks with High-Order User-Contextual Preference for Session-Based Recommendation
The recent adoption of recurrent neural networks (RNNs) for session modeling has yielded substantial performance gains compared to previous approaches.
