GenAI for Scientific Discovery in Electrochemical Energy Storage: State‐of‐the‐Art and Perspectives from Nano‐and Micro‐Scale
The transition to electric vehicles (EVs) and the increased reliance on renewable energy
sources necessitate significant advancements in electrochemical energy storage systems …
sources necessitate significant advancements in electrochemical energy storage systems …
Tsi-bench: Benchmarking time series imputation
Effective imputation is a crucial preprocessing step for time series analysis. Despite the
development of numerous deep learning algorithms for time series imputation, the …
development of numerous deep learning algorithms for time series imputation, the …
[PDF][PDF] Rethinking the diffusion models for missing data imputation: A gradient flow perspective
幻灯片 1 Page 1 NeurIPS 2024, Main Track, Submission ID: #1850 Rethinking the Diffusion
Models for Missing Data Imputation: A Gradient Flow Perspective Presenter:Zhichao Chen …
Models for Missing Data Imputation: A Gradient Flow Perspective Presenter:Zhichao Chen …
Timedit: General-purpose diffusion transformers for time series foundation model
With recent advances in building foundation models for texts and video data, there is a surge
of interest in foundation models for time series. A family of models have been developed …
of interest in foundation models for time series. A family of models have been developed …
Diff-DGMN: A Diffusion-Based Dual Graph Multi-Attention Network for POI Recommendation
J Zuo, Y Zhang - IEEE Internet of Things Journal, 2024 - ieeexplore.ieee.org
Effective Point-of-Interest (POI) recommendation systems play a pivotal role in modern
location-aware applications and human mobility, facilitating customized suggestions for …
location-aware applications and human mobility, facilitating customized suggestions for …
A survey of spatio-temporal eeg data analysis: from models to applications
In recent years, the field of electroencephalography (EEG) analysis has witnessed
remarkable advancements, driven by the integration of machine learning and artificial …
remarkable advancements, driven by the integration of machine learning and artificial …
BayOTIDE: Bayesian online multivariate time series imputation with functional decomposition
In real-world scenarios like traffic and energy, massive time-series data with missing values
and noises are widely observed, even sampled irregularly. While many imputation methods …
and noises are widely observed, even sampled irregularly. While many imputation methods …
Diffusion models for intelligent transportation systems: A survey
Intelligent Transportation Systems (ITS) are vital in modern traffic management and
optimization, significantly enhancing traffic efficiency and safety. Recently, diffusion models …
optimization, significantly enhancing traffic efficiency and safety. Recently, diffusion models …
Urbandit: A foundation model for open-world urban spatio-temporal learning
The urban environment is characterized by complex spatio-temporal dynamics arising from
diverse human activities and interactions. Effectively modeling these dynamics is essential …
diverse human activities and interactions. Effectively modeling these dynamics is essential …
DACAD: Domain adaptation contrastive learning for anomaly detection in multivariate time series
In time series anomaly detection (TSAD), the scarcity of labeled data poses a challenge to
the development of accurate models. Unsupervised domain adaptation (UDA) offers a …
the development of accurate models. Unsupervised domain adaptation (UDA) offers a …