{"total":28,"rows":[{"id":"2406.06647","title":"How Efficient is LLM-Generated Code? A Rigorous & High-Standard Benchmark","created":"2024-06-10","cited_by_count":43,"area":null},{"id":"2310.15978","title":"Graph Deep Learning for Time Series Forecasting","created":"2023-10-24","cited_by_count":26,"area":null},{"id":"2505.12709","title":"Pave Your Own Path: Graph Gradual Domain Adaptation on Fused Gromov-Wasserstein Geodesics","created":"2025-05-19","cited_by_count":17,"area":null},{"id":"2412.08174","title":"Can Graph Neural Networks Learn Language with Extremely Weak Text Supervision?","created":"2024-12-11","cited_by_count":13,"area":null},{"id":"2405.16805","title":"Gradient Compressed Sensing: A Query-Efficient Gradient Estimator for High-Dimensional Zeroth-Order Optimization","created":"2024-05-27","cited_by_count":12,"area":null},{"id":"2505.18442","title":"Breaking Silos: Adaptive Model Fusion Unlocks Better Time Series Forecasting","created":"2025-05-24","cited_by_count":12,"area":null},{"id":"2403.11960","title":"Causality-Aware Spatiotemporal Graph Neural Networks for Spatiotemporal Time Series Imputation","created":"2024-03-18","cited_by_count":11,"area":null},{"id":"2410.06976","title":"Matcha: Mitigating Graph Structure Shifts with Test-Time Adaptation","created":"2024-10-09","cited_by_count":10,"area":null},{"id":"2411.01992","title":"Ask, and it shall be given: On the Turing completeness of prompting","created":"2024-11-04","cited_by_count":10,"area":null},{"id":"2502.19334","title":"Joint Optimal Transport and Embedding for Network Alignment","created":"2025-02-26","cited_by_count":10,"area":null},{"id":"2505.21366","title":"PLANETALIGN: A Comprehensive Python Library for Benchmarking Network Alignment","created":"2025-05-27","cited_by_count":10,"area":null},{"id":"2411.09852","title":"InterFormer: Effective Heterogeneous Interaction Learning for Click-Through Rate Prediction","created":"2024-11-15","cited_by_count":8,"area":null},{"id":"2502.08963","title":"Modeling Time-evolving Causality over Data Streams","created":"2025-02-13","cited_by_count":6,"area":null},{"id":"2403.12641","title":"Automated Contrastive Learning Strategy Search for Time Series","created":"2024-03-19","cited_by_count":5,"area":null},{"id":"2405.07233","title":"OXYGENERATOR: Reconstructing Global Ocean Deoxygenation Over a Century with Deep Learning","created":"2024-05-12","cited_by_count":5,"area":null},{"id":"2409.09930","title":"Mining of Switching Sparse Networks for Missing Value Imputation in Multivariate Time Series","created":"2024-09-16","cited_by_count":5,"area":null},{"id":"2410.11924","title":"NRFormer: Nationwide Nuclear Radiation Forecasting with Spatio-Temporal Transformer","created":"2024-10-15","cited_by_count":4,"area":null},{"id":"2408.04245","title":"Scalable Transformer for High Dimensional Multivariate Time Series Forecasting","created":"2024-08-08","cited_by_count":3,"area":null},{"id":"2312.07859","title":"Fine-grained Graph Rationalization","created":"2023-12-13","cited_by_count":1,"area":null},{"id":"2509.12530","title":"Graph Homophily Booster: Rethinking the Role of Discrete Features on Heterophilic Graphs","created":"2025-09-16","cited_by_count":1,"area":null},{"id":"2602.07256","title":"Graph homophily booster: Reimagining the role of discrete features in heterophilic graph learning","created":"2026-02-06","cited_by_count":1,"area":null},{"id":"2603.10160","title":"ReMix: Reinforcement routing for mixtures of LoRAs in LLM finetuning","created":"2026-03-10","cited_by_count":1,"area":null},{"id":"2605.24395","title":"AvAtar: Learning to Align via Active Optimal Transport","created":"2026-05-23","cited_by_count":1,"area":null},{"id":"2505.09017","title":"DyGSSM: Multi-view Dynamic Graph Embeddings with State Space Model Gradient Update","created":"2025-05-13","cited_by_count":0,"area":null},{"id":"2606.01283","title":"AdaKernel: Learning Adaptive Kernel Parameters for Spatiotemporal Graph Neural Networks","created":"2026-05-31","cited_by_count":0,"area":null},{"id":"2606.20554","title":"Structuring and Tokenizing Distributed User Interest Context for Generative Recommendation","created":"2026-06-18","cited_by_count":0,"area":null},{"id":"2607.23503","title":"Impute On-Demand: Adaptive Correlated Time Series Imputation for Changing Environments","created":"2026-07-26","cited_by_count":0,"area":null},{"id":"2609.05862","title":"Budgeted Task-Aware Acquisition of Dynamic Networks","created":"2026-09-05","cited_by_count":0,"area":null}],"next_cursor":null}