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fix: add more negative samples to the template query
This is an immediate fix to make sure the article fetching program runs successfully by adding more negative samples.
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Time-series forecasting 1 https://www.semanticscholar.org/paper/A-Survey-on-Graph-Neural-Networks-for-Time-Series%3A-Jin-Koh/d3dbbd0f0de51b421a6220bd6480b8d2e99a88e9?utm_source=direct_link | ||
Time-series forecasting 1 https://www.semanticscholar.org/paper/Graph-Guided-Network-for-Irregularly-Sampled-Time-Zhang-Zeman/455bfc515eb279cc09023faa1f78c6efb61224ba?utm_source=direct_link | ||
Time-series forecasting 1 https://www.semanticscholar.org/paper/Taming-Local-Effects-in-Graph-based-Spatiotemporal-Cini-Marisca/e2a83369383aff37224170c1ae3d3870d5d9e419?utm_source=direct_link | ||
Time-series forecasting 1 https://www.semanticscholar.org/paper/Sparse-Graph-Learning-from-Spatiotemporal-Time-Cini-Zambon/0d01d21137a5af9f04e4b16a55a0f732cb8a540b?utm_source=direct_link | ||
Time-series forecasting 1 https://www.semanticscholar.org/paper/Graph-Deep-Learning-for-Time-Series-Forecasting-Cini-Marisca/ccea298edb788edf821aef58f0952c3e8debc25a?utm_source=direct_link | ||
Time-series forecasting 1 https://www.semanticscholar.org/paper/Large-Language-Models-Are-Zero-Shot-Time-Series-Gruver-Finzi/123acfbccca0460171b6b06a4012dbb991cde55b?utm_source=direct_link | ||
Time-series forecasting 1 https://www.semanticscholar.org/paper/Graph-Mamba%3A-Towards-Long-Range-Graph-Sequence-with-Wang-Tsepa/1df04f33a8ef313cc2067147dbb79c3ca7c5c99f?utm_source=direct_link | ||
Time-series forecasting 1 https://www.semanticscholar.org/paper/A-decoder-only-foundation-model-for-time-series-Das-Kong/f45f85fa1beaa795c24c4ff86f1f2deece72252f?utm_source=direct_link | ||
Time-series forecasting 1 https://www.semanticscholar.org/paper/UniTS%3A-Building-a-Unified-Time-Series-Model-Gao-Koker/bcbcc2e1af8bcf6b07edf866be95116a8ed0bf91?utm_source=direct_link | ||
Time-series forecasting 1 https://www.semanticscholar.org/paper/Unified-Training-of-Universal-Time-Series-Woo-Liu/4a111f7a3b56d0468f13104999844885157ef17d?utm_source=direct_link | ||
Time-series forecasting 1 https://www.semanticscholar.org/paper/Time-LLM%3A-Time-Series-Forecasting-by-Reprogramming-Jin-Wang/16f01c1b3ddd0b2abd5ddfe4fdb3f74767607277?utm_source=direct_link | ||
Time-series forecasting 1 https://www.semanticscholar.org/paper/Tiny-Time-Mixers-(TTMs)%3A-Fast-Pre-trained-Models-of-Ekambaram-Jati/e2e1f1b8e6c1b7f4f166e15b7c674945856a51b6?utm_source=direct_link | ||
Time-series forecasting 0 https://www.semanticscholar.org/paper/Self-Supervised-Contrastive-Pre-Training-For-Time-Zhang-Zhao/648d90b713997a771e2c49f02cd771e8b7b10b37?utm_source=direct_link | ||
Time-series forecasting 0 https://www.semanticscholar.org/paper/Domain-Adaptation-for-Time-Series-Under-Feature-and-He-Queen/5bd2c0acaf58c25f71617db2396188c74d29bf14?utm_source=direct_link | ||
Time-series forecasting 0 https://www.semanticscholar.org/paper/AZ-whiteness-test%3A-a-test-for-signal-uncorrelation-Zambon-Alippi/c3c94ccc094dcf546e8e31c9a42506302e837524?utm_source=direct_link | ||
Time-series forecasting 0 https://www.semanticscholar.org/paper/Graph-state-space-models-Zambon-Cini/279cd637b7e38bba1dd8915b5ce68cbcacecbe68?utm_source=direct_link | ||
Symbolic regression 1 https://www.semanticscholar.org/paper/Discovering-governing-equations-from-data-by-sparse-Brunton-Proctor/5d150cec2775f9bc863760448f14104cc8f42368?utm_source=direct_link | ||
Symbolic regression 1 https://www.semanticscholar.org/paper/Robust-learning-from-noisy%2C-incomplete%2C-data-via-Reinbold-Kageorge/60d0d998fa038182b3b69a57adb9b2f82d40589c?utm_source=direct_link | ||
Symbolic regression 1 https://www.semanticscholar.org/paper/Data-driven-discovery-of-coordinates-and-governing-Champion-Lusch/3c9961153493370500020c81527b3548c96f81e0?utm_source=direct_link | ||
Symbolic regression 1 https://www.semanticscholar.org/paper/Chaos-as-an-intermittently-forced-linear-system-Brunton-Brunton/3df50e9b73cc2937dfd651f4c3344bc99b7ed3f2?utm_source=direct_link | ||
Symbolic regression 1 https://www.semanticscholar.org/paper/Sparse-identification-of-nonlinear-dynamics-for-in-Kaiser-Kutz/b2eb064f432557c59ce99834d7dc7817e4687271?utm_source=direct_link | ||
Symbolic regression 1 https://www.semanticscholar.org/paper/Inferring-Biological-Networks-by-Sparse-of-Dynamics-Mangan-Brunton/06a0ba437d41a7c82c08a9636a4438c1b5031378?utm_source=direct_link | ||
Symbolic regression 1 https://www.semanticscholar.org/paper/SINDy-PI%3A-a-robust-algorithm-for-parallel-implicit-Kaheman-Kutz/4971f9abd024e40fbbdff2e9492745b68a6bca01?utm_source=direct_link | ||
Symbolic regression 0 https://www.semanticscholar.org/paper/Multidimensional-Approximation-of-Nonlinear-Systems-Gel%C3%9F-Klus/2b2aa13d4959073f61ad70555bc8c7da7d116196?utm_source=direct_link | ||
Physics-based GNNs 1 https://www.semanticscholar.org/paper/Learning-rigid-dynamics-with-face-interaction-graph-Allen-Rubanova/d6fdd8fc0c5fc052d040687e72638fb4297661cc?utm_source=direct_link | ||
Physics-based GNNs 1 https://www.semanticscholar.org/paper/Graph-network-simulators-can-learn-discontinuous%2C-Allen-Lopez-Guevara/979c112d5ed2f7653990a3591cdfccfad0dc27fd?utm_source=direct_link | ||
Physics-based GNNs 1 https://www.semanticscholar.org/paper/Learning-Mesh-Based-Simulation-with-Graph-Networks-Pfaff-Fortunato/9e20f6874feaaf7c9994f9875b1d9cab17a2fd59?utm_source=direct_link | ||
Koopman operator 1 https://www.semanticscholar.org/paper/From-Fourier-to-Koopman%3A-Spectral-Methods-for-Time-Lange-Brunton/11df7f23f72703ceefccc6367a6a18719850c53e?utm_source=direct_link | ||
Koopman operator 1 https://www.semanticscholar.org/paper/Modern-Koopman-Theory-for-Dynamical-Systems-Brunton-Budi%C5%A1i%C4%87/68b6ca45a588d538b36335b23f6969c960cf2e6e?utm_source=direct_link | ||
Koopman operator 1 https://www.semanticscholar.org/paper/Parsimony-as-the-ultimate-regularizer-for-machine-Kutz-Brunton/893768d957f8a46f0ba5bab11e5f2e2698ef1409?utm_source=direct_link | ||
Symbolic regression 1 https://www.semanticscholar.org/paper/Learning-Discrepancy-Models-From-Experimental-Data-Kaheman-Kaiser/73dd9c49f205280991826b2ea4b50344203916b4?utm_source=direct_link | ||
Symbolic regression 1 https://www.semanticscholar.org/paper/Discovery-of-Physics-From-Data%3A-Universal-Laws-and-Silva-Higdon/35e2571c17246577e0bc1b9de57a314c3b60e220?utm_source=direct_link | ||
Symbolic regression 1 https://www.semanticscholar.org/paper/Data-driven-discovery-of-partial-differential-Rudy-Brunton/0acd117521ef5aafb09fed02ab415523b330b058?utm_source=direct_link | ||
Symbolic regression 1 https://www.semanticscholar.org/paper/Ensemble-SINDy%3A-Robust-sparse-model-discovery-in-Fasel-Kutz/883547fdbd88552328a6615ec620f96e39c57018?utm_source=direct_link | ||
Symbolic regression 1 https://www.semanticscholar.org/paper/Learning-sparse-nonlinear-dynamics-via-optimization-Bertsimas-Gurnee/e6f0a85009481dcfd93aaa43ed3f980e5033b0d8?utm_source=direct_link | ||
Symbolic regression 1 https://www.semanticscholar.org/paper/A-Unified-Framework-for-Sparse-Relaxed-Regularized-Zheng-Askham/c0fc3882a9976f6a9cdc3a724bce184b786503da?utm_source=direct_link |