Industry
B-ary Tree Push-Pull Method is Provably Efficient for Distributed Learning on Heterogeneous Data
Assumption 1.1 ensures that the gradient estimator
Holistic Evaluation of Text-to-Image Models Tony Lee
We curate 62 scenarios encompassing these aspects and evaluate 26 state-of-the-art text-to-image models on this benchmark.
Holistic Evaluation of Text-to-Image Models Tony Lee
We curate 62 scenarios encompassing these aspects and evaluate 26 state-of-the-art text-to-image models on this benchmark.
OneNet: Enhancing Time Series Forecasting Models under Concept Drift by Online Ensembling
In such cases, re-training the model from scratch could be time-consuming.
Thrust: Adaptively Propels Large Language Models with External Knowledge
To address these challenges, we propose the instance-level adaptive propulsion of external knowledge (IAPEK), where we only conduct the retrieval when necessary.
dd03f856fc7f2efeec8b1c796284561d-Paper-Conference.pdf
Short-horizon tasks require few steps: e.g., go to a tree and chop it down, dig a hole.
Appendix to "GLEMOS: Benchmark for Instantaneous Graph Learning Model Selection "
In Table 1, we report the cross-task testbed results in two transfer learning settings, i.e., (a) node classification to link prediction (Table 1a) and (b) link prediction to node classification (Table 1b).