Summary of A Benchmarking Study Of Kolmogorov-arnold Networks on Tabular Data, by Eleonora Poeta et al.
A Benchmarking Study of Kolmogorov-Arnold Networks on Tabular Databy Eleonora Poeta, Flavio Giobergia, Eliana Pastor,…
Summary of Why Llms Are Bad at Synthetic Table Generation (and What to Do About It), by Shengzhe Xu et al.
Why LLMs Are Bad at Synthetic Table Generation (and what to do about it)by Shengzhe…
Summary of Physics-informed Neural Networks For Parameter Learning Of Wildfire Spreading, by Konstantinos Vogiatzoglou et al.
Physics-informed neural networks for parameter learning of wildfire spreadingby Konstantinos Vogiatzoglou, Costas Papadimitriou, Vasilis Bontozoglou,…
Summary of Computing Within Limits: An Empirical Study Of Energy Consumption in Ml Training and Inference, by Ioannis Mavromatis and Kostas Katsaros and Aftab Khan
Computing Within Limits: An Empirical Study of Energy Consumption in ML Training and Inferenceby Ioannis…
Summary of Adaptive Adversarial Cross-entropy Loss For Sharpness-aware Minimization, by Tanapat Ratchatorn et al.
Adaptive Adversarial Cross-Entropy Loss for Sharpness-Aware Minimizationby Tanapat Ratchatorn, Masayuki TanakaFirst submitted to arxiv on:…
Summary of Predicting Probabilities Of Error to Combine Quantization and Early Exiting: Quee, by Florence Regol et al.
Predicting Probabilities of Error to Combine Quantization and Early Exiting: QuEEby Florence Regol, Joud Chataoui,…
Summary of Memory-efficient Gradient Unrolling For Large-scale Bi-level Optimization, by Qianli Shen et al.
Memory-Efficient Gradient Unrolling for Large-Scale Bi-level Optimizationby Qianli Shen, Yezhen Wang, Zhouhao Yang, Xiang Li,…
Summary of Aeon: a Python Toolkit For Learning From Time Series, by Matthew Middlehurst et al.
aeon: a Python toolkit for learning from time seriesby Matthew Middlehurst, Ali Ismail-Fawaz, Antoine Guillaume,…
Summary of Enhancing Robustness Of Data-driven Shm Models: Adversarial Training with Circle Loss, by Xiangli Yang et al.
Enhancing robustness of data-driven SHM models: adversarial training with circle lossby Xiangli Yang, Xijie Deng,…
Summary of Meat: Median-ensemble Adversarial Training For Improving Robustness and Generalization, by Zhaozhe Hu et al.
MEAT: Median-Ensemble Adversarial Training for Improving Robustness and Generalizationby Zhaozhe Hu, Jia-Li Yin, Bin Chen,…