Summary of Efficient Visual Transformer by Learnable Token Merging, By Yancheng Wang et al.
Efficient Visual Transformer by Learnable Token Mergingby Yancheng Wang, Yingzhen YangFirst submitted to arxiv on:…
Summary of Technical Report: Improving the Properties Of Molecules Generated by Limo, By Vineet Thumuluri et al.
Technical report: Improving the properties of molecules generated by LIMOby Vineet Thumuluri, Peter Eckmann, Michael…
Summary of Fmamba: Mamba Based on Fast-attention For Multivariate Time-series Forecasting, by Shusen Ma et al.
FMamba: Mamba based on Fast-attention for Multivariate Time-series Forecastingby Shusen Ma, Yu Kang, Peng Bai,…
Summary of A Foundation Model For Soccer, by Ethan Baron and Daniel Hocevar and Zach Salehe
A Foundation Model for Soccerby Ethan Baron, Daniel Hocevar, Zach SaleheFirst submitted to arxiv on:…
Summary of Modality-order Matters! a Novel Hierarchical Feature Fusion Method For Cosam: a Code-switched Autism Corpus, by Mohd Mujtaba Akhtar et al.
Modality-Order Matters! A Novel Hierarchical Feature Fusion Method for CoSAm: A Code-Switched Autism Corpusby Mohd…
Summary of Frontiers Of Deep Learning: From Novel Application to Real-world Deployment, by Rui Xie
Frontiers of Deep Learning: From Novel Application to Real-World Deploymentby Rui XieFirst submitted to arxiv…
Summary of Polyformer: Scalable Node-wise Filters Via Polynomial Graph Transformer, by Jiahong Ma et al.
PolyFormer: Scalable Node-wise Filters via Polynomial Graph Transformerby Jiahong Ma, Mingguo He, Zhewei WeiFirst submitted…
Summary of Comparing and Contrasting Deep Learning Weather Prediction Backbones on Navier-stokes and Atmospheric Dynamics, by Matthias Karlbauer et al.
Comparing and Contrasting Deep Learning Weather Prediction Backbones on Navier-Stokes and Atmospheric Dynamicsby Matthias Karlbauer,…
Summary of Longhorn: State Space Models Are Amortized Online Learners, by Bo Liu et al.
Longhorn: State Space Models are Amortized Online Learnersby Bo Liu, Rui Wang, Lemeng Wu, Yihao…
Summary of Generative Language Model For Catalyst Discovery, by Dong Hyeon Mok and Seoin Back
Generative Language Model for Catalyst Discoveryby Dong Hyeon Mok, Seoin BackFirst submitted to arxiv on:…