Summary of Learning on Transformers Is Provable Low-rank and Sparse: a One-layer Analysis, by Hongkang Li et al.
Learning on Transformers is Provable Low-Rank and Sparse: A One-layer Analysisby Hongkang Li, Meng Wang,…
Summary of Xami — a Benchmark Dataset For Artefact Detection in Xmm-newton Optical Images, by Elisabeta-iulia Dima et al.
XAMI – A Benchmark Dataset for Artefact Detection in XMM-Newton Optical Imagesby Elisabeta-Iulia Dima, Pablo…
Summary of Make Graph Neural Networks Great Again: a Generic Integration Paradigm Of Topology-free Patterns For Traffic Speed Prediction, by Yicheng Zhou et al.
Make Graph Neural Networks Great Again: A Generic Integration Paradigm of Topology-Free Patterns for Traffic…
Summary of Metagreen: Meta-learning Inspired Transformer Selection For Green Semantic Communication, by Shubhabrata Mukherjee et al.
MetaGreen: Meta-Learning Inspired Transformer Selection for Green Semantic Communicationby Shubhabrata Mukherjee, Cory Beard, Sejun SongFirst…
Summary of Geomformer: a General Architecture For Geometric Molecular Representation Learning, by Tianlang Chen et al.
GeoMFormer: A General Architecture for Geometric Molecular Representation Learningby Tianlang Chen, Shengjie Luo, Di He,…
Summary of Causalformer: An Interpretable Transformer For Temporal Causal Discovery, by Lingbai Kong et al.
CausalFormer: An Interpretable Transformer for Temporal Causal Discoveryby Lingbai Kong, Wengen Li, Hanchen Yang, Yichao…
Summary of Metrik: Measurement-efficient Randomized Controlled Trials Using Transformers with Input Masking, by Sayeri Lala (1) and Niraj K. Jha (1) ((1) Princeton University et al.
METRIK: Measurement-Efficient Randomized Controlled Trials using Transformers with Input Maskingby Sayeri Lala, Niraj K. JhaFirst…
Summary of An All-mlp Sequence Modeling Architecture That Excels at Copying, by Chenwei Cui et al.
An All-MLP Sequence Modeling Architecture That Excels at Copyingby Chenwei Cui, Zehao Yan, Gedeon Muhawenayo,…
Summary of Decentralized Transformers with Centralized Aggregation Are Sample-efficient Multi-agent World Models, by Yang Zhang et al.
Decentralized Transformers with Centralized Aggregation are Sample-Efficient Multi-Agent World Modelsby Yang Zhang, Chenjia Bai, Bin…
Summary of Beyond Individual Facts: Investigating Categorical Knowledge Locality Of Taxonomy and Meronomy Concepts in Gpt Models, by Christopher Burger et al.
Beyond Individual Facts: Investigating Categorical Knowledge Locality of Taxonomy and Meronomy Concepts in GPT Modelsby…