Summary of An Image Is Worth More Than 16×16 Patches: Exploring Transformers on Individual Pixels, by Duy-kien Nguyen et al.
An Image is Worth More Than 16x16 Patches: Exploring Transformers on Individual Pixelsby Duy-Kien Nguyen,…
Summary of Learning Positional Encodings in Transformers Depends on Initialization, by Takuya Ito et al.
Learning positional encodings in transformers depends on initializationby Takuya Ito, Luca Cocchi, Tim Klinger, Parikshit…
Summary of A Concept-based Explainability Framework For Large Multimodal Models, by Jayneel Parekh et al.
A Concept-Based Explainability Framework for Large Multimodal Modelsby Jayneel Parekh, Pegah Khayatan, Mustafa Shukor, Alasdair…
Summary of Real Sampling: Boosting Factuality and Diversity Of Open-ended Generation Via Asymptotic Entropy, by Haw-shiuan Chang et al.
REAL Sampling: Boosting Factuality and Diversity of Open-Ended Generation via Asymptotic Entropyby Haw-Shiuan Chang, Nanyun…
Summary of Entropy-reinforced Planning with Large Language Models For Drug Discovery, by Xuefeng Liu et al.
Entropy-Reinforced Planning with Large Language Models for Drug Discoveryby Xuefeng Liu, Chih-chan Tien, Peng Ding,…
Summary of Transformers Provably Learn Sparse Token Selection While Fully-connected Nets Cannot, by Zixuan Wang et al.
Transformers Provably Learn Sparse Token Selection While Fully-Connected Nets Cannotby Zixuan Wang, Stanley Wei, Daniel…
Summary of Scaling the Vocabulary Of Non-autoregressive Models For Efficient Generative Retrieval, by Ravisri Valluri et al.
Scaling the Vocabulary of Non-autoregressive Models for Efficient Generative Retrievalby Ravisri Valluri, Akash Kumar Mohankumar,…
Summary of Rep: Resource-efficient Prompting For Rehearsal-free Continual Learning, by Sungho Jeon et al.
REP: Resource-Efficient Prompting for Rehearsal-Free Continual Learningby Sungho Jeon, Xinyue Ma, Kwang In Kim, Myeongjae…
Summary of Transformers Need Glasses! Information Over-squashing in Language Tasks, by Federico Barbero et al.
Transformers need glasses! Information over-squashing in language tasksby Federico Barbero, Andrea Banino, Steven Kapturowski, Dharshan…
Summary of What Should Embeddings Embed? Autoregressive Models Represent Latent Generating Distributions, by Liyi Zhang et al.
What Should Embeddings Embed? Autoregressive Models Represent Latent Generating Distributionsby Liyi Zhang, Michael Y. Li,…