Summary of Unraveling the Gradient Descent Dynamics Of Transformers, by Bingqing Song et al.
Unraveling the Gradient Descent Dynamics of Transformersby Bingqing Song, Boran Han, Shuai Zhang, Jie Ding,…
Summary of More Expressive Attention with Negative Weights, by Ang Lv et al.
More Expressive Attention with Negative Weightsby Ang Lv, Ruobing Xie, Shuaipeng Li, Jiayi Liao, Xingwu…
Summary of Training Neural Networks As Recognizers Of Formal Languages, by Alexandra Butoi and Ghazal Khalighinejad and Anej Svete and Josef Valvoda and Ryan Cotterell and Brian Dusell
Training Neural Networks as Recognizers of Formal Languagesby Alexandra Butoi, Ghazal Khalighinejad, Anej Svete, Josef…
Summary of Convmixformer- a Resource-efficient Convolution Mixer For Transformer-based Dynamic Hand Gesture Recognition, by Mallika Garg et al.
ConvMixFormer- A Resource-efficient Convolution Mixer for Transformer-based Dynamic Hand Gesture Recognitionby Mallika Garg, Debashis Ghosh,…
Summary of 1-800-shared-tasks @ Nlu Of Devanagari Script Languages: Detection Of Language, Hate Speech, and Targets Using Llms, by Jebish Purbey et al.
1-800-SHARED-TASKS @ NLU of Devanagari Script Languages: Detection of Language, Hate Speech, and Targets using…
Summary of Spartan: a Sparse Transformer Learning Local Causation, by Anson Lei et al.
SPARTAN: A Sparse Transformer Learning Local Causationby Anson Lei, Bernhard Schölkopf, Ingmar PosnerFirst submitted to…
Summary of White-box Diffusion Transformer For Single-cell Rna-seq Generation, by Zhuorui Cui et al.
White-Box Diffusion Transformer for single-cell RNA-seq generationby Zhuorui Cui, Shengze Dong, Ding LiuFirst submitted to…
Summary of Spatially Constrained Transformer with Efficient Global Relation Modelling For Spatio-temporal Prediction, by Ashutosh Sao and Simon Gottschalk
Spatially Constrained Transformer with Efficient Global Relation Modelling for Spatio-Temporal Predictionby Ashutosh Sao, Simon GottschalkFirst…
Summary of Understanding Scaling Laws with Statistical and Approximation Theory For Transformer Neural Networks on Intrinsically Low-dimensional Data, by Alex Havrilla et al.
Understanding Scaling Laws with Statistical and Approximation Theory for Transformer Neural Networks on Intrinsically Low-dimensional…
Summary of Renaissance: Investigating the Pretraining Of Vision-language Encoders, by Clayton Fields et al.
Renaissance: Investigating the Pretraining of Vision-Language Encodersby Clayton Fields, Casey KenningtonFirst submitted to arxiv on:…