Summary of On the Power Of Convolution Augmented Transformer, by Mingchen Li et al.
On the Power of Convolution Augmented Transformerby Mingchen Li, Xuechen Zhang, Yixiao Huang, Samet OymakFirst…
On the Power of Convolution Augmented Transformerby Mingchen Li, Xuechen Zhang, Yixiao Huang, Samet OymakFirst…
Deep Learning-based Anomaly Detection and Log Analysis for Computer Networksby Shuzhan Wang, Ruxue Jiang, Zhaoqi…
FairPFN: Transformers Can do Counterfactual Fairnessby Jake Robertson, Noah Hollmann, Noor Awad, Frank HutterFirst submitted…
Mamba Hawkes Processby Anningzhe Gao, Shan Dai, Yan HuFirst submitted to arxiv on: 7 Jul…
ReCAP: Recursive Cross Attention Network for Pseudo-Label Generation in Robotic Surgical Skill Assessmentby Julien Quarez,…
TRACE: TRansformer-based Attribution using Contrastive Embeddings in LLMsby Cheng Wang, Xinyang Lu, See-Kiong Ng, Bryan…
The Solution for the AIGC Inference Performance Optimization Competitionby Sishun Pan, Haonan Xu, Zhonghua Wan,…
Associative Recurrent Memory Transformerby Ivan Rodkin, Yuri Kuratov, Aydar Bulatov, Mikhail BurtsevFirst submitted to arxiv…
Learning to (Learn at Test Time): RNNs with Expressive Hidden Statesby Yu Sun, Xinhao Li,…
GPT vs RETRO: Exploring the Intersection of Retrieval and Parameter-Efficient Fine-Tuningby Aleksander Ficek, Jiaqi Zeng,…