Summary of Hybrid Dynamic Pruning: a Pathway to Efficient Transformer Inference, by Ghadeer Jaradat et al.
Hybrid Dynamic Pruning: A Pathway to Efficient Transformer Inferenceby Ghadeer Jaradat, Mohammed Tolba, Ghada Alsuhli,…
Summary of A Depression Detection Method Based on Multi-modal Feature Fusion Using Cross-attention, by Shengjie Li et al.
A Depression Detection Method Based on Multi-Modal Feature Fusion Using Cross-Attentionby Shengjie Li, Yinhao XiaoFirst…
Summary of Enhancing the Utility Of Privacy-preserving Cancer Classification Using Synthetic Data, by Richard Osuala et al.
Enhancing the Utility of Privacy-Preserving Cancer Classification using Synthetic Databy Richard Osuala, Daniel M. Lang,…
Summary of When Can Transformers Compositionally Generalize In-context?, by Seijin Kobayashi et al.
When can transformers compositionally generalize in-context?by Seijin Kobayashi, Simon Schug, Yassir Akram, Florian Redhardt, Johannes…
Summary of Co-designing Binarized Transformer and Hardware Accelerator For Efficient End-to-end Edge Deployment, by Yuhao Ji et al.
Co-Designing Binarized Transformer and Hardware Accelerator for Efficient End-to-End Edge Deploymentby Yuhao Ji, Chao Fang,…
Summary of A Graph-based Adversarial Imitation Learning Framework For Reliable & Realtime Fleet Scheduling in Urban Air Mobility, by Prithvi Poddar et al.
A Graph-based Adversarial Imitation Learning Framework for Reliable & Realtime Fleet Scheduling in Urban Air…
Summary of Memo: Fine-grained Tensor Management For Ultra-long Context Llm Training, by Pinxue Zhao et al.
MEMO: Fine-grained Tensor Management For Ultra-long Context LLM Trainingby Pinxue Zhao, Hailin Zhang, Fangcheng Fu,…
Summary of Understanding Transformers Via N-gram Statistics, by Timothy Nguyen
Understanding Transformers via N-gram Statisticsby Timothy NguyenFirst submitted to arxiv on: 30 Jun 2024CategoriesMain: Computation…
Summary of Exploring Quantization For Efficient Pre-training Of Transformer Language Models, by Kamran Chitsaz et al.
Exploring Quantization for Efficient Pre-Training of Transformer Language Modelsby Kamran Chitsaz, Quentin Fournier, Gonçalo Mordido,…
Summary of Lrq: Optimizing Post-training Quantization For Large Language Models by Learning Low-rank Weight-scaling Matrices, By Jung Hyun Lee et al.
LRQ: Optimizing Post-Training Quantization for Large Language Models by Learning Low-Rank Weight-Scaling Matricesby Jung Hyun…