Summary of Efficient World Models with Context-aware Tokenization, by Vincent Micheli et al.
Efficient World Models with Context-Aware Tokenizationby Vincent Micheli, Eloi Alonso, François FleuretFirst submitted to arxiv…
Summary of All Random Features Representations Are Equivalent, by Luke Sernau et al.
All Random Features Representations are Equivalentby Luke Sernau, Silvano Bonacina, Rif A. SaurousFirst submitted to…
Summary of A Closer Look Into Mixture-of-experts in Large Language Models, by Ka Man Lo et al.
A Closer Look into Mixture-of-Experts in Large Language Modelsby Ka Man Lo, Zeyu Huang, Zihan…
Summary of Transformer Normalisation Layers and the Independence Of Semantic Subspaces, by Stephen Menary et al.
Transformer Normalisation Layers and the Independence of Semantic Subspacesby Stephen Menary, Samuel Kaski, Andre FreitasFirst…
Summary of Interpreting Attention Layer Outputs with Sparse Autoencoders, by Connor Kissane et al.
Interpreting Attention Layer Outputs with Sparse Autoencodersby Connor Kissane, Robert Krzyzanowski, Joseph Isaac Bloom, Arthur…
Summary of Paraphrase and Aggregate with Large Language Models For Minimizing Intent Classification Errors, by Vikas Yadav and Zheng Tang and Vijay Srinivasan
Paraphrase and Aggregate with Large Language Models for Minimizing Intent Classification Errorsby Vikas Yadav, Zheng…
Summary of Are Language Models Actually Useful For Time Series Forecasting?, by Mingtian Tan et al.
Are Language Models Actually Useful for Time Series Forecasting?by Mingtian Tan, Mike A. Merrill, Vinayak…
Summary of Sparser Is Faster and Less Is More: Efficient Sparse Attention For Long-range Transformers, by Chao Lou et al.
Sparser is Faster and Less is More: Efficient Sparse Attention for Long-Range Transformersby Chao Lou,…
Summary of Shadowllm: Predictor-based Contextual Sparsity For Large Language Models, by Yash Akhauri et al.
ShadowLLM: Predictor-based Contextual Sparsity for Large Language Modelsby Yash Akhauri, Ahmed F AbouElhamayed, Jordan Dotzel,…
Summary of Found in the Middle: Calibrating Positional Attention Bias Improves Long Context Utilization, by Cheng-yu Hsieh et al.
Found in the Middle: Calibrating Positional Attention Bias Improves Long Context Utilizationby Cheng-Yu Hsieh, Yung-Sung…