1 citations · 3 across the 4 of their papers we have counts for
4 papers · 1 filter
Mamba-3: Improved Sequence Modeling using State Space Principles
Aakash Lahoti, Kevin Y. Li, Berlin Chen +5
Scaling inference-time compute has emerged as an important driver of LLM performance, making inference efficiency a central focus of model design alongside model quality. While the…
Chimera: State Space Models Beyond Sequences
Aakash Lahoti, Tanya Marwah, Ratish Puduppully +1
Transformer-based deep learning methods have become the standard approach for modeling diverse data such as sequences, images, and graphs. These methods rely on self-attention, whi…
Hydra: Bidirectional State Space Models Through Generalized Matrix Mixers
Sukjun Hwang, Aakash Lahoti, Tri Dao +1
A wide array of sequence models are built on a framework modeled after Transformers, comprising alternating sequence mixer and channel mixer layers. This paper studies a unifying m…
Role of Locality and Weight Sharing in Image-Based Tasks: A Sample Complexity Separation between CNNs, LCNs, and FCNs
Aakash Lahoti, Stefani Karp, Ezra Winston +2
Vision tasks are characterized by the properties of locality and translation invariance. The superior performance of convolutional neural networks (CNNs) on these tasks is widely a…