7 citations · 34 across the 25 of their papers we have counts for
4 papers · 1 filter
Not How Many, But Which: Parameter Placement in Low-Rank Adaptation
Arijit Sehanobish, Charles Lovering
We study the \textit{parameter placement problem}: given a fixed budget of trainable entries within the B matrix of a LoRA adapter (A frozen), does the choice of which matt…
RelFlexformer: Efficient Attention 3D-Transformers for Integrable Relative Positional Encodings
Byeongchan Kim, Arijit Sehanobish, Avinava Dubey +2
We present a new class of efficient attention mechanisms applying universal 3D Relative Positional Encoding (RPE) methods given by arbitrary integrable modulation functions . Th…
EUGens: Efficient, Unified, and General Dense Layers
Sang Min Kim, Byeongchan Kim, Arijit Sehanobish +7
Efficient neural networks are essential for scaling machine learning models to real-time applications and resource-constrained environments. Fully-connected feedforward layers (FFL…
SWING: Unlocking Implicit Graph Representations for Graph Random Features
Alessandro Manenti, Avinava Dubey, Arijit Sehanobish +2
We propose SWING: Space Walks for Implicit Network Graphs, a new class of algorithms for computations involving Graph Random Features on graphs given by implicit representations (i…