2 citations · 2 across the 5 of their papers we have counts for
5 papers
ETT: Expanding the Long Context Understanding Capability of LLMs at Test-Time
Kiarash Zahirnia, Zahra Golpayegani, Walid Ahmed +1
Transformer-based Language Models' computation and memory overhead increase quadratically as a function of sequence length. The quadratic cost poses challenges when employing LLMs…
A Fourier Space Perspective on Diffusion Models
Fabian Falck, Teodora Pandeva, Kiarash Zahirnia +5
Diffusion models are state-of-the-art generative models on data modalities such as images, audio, proteins and materials. These modalities share the property of exponentially decay…
Deep Generative Models for Subgraph Prediction
Erfaneh Mahmoudzadeh, Parmis Naddaf, Kiarash Zahirnia +1
Graph Neural Networks (GNNs) are important across different domains, such as social network analysis and recommendation systems, due to their ability to model complex relational da…
Micro and Macro Level Graph Modeling for Graph Variational Auto-Encoders
Kiarash Zahirnia, Oliver Schulte, Parmis Naddaf +1
Generative models for graph data are an important research topic in machine learning. Graph data comprise two levels that are typically analyzed separately: node-level properties s…
Generating the Graph Gestalt: Kernel-Regularized Graph Representation Learning
Kiarash Zahirnia, Ankita Sakhuja, Oliver Schulte +3
Recent work on graph generative models has made remarkable progress towards generating increasingly realistic graphs, as measured by global graph features such as degree distributi…