101 citations · 301 across the 44 of their papers we have counts for
8 papers · 1 filter
Neural Cover Selection for Image Steganography
Karl Chahine, Hyeji Kim
In steganography, selecting an optimal cover image, referred to as cover selection, is pivotal for effective message concealment. Traditional methods have typically employed exhaus…
Generating High Dimensional User-Specific Wireless Channels using Diffusion Models
Taekyun Lee, Juseong Park, Hyeji Kim +1
Deep neural network (DNN)-based algorithms are emerging as an important tool for many physical and MAC layer functions in future wireless communication systems, including for large…
Enhancing K-user Interference Alignment for Discrete Constellations via Learning
Rajesh Mishra, Syed Jafar, Sriram Vishwanath +1
In this paper, we consider a K-user interference channel where interference among the users is neither too strong nor too weak, a scenario that is relatively underexplored in the l…
Fundamental Limits of Prompt Compression: A Rate-Distortion Framework for Black-Box Language Models
Alliot Nagle, Adway Girish, Marco Bondaschi +3
We formalize the problem of prompt compression for large language models (LLMs) and present a framework to unify token-level prompt compression methods which create hard prompts fo…
Local to Global: Learning Dynamics and Effect of Initialization for Transformers
Ashok Vardhan Makkuva, Marco Bondaschi, Chanakya Ekbote +4
In recent years, transformer-based models have revolutionized deep learning, particularly in sequence modeling. To better understand this phenomenon, there is a growing interest in…
LightCode: Light Analytical and Neural Codes for Channels with Feedback
Sravan Kumar Ankireddy, Krishna Narayanan, Hyeji Kim
The design of reliable and efficient codes for channels with feedback remains a longstanding challenge in communication theory. While significant improvements have been achieved by…
DeepPolar: Inventing Nonlinear Large-Kernel Polar Codes via Deep Learning
S Ashwin Hebbar, Sravan Kumar Ankireddy, Hyeji Kim +2
Progress in designing channel codes has been driven by human ingenuity and, fittingly, has been sporadic. Polar codes, developed on the foundation of Arikan's polarization kernel,…
Attention with Markov: A Framework for Principled Analysis of Transformers via Markov Chains
Ashok Vardhan Makkuva, Marco Bondaschi, Adway Girish +4
Attention-based transformers have achieved tremendous success across a variety of disciplines including natural languages. To deepen our understanding of their sequential modeling…