3 citations · 3 across the 7 of their papers we have counts for
8 papers
AgentDiscover: Autonomous Discovery with Minimal Search Scaffolding
Mahdi Farahbakhsh, Ilan Sela, Fatemeh Doudi +4
Frameworks that use large language models for scientific discovery typically rely on a fixed, human-designed algorithm that decides what the model sees at each step, leaving the mo…
Real-Time Text Transmission via LLM-Based Entropy Coding over Fixed-Rate Channels
Vishnu Teja Kunde, Jean-Francois Chamberland, Krishna R. Narayanan +1
Learning, prediction, and compression are intimately connected: a model that accurately predicts the next symbol in a sequence can be coupled with a source coder to compress that s…
Complex Approximate Message Passing with Non-separable Denoising
Vishnu Teja Kunde, Alessandro Mirri, Jean-Francois Chamberland +1
Approximate Message Passing (AMP) is a general framework for iterative algorithms, originally developed for compressed sensing and later extended to a wide range of high-dimensiona…
Reinforcement Learning for Diffusion LLMs with Entropy-Guided Step Selection and Stepwise Advantages
Vishnu Teja Kunde, Fatemeh Doudi, Mahdi Farahbakhsh +3
Reinforcement learning (RL) has been effective for post-training autoregressive (AR) language models, but extending these methods to diffusion language models (DLMs) is challenging…
Inference-Time Search Using Side Information for Diffusion-Based Image Reconstruction
Mahdi Farahbakhsh, Vishnu Teja Kunde, Dileep Kalathil +2
Diffusion models have been used as priors for solving inverse problems. However, existing approaches typically overlook side information that could significantly improve reconstruc…
Approximate Message Passing for Multi-Preamble Detection in OTFS Random Access
Alessandro Mirri, Vishnu Teja Kunde, Enrico Paolini +1
This article addresses the problem of multiple preamble detection in random access systems based on orthogonal time frequency space (OTFS) signaling. This challenge is formulated a…
Camera-Based Localization and Enhanced Normalized Mutual Information
Vishnu Teja Kunde, Jean-Francois Chamberland, Siddharth Agarwal
Robust and fine localization algorithms are crucial for autonomous driving. For the production of such vehicles as a commodity, affordable sensing solutions and reliable localizati…
Transformers are Provably Optimal In-context Estimators for Wireless Communications
Vishnu Teja Kunde, Vicram Rajagopalan, Chandra Shekhara Kaushik Valmeekam +4
Pre-trained transformers exhibit the capability of adapting to new tasks through in-context learning (ICL), where they efficiently utilize a limited set of prompts without explicit…