11 citations · 13 across the 6 of their papers we have counts for
9 papers
Rank-Aware Speculative Sampling for Diffusion Draft Trees
Marcello Bullo, Yanxiao Liu, Öykü Sıla Güner +2
Speculative sampling accelerates diffusion generation by verifying inexpensive draft states in parallel while preserving the target law. Recent tree-based methods allocate the para…
Accelerating Diffusion Sampling via Speculative Draft Trees
Marcello Bullo, Yanxiao Liu, Öykü Sıla Güner +2
Speculative sampling accelerates diffusion model generation by drafting inexpensive candidate states and correcting them under a coupling that preserves the target distribution exa…
Test-time Verification via Optimal Transport: Coverage, ROC, & Sub-optimality
Arpan Mukherjee, Marcello Bullo, Debabrota Basu +1
While test-time scaling with verification has shown promise in improving the performance of large language models (LLMs), the role of the verifier and its imperfections remain unde…
SharedRep-RLHF: A Shared Representation Approach to RLHF with Diverse Preferences
Arpan Mukherjee, Marcello Bullo, Deniz Gündüz
Uniform-reward reinforcement learning from human feedback (RLHF), which trains a single reward model to represent the preferences of all annotators, fails to capture the diversity…
Statistical Analysis and End-to-End Performance Evaluation of Traffic Models for Automotive Data
Marcello Bullo, Amir Ashtari Gargari, Paolo Testolina +2
Autonomous driving is a major paradigm shift in transportation, with the potential to enhance safety, optimize traffic congestion, and reduce fuel consumption. Although autonomous…
Energy-Aware Dynamic Neural Inference
Marcello Bullo, Seifallah Jardak, Pietro Carnelli +1
The growing demand for intelligent applications beyond the network edge, coupled with the need for sustainable operation, are driving the seamless integration of deep learning (DL)…
Sustainable Edge Intelligence Through Energy-Aware Early Exiting
Marcello Bullo, Seifallah Jardak, Pietro Carnelli +1
Deep learning (DL) models have emerged as a promising solution for the Internet of Things (IoT). However, due to their computational complexity, DL models consume significant amoun…
LE3D: A Lightweight Ensemble Framework of Data Drift Detectors for Resource-Constrained Devices
Ioannis Mavromatis, Adrian Sanchez-Mompo, Francesco Raimondo +8
Data integrity becomes paramount as the number of Internet of Things (IoT) sensor deployments increases. Sensor data can be altered by benign causes or malicious actions. Mechanism…
Hybrid Point Cloud Semantic Compression for Automotive Sensors: A Performance Evaluation
Andrea Varischio, Francesco Mandruzzato, Marcello Bullo +3
In a fully autonomous driving framework, where vehicles operate without human intervention, information sharing plays a fundamental role. In this context, new network solutions hav…