Federated learning (FL) holds strong potential for distributed machine learning, but in heterogeneous environments, Bandwidth-Constrained Clients (BCCs) often s...
Second-order optimization methods, which leverage curvature information, offer faster and more stable convergence than first-order methods such as stochastic gr...
In this work, we propose a novel energy-efficient spiking neural network (SNN)-based receiver for 5G-NR OFDM system, called neuromorphic receiver (NeuromorphicR...
Actor-based systems like Erlang/OTP power critical infrastructure -- from telecommunications to messaging platforms -- handling millions of concurrent connectio...
We show that deep neural networks trained across diverse tasks exhibit remarkably similar low-dimensional parametric subspaces. We provide the first large-scale...
Recent advances in illumination control extend image-based methods to video, yet still facing a trade-off between lighting fidelity and temporal consistency. Mo...
While methods exist for aligning flow matching models--a popular and effective class of generative models--with human preferences, existing approaches fail to a...
Segmentation of magnetic resonance images (MRI) facilitates analysis of human brain development by delineating anatomical structures. However, in infants and yo...
Recent unified multimodal large language models (MLLMs) have shown impressive capabilities, incorporating chain-of-thought (CoT) reasoning for enhanced text-to-...
Synthesizing high-fidelity frozen 3D scenes from monocular Mannequin-Challenge (MC) videos is a unique problem distinct from standard dynamic scene reconstructi...
Reward models are critical for aligning vision-language systems with human preferences, yet current approaches suffer from hallucination, weak visual grounding,...
We introduce ShadowDraw, a framework that transforms ordinary 3D objects into shadow-drawing compositional art. Given a 3D object, our system predicts scene par...
