6 papers
Training-Free Layout-to-Image Generation with Marginal Attention Constraints
Huancheng Chen, Jingtao Li, Weiming Zhuang +2
Recently, many text-to-image diffusion models have excelled at generating high-resolution images from text but struggle with precise control over spatial composition and object cou…
Transformers as Implicit State Estimators: In-Context Learning in Dynamical Systems
Usman Akram, Haris Vikalo
Predicting the behavior of a dynamical system from noisy observations of its past outputs is a classical problem encountered across engineering and science. For linear systems with…
Optimal Resource Allocation for ML Model Training and Deployment under Concept Drift
Hasan Burhan Beytur, Gustavo de Veciana, Haris Vikalo +1
We study how to allocate resources for training and deployment of machine learning (ML) models under concept drift and limited budgets. We consider a setting in which a model provi…
Batching-Aware Joint Model Onloading and Offloading for Hierarchical Multi-Task Inference
Seohyeon Cha, Kevin Chan, Gustavo de Veciana +1
The growing demand for intelligent services on resource-constrained edge devices has spurred the development of collaborative inference systems that distribute workloads across end…
Accelerated Distributed Stochastic Non-Convex Optimization over Time-Varying Directed Networks
Yiyue Chen, Abolfazl Hashemi, Haris Vikalo
Distributed stochastic non-convex optimization problems have recently received attention due to the growing interest of signal processing, computer vision, and natural language pro…
Heterogeneity-Guided Client Sampling: Towards Fast and Efficient Non-IID Federated Learning
Huancheng Chen, Haris Vikalo
Statistical heterogeneity of data present at client devices in a federated learning (FL) system renders the training of a global model in such systems difficult. Particularly chall…