🏢 Northeastern University

Training-free architecture search finds optimal subnets in LLMs, boosting inference speed and slashing memory needs without retraining.

Personalized federated learning (PFL) often struggles with data scarcity and distribution shifts. pFedFDA, a novel algorithm, tackles this by framing representation learning as a generative modeling …

MatrixNet learns efficient group representations for improved deep learning on symmetry groups, achieving higher sample efficiency and generalization than existing methods.

GraphCroc, a novel graph autoencoder, leverages cross-correlation to accurately reconstruct complex graph structures, outperforming self-correlation-based methods.

Streamlined Inference, a novel training-free framework, dramatically reduces the computation and memory costs of video diffusion models without sacrificing quality, enabling high-resolution video gene…

This paper introduces a novel token pruning method for vision state space models, achieving significant computational reduction with minimal performance impact, addressing the limitations of directly …

FedAWE, a novel federated learning algorithm, efficiently handles intermittent and unpredictable client availability, ensuring fast and unbiased model training.

Bileve: a novel bi-level signature secures text provenance in LLMs against spoofing, enhancing detectability and reliability via fine-grained integrity checks and coarse-grained source tracing.