activity
20222026
collaborators

7 papers

cs.LG2026

DP-LAC: Lightweight Adaptive Clipping for Differentially Private Federated Fine-tuning of Language Models

Haaris Mehmood, Jie Xu, Karthikeyan Saravanan +2

Federated learning (FL) enables the collaborative training of large-scale language models (LLMs) across edge devices while keeping user data on-device. However, FL still exposes se…

cs.CV2025

Mem-MLP: Real-Time 3D Human Motion Generation from Sparse Inputs

Sinan Mutlu, Georgios F. Angelis, Savas Ozkan +3

Realistic and smooth full-body tracking is crucial for immersive AR/VR applications. Existing systems primarily track head and hands via Head Mounted Devices (HMDs) and controllers…

cs.CL2025

Multi-Task Pre-Finetuning of Lightweight Transformer Encoders for Text Classification and NER

Junyi Zhu, Savas Ozkan, Andrea Maracani +3

Deploying natural language processing (NLP) models on mobile platforms requires models that can adapt across diverse applications while remaining efficient in memory and computatio…

cs.CV2025

Efficient 3D Full-Body Motion Generation from Sparse Tracking Inputs with Temporal Windows

Georgios Fotios Angelis, Savas Ozkan, Sinan Mutlu +3

To have a seamless user experience on immersive AR/VR applications, the importance of efficient and effective Neural Network (NN) models is undeniable, since missing body parts tha…

cs.CV2025

Accurate Scene Text Recognition with Efficient Model Scaling and Cloze Self-Distillation

Andrea Maracani, Savas Ozkan, Sijun Cho +6

Scaling architectures have been proven effective for improving Scene Text Recognition (STR), but the individual contribution of vision encoder and text decoder scaling remain under…

cs.LG2025

Guided Model Merging for Hybrid Data Learning: Leveraging Centralized Data to Refine Decentralized Models

Junyi Zhu, Ruicong Yao, Taha Ceritli +6

Current network training paradigms primarily focus on either centralized or decentralized data regimes. However, in practice, data availability often exhibits a hybrid nature, wher…

eess.IV2022

A Mixed Quantization Network for Computationally Efficient Mobile Inverse Tone Mapping

Juan Borrego-Carazo, Mete Ozay, Frederik Laboyrie +1

Recovering a high dynamic range (HDR) image from a single low dynamic range (LDR) image, namely inverse tone mapping (ITM), is challenging due to the lack of information in over- a…