114 citations · 116 across the 6 of their papers we have counts for
6 papers
HeatKV: Head-tuned KV-cache Compression for Visual Autoregressive Modeling
Jonathan Cederlund, Axel Berg, William Isaksson +3
Visual Autoregressive (VAR) models have recently demonstrated impressive image generation quality while maintaining low latency. However, they suffer from severe KV-cache memory co…
Towards Federated Learning with On-device Training and Communication in 8-bit Floating Point
Bokun Wang, Axel Berg, Durmus Alp Emre Acar +1
Recent work has shown that 8-bit floating point (FP8) can be used for efficiently training neural networks with reduced computational cost compared to training in FP32/FP16. In thi…
FedHeN: Federated Learning in Heterogeneous Networks
Durmus Alp Emre Acar, Venkatesh Saligrama
We propose a novel training recipe for federated learning with heterogeneous networks where each device can have different architectures. We introduce training with a side objectiv…
Federated Learning Based on Dynamic Regularization
Durmus Alp Emre Acar, Yue Zhao, Ramon Matas Navarro +3
We propose a novel federated learning method for distributively training neural network models, where the server orchestrates cooperation between a subset of randomly chosen device…
Faster Algorithms for Learning Convex Functions
Ali Siahkamari, Durmus Alp Emre Acar, Christopher Liao +3
The task of approximating an arbitrary convex function arises in several learning problems such as convex regression, learning with a difference of convex (DC) functions, and learn…
Budget Learning via Bracketing
Aditya Gangrade, Durmus Alp Emre Acar, Venkatesh Saligrama
Conventional machine learning applications in the mobile/IoT setting transmit data to a cloud-server for predictions. Due to cost considerations (power, latency, monetary), it is d…