2 citations · 5 across the 5 of their papers we have counts for
7 papers
Language Integration in Fine-Tuning Multimodal Large Language Models for Image-Based Regression
Roy H. Jennings, Genady Paikin, Roy Shaul +1
Multimodal Large Language Models (MLLMs) show promise for image-based regression tasks, but current approaches face key limitations. Recent methods fine-tune MLLMs using preset out…
Reconciling a Centroid-Hypothesis Conflict in Source-Free Domain Adaptation
Idit Diamant, Roy H. Jennings, Oranit Dror +2
Source-free domain adaptation (SFDA) aims to transfer knowledge learned from a source domain to an unlabeled target domain, where the source data is unavailable during adaptation.…
HPTQ: Hardware-Friendly Post Training Quantization
Hai Victor Habi, Reuven Peretz, Elad Cohen +5
Neural network quantization enables the deployment of models on edge devices. An essential requirement for their hardware efficiency is that the quantizers are hardware-friendly: u…
Flip Graphs
Roy H. Jennings
Flip graphs are graphs on combinatorial objects in which the adjacency relation reflects a local change in the underlying objects. In this thesis we introduce Yoke graphs, a family…
HMQ: Hardware Friendly Mixed Precision Quantization Block for CNNs
Hai Victor Habi, Roy H. Jennings, Arnon Netzer
Recent work in network quantization produced state-of-the-art results using mixed precision quantization. An imperative requirement for many efficient edge device hardware implemen…
Flip Graphs, Yoke Graphs and Diameter
Roy H. Jennings
In this paper we introduce Yoke graphs, a family of flip graphs that generalizes several previously studied families of graphs: colored triangle free triangulations, arc permutatio…
Geodesics in a Graph of Perfect Matchings
Roy H. Jennings
Let be the graph on the set of perfect matchings in the complete graph , where two perfect matchings are connected by an edge if their symmetric differenc…