132 citations
- Intel (United States)US16 papers
- Imperial College LondonGB6 papers
- Lawrence Berkeley National LaboratoryUS5 papers
- University of CambridgeGB4 papers
- University of Illinois Urbana-ChampaignUS4 papers
- Argonne National LaboratoryUS3 papers
- California University of PennsylvaniaUS3 papers
- Massachusetts Institute of TechnologyUS3 papers
- Max Planck Institute for InformaticsDE3 papers
- Max Planck SocietyDE3 papers
- Technicolor (France)FR3 papers
- The University of Texas at AustinUS3 papers
85 papers
Kronecker Products, Polarity Quotients and Large Graph Constructions
Kelly Isham, Kartik Lakhotia, Laura Monroe +1
In this paper, we establish a structural compatibility between the Kronecker product of bipartite graphs that admit polarity and their polarity quotient, and provide a sharp upper…
The Minimal Retroreflective Microfacet Model
Jamie Portsmouth, Matthias Raab, Laurent Belcour +1
We present the Minimal Retroreflective Microfacet (MRM) model, which turns any existing microfacet BSDF into a physically plausible retroreflective one by a single substitution: re…
EMOVIS: Emotion-Optimized Image Processing
Dor Barber, Rony Zatzarinni, Hava Matichin +1
In cinematography, visual attributes such as color grading, contrast, and brightness are manipulated to reinforce the emotional narrative of a scene. However, conventional Image Si…
SPEC CPU: The Next Generation
Mahesh Madhav, Allen Lee, Andres Mejia +31
The march toward developing relevant and robust CPU benchmarks continues with the introduction of SPEC CPU 2026, the next generation suite for measuring processor performance. This…
Online semi-supervised perception: Real-time learning without explicit feedback
Branislav Kveton, Michal Valko, Matthai Phillipose +1
This paper proposes an algorithm for real-time learning without explicit feedback. The algorithm combines the ideas of semi-supervised learning on graphs and online learning. In pa…
Semi-supervised learning with max-margin graph cuts
Branislav Kveton, Michal Valko, Ali Rahimi +1
This paper proposes a novel algorithm for semisupervised learning. This algorithm learns graph cuts that maximize the margin with respect to the labels induced by the harmonic func…