output
20122026
most citedMLlib: Machine Learning in Apache Spark

961 citations

Showing 2024Show all

8 papers · 1 filter

cs.CV2024

Fitting Spherical Gaussians to Dynamic HDRI Sequences

Pascal Clausen, Li Ma, Mingming He +3

We present a technique for fitting high dynamic range illumination (HDRI) sequences using anisotropic spherical Gaussians (ASGs) while preserving temporal consistency in the compre…

cs.CV2024★ 15 cited

HIIF: Hierarchical Encoding based Implicit Image Function for Continuous Super-resolution

Yuxuan Jiang, Ho Man Kwan, Tianhao Peng +5

Recent advances in implicit neural representations (INRs) have shown significant promise in modeling visual signals for various low-vision tasks including image super-resolution (I…

eess.IV2024★ 2 cited

RTSR: A Real-Time Super-Resolution Model for AV1 Compressed Content

Yuxuan Jiang, Jakub Nawała, Chen Feng +4

Super-resolution (SR) is a key technique for improving the visual quality of video content by increasing its spatial resolution while reconstructing fine details. SR has been emplo…

cs.DB2024★ 3 cited

Intelligent Pooling: Proactive Resource Provisioning in Large-scale Cloud Service

Deepak Ravikumar, Alex Yeo, Yiwen Zhu +11

The proliferation of big data and analytic workloads has driven the need for cloud compute and cluster-based job processing. With Apache Spark, users can process terabytes of data…

cs.CV2024★ 16 cited

DifFRelight: Diffusion-Based Facial Performance Relighting

Mingming He, Pascal Clausen, Ahmet Levent Taşel +8

We present a novel framework for free-viewpoint facial performance relighting using diffusion-based image-to-image translation. Leveraging a subject-specific dataset containing div…

cs.IR2024★ 5 cited

Sliding Window Training -- Utilizing Historical Recommender Systems Data for Foundation Models

Swanand Joshi, Yesu Feng, Ko-Jen Hsiao +2

Long-lived recommender systems (RecSys) often encounter lengthy user-item interaction histories that span many years. To effectively learn long term user preferences, Large RecSys…