RelKD 2024: The Second International Workshop on Resource-Efficient Learning for Knowledge Discovery
Chuxu Zhanga, Dongkuan Xub, Kaize Dingc, Jundong Lid, Mojan Javaheripie, Subhabrata Mukherjeef, Nitesh V. Chawlag, Huan Liuh
- aBrandeis University, Waltham, MA, USA
- bNorth Carolina State University, Raleigh, NC, USA
- cNorthwestern University, Evanston, IL, USA
- dUniversity of Virginia, Charlottesville, VA, USA
- eMicrosoft Research, Redmond, WA, USA
- fHippocratic AI, San Francisco, CA, USA
- gUniversity of Notre Dame, Notre Dame, IN, USA
- hArizona State University, Temp, AZ, USA
Papers open inside SciRah from the original publisher or repository; SciRah never hosts them.
Abstract
Modern machine learning techniques, particularly deep learning, have showcased remarkable efficacy across numerous knowledge discovery and data mining applications. However, the advancement of many of these methods is frequently impeded by resource constraint challenges in many scenarios, such as limited labeled data (data-level), small model size requirements in real-world computing platforms (model-level), and efficient mapping of the computations to heterogeneous target hardware (system-level). Addressing all these factors is crucial for effectively and efficiently deploying developed models across a broad spectrum of real-world systems, including large-scale social network analysis, recommendation systems, and real-time anomaly detection. Therefore, there is a critical need to develop efficient learning techniques to address the challenges posed by resource limitations, whether from data, model/algorithm, or system/hardware perspectives. The proposed international workshop on "Resource-Efficient Learning for Knowledge Discovery (RelKD 2024)" will provide a great venue for academic researchers and industrial practitioners to share challenges, solutions, and future opportunities of resource-efficient learning.