Search Results for author:
Found 7 papers, 2 papers with code
Socialized Learning: A Survey of the Paradigm Shift for Edge Intelligence in Networked Systems
In response to these issues, this paper introduces socialized learning (SL) as a promising solution, further propelling the advancement of EI.
Calibration-compatible Listwise Distillation of Privileged Features for CTR Prediction
A typical practice is privileged features distillation (PFD): train a teacher model using all features (including privileged ones) and then distill the knowledge from the teacher model using a student model (excluding the privileged features), which is then employed for online serving.
Entire Space Cascade Delayed Feedback Modeling for Effective Conversion Rate Prediction
Furthermore, there is delayed feedback in both conversion and refund events and they are sequentially dependent, named cascade delayed feedback (CDF), which significantly harms data freshness for model training.
Representing Camera Response Function by a Single Latent Variable and Fully Connected Neural Network
In this paper, a new high-performance camera response model that uses a single latent variable and fully connected neural network is proposed.
Colour alignment for relative colour constancy via non-standard references
This makes it hard to achieve consistent colour assessment across a range of devices, and that undermines the performance of computer vision algorithms.
Few-Shot Partial-Label Learning
Partial-label learning (PLL) generally focuses on inducing a noise-tolerant multi-class classifier by training on overly-annotated samples, each of which is annotated with a set of labels, but only one is the valid label.
Spectral Illumination Correction: Achieving Relative Color Constancy Under the Spectral Domain
Achieving color constancy between and within images, i. e., minimizing the color difference between the same object imaged under nonuniform and varied illuminations is crucial for computer vision tasks such as colorimetric analysis and object recognition.
