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Found 6 papers, 1 papers with code
Boosting Large-scale Parallel Training Efficiency with C4: A Communication-Driven Approach
By leveraging this feature, C4 can rapidly identify the faulty components, swiftly isolate the anomaly, and restart the task, thereby avoiding resource wastage caused by delays in anomaly detection.
Rec4Ad: A Free Lunch to Mitigate Sample Selection Bias for Ads CTR Prediction in Taobao
Another line of work relies on costly uniform data that is inadequate to train industrial models.
Joint Optimization of Ranking and Calibration with Contextualized Hybrid Model
It can be attributed to the calibration ability of the pointwise loss since the prediction can be viewed as the click probability.
PICASSO: Unleashing the Potential of GPU-centric Training for Wide-and-deep Recommender Systems
However, we observe that GPU devices in training recommender systems are underutilized, and they cannot attain an expected throughput improvement as what it has achieved in CV and NLP areas.
One Model to Serve All: Star Topology Adaptive Recommender for Multi-Domain CTR Prediction
Concretely, STAR has the star topology, which consists of the shared centered parameters and domain-specific parameters.
A bi-diffusion based layer-wise sampling method for deep learning in large graphs
The Graph Convolutional Network (GCN) and its variants are powerful models for graph representation learning and have recently achieved great success on many graph-based applications.
