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Found 12 papers, 5 papers with code
Learning to Rank when Grades Matter
In this paper, we conduct a rigorous study of learning to rank with grades, where both ranking performance and grade prediction performance are important.
On the Factory Floor: ML Engineering for Industrial-Scale Ads Recommendation Models
For industrial-scale advertising systems, prediction of ad click-through rate (CTR) is a central problem.
Real World Large Scale Recommendation Systems Reproducibility and Smooth Activations
We describe a novel family of smooth activations; Smooth ReLU (SmeLU), designed to improve reproducibility with mathematical simplicity, with potentially cheaper implementation.
Dropout Prediction Uncertainty Estimation Using Neuron Activation Strength
Dropout has been commonly used to quantify prediction uncertainty, i. e, the variations of model predictions on a given input example.
Looking Outside the Window: Wide-Context Transformer for the Semantic Segmentation of High-Resolution Remote Sensing Images
To overcome this limitation, we propose a Wide-Context Network (WiCoNet) for the semantic segmentation of HR RSIs.
MP-ResNet: Multi-path Residual Network for the Semantic segmentation of High-Resolution PolSAR Images
This CNN architecture can be used as a baseline method for future studies on the semantic segmentation of PolSAR images.
Smooth activations and reproducibility in deep networks
We propose a new family of activations; Smooth ReLU (\emph{SmeLU}), designed to give such better tradeoffs, while also keeping the mathematical expression simple, and thus implementation cheap.
DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems
Learning effective feature crosses is the key behind building recommender systems.
Ranked #5 on
Click-Through Rate Prediction
on KKBox
Beyond Point Estimate: Inferring Ensemble Prediction Variation from Neuron Activation Strength in Recommender Systems
Despite deep neural network (DNN)'s impressive prediction performance in various domains, it is well known now that a set of DNN models trained with the same model specification and the same data can produce very different prediction results.
Small Towers Make Big Differences
A delicate balance between multi-task generalization and multi-objective optimization is therefore needed for finding a better trade-off between efficiency and generalization.
Learning Multi-granular Quantized Embeddings for Large-Vocab Categorical Features in Recommender Systems
In this paper, we seek to learn highly compact embeddings for large-vocab sparse features in recommender systems (recsys).
Understanding and Improving Knowledge Distillation
Knowledge Distillation (KD) is a model-agnostic technique to improve model quality while having a fixed capacity budget.
