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Found 11 papers, 2 papers with code
Deep Pattern Network for Click-Through Rate Prediction
These patterns harbor substantial potential to significantly enhance CTR prediction performance.
Ad Recommendation in a Collapsed and Entangled World
In this paper, we present an industry ad recommendation system, paying attention to the challenges and practices of learning appropriate representations.
AllSpark: A Multimodal Spatio-Temporal General Intelligence Model with Thirteen Modalities
We introduce the Language as Reference Framework (LaRF), a fundamental principle for constructing a multimodal unified model, aiming to strike a trade-off between the cohesion and autonomy among different modalities.
Decoupled Training: Return of Frustratingly Easy Multi-Domain Learning
Multi-domain learning (MDL) aims to train a model with minimal average risk across multiple overlapping but non-identical domains.
Generic and Robust Root Cause Localization for Multi-Dimensional Data in Online Service Systems
Such attribute combinations are substantial clues to the underlying root causes and thus are called root causes of multidimensional data.
AutoAttention: Automatic Field Pair Selection for Attention in User Behavior Modeling
In this paper, we propose a novel model named AutoAttention, which includes all item/user/context side fields as the query, and assigns a learnable weight for each field pair between behavior fields and query fields.
Cross-Task Knowledge Distillation in Multi-Task Recommendation
Multi-task learning (MTL) has been widely used in recommender systems, wherein predicting each type of user feedback on items (e. g, click, purchase) are treated as individual tasks and jointly trained with a unified model.
Follow the Prophet: Accurate Online Conversion Rate Prediction in the Face of Delayed Feedback
The delayed feedback problem is one of the imperative challenges in online advertising, which is caused by the highly diversified feedback delay of a conversion varying from a few minutes to several days.
Convolutional Normalizing Flows for Deep Gaussian Processes
Deep Gaussian processes (DGPs), a hierarchical composition of GP models, have successfully boosted the expressive power of their single-layer counterpart.
Decision Making for Autonomous Driving via Augmented Adversarial Inverse Reinforcement Learning
Simulation results show that the augmented AIRL outperforms all the baseline methods, and its performance is comparable with that of the experts on all of the four metrics.
Field-aware Calibration: A Simple and Empirically Strong Method for Reliable Probabilistic Predictions
It is often observed that the probabilistic predictions given by a machine learning model can disagree with averaged actual outcomes on specific subsets of data, which is also known as the issue of miscalibration.
