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Found 10 papers, 3 papers with code
Hiformer: Heterogeneous Feature Interactions Learning with Transformers for Recommender Systems
We identify two key challenges for applying the vanilla Transformer architecture to web-scale recommender systems: (1) Transformer architecture fails to capture the heterogeneous feature interactions in the self-attention layer; (2) The serving latency of Transformer architecture might be too high to be deployed in web-scale recommender systems.
HyperFormer: Learning Expressive Sparse Feature Representations via Hypergraph Transformer
Learning expressive representations for high-dimensional yet sparse features has been a longstanding problem in information retrieval.
Unified Embedding: Battle-Tested Feature Representations for Web-Scale ML Systems
Learning high-quality feature embeddings efficiently and effectively is critical for the performance of web-scale machine learning systems.
Empowering Long-tail Item Recommendation through Cross Decoupling Network (CDN)
In this work, we aim to improve tail item recommendations while maintaining the overall performance with less training and serving cost.
Surgical Gesture Recognition Based on Bidirectional Multi-Layer Independently RNN with Explainable Spatial Feature Extraction
It can provide explainable results by showing the regions of the surgical images that have a strong relationship with the surgical gesture classification results.
Real-time Surgical Environment Enhancement for Robot-Assisted Minimally Invasive Surgery Based on Super-Resolution
In Robot-Assisted Minimally Invasive Surgery (RAMIS), a camera assistant is normally required to control the position and zooming ratio of the laparoscope, following the surgeon's instructions.
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 #12 on
Click-Through Rate Prediction
on Criteo
PBBFMM3D: a parallel black-box algorithm for kernel matrix-vector multiplication
We introduce a parallel method that provably requires $O(N)$ operations to reduce the computation cost.
Mathematical Software
Deep & Cross Network for Ad Click Predictions
Feature engineering has been the key to the success of many prediction models.
Block Basis Factorization for Scalable Kernel Matrix Evaluation
Kernel methods are widespread in machine learning; however, they are limited by the quadratic complexity of the construction, application, and storage of kernel matrices.
