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Found 8 papers, 3 papers with code
Joint Learning of Deep Retrieval Model and Product Quantization based Embedding Index
Embedding index that enables fast approximate nearest neighbor(ANN) search, serves as an indispensable component for state-of-the-art deep retrieval systems.
Pre-training Tasks for User Intent Detection and Embedding Retrieval in E-commerce Search
BERT-style models pre-trained on the general corpus (e. g., Wikipedia) and fine-tuned on specific task corpus, have recently emerged as breakthrough techniques in many NLP tasks: question answering, text classification, sequence labeling and so on.
Sky pixel detection in outdoor imagery using an adaptive algorithm and machine learning
To address this problem, we present a new sky pixel detection system demonstrated to produce accurate results using a wide range of outdoor imagery types.
Beyond magnons in Nd2ScNbO7: An Ising pyrochlore antiferromagnet with all in all out order and random fields
Inelastic neutron scattering shows a low-energy flat-band, indicating a magnetic Hamiltonian similar to $\rm Nd_2Zr_2O_7$.
Strongly Correlated Electrons
Query Rewriting via Cycle-Consistent Translation for E-Commerce Search
Online A/B experiments show that it improves core e-commerce business metrics significantly.
Givens Coordinate Descent Methods for Rotation Matrix Learning in Trainable Embedding Indexes
Product quantization (PQ) coupled with a space rotation, is widely used in modern approximate nearest neighbor (ANN) search systems to significantly compress the disk storage for embeddings and speed up the inner product computation.
A Multi-Granularity Matching Attention Network for Query Intent Classification in E-commerce Retrieval
However, these models cannot capture multi-granularity matching features from queries and categories, which makes them hard to mitigate the gap in the expression between informal queries and categories.
Differentiable Retrieval Augmentation via Generative Language Modeling for E-commerce Query Intent Classification
Retrieval augmentation, which enhances downstream models by a knowledge retriever and an external corpus instead of by merely increasing the number of model parameters, has been successfully applied to many natural language processing (NLP) tasks such as text classification, question answering and so on.
