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Found 10 papers, 3 papers with code
A Preference-oriented Diversity Model Based on Mutual-information in Re-ranking for E-commerce Search
Subsequently, we derive a utility matrix based on the correlations, enabling the adaptive ranking of items in line with user preferences and establishing a balance between the aforementioned objectives.
Optimizing E-commerce Search: Toward a Generalizable and Rank-Consistent Pre-Ranking Model
In large e-commerce platforms, search systems are typically composed of a series of modules, including recall, pre-ranking, and ranking phases.
DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model
MLA guarantees efficient inference through significantly compressing the Key-Value (KV) cache into a latent vector, while DeepSeekMoE enables training strong models at an economical cost through sparse computation.
TagCLIP: A Local-to-Global Framework to Enhance Open-Vocabulary Multi-Label Classification of CLIP Without Training
As a result, we dissect the preservation of patch-wise spatial information in CLIP and proposed a local-to-global framework to obtain image tags.
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.
Learning Multi-Stage Multi-Grained Semantic Embeddings for E-Commerce Search
Retrieving relevant items that match users' queries from billion-scale corpus forms the core of industrial e-commerce search systems, in which embedding-based retrieval (EBR) methods are prevailing.
Learning to Generate Time Series Conditioned Graphs with Generative Adversarial Nets
Differently, in this paper, we are interested in a novel problem named Time Series Conditioned Graph Generation: given an input multivariate time series, we aim to infer a target relation graph modeling the underlying interrelationships between time series with each node corresponding to each time series.
Multi-hop Selector Network for Multi-turn Response Selection in Retrieval-based Chatbots
Existing works mainly focus on matching candidate responses with every context utterance on multiple levels of granularity, which ignore the side effect of using excessive context information.
Ranked #5 on
Conversational Response Selection
on RRS
Connection Sensitive Attention U-NET for Accurate Retinal Vessel Segmentation
We develop a connection sensitive attention U-Net(CSAU) for accurate retinal vessel segmentation.
Role Playing Learning for Socially Concomitant Mobile Robot Navigation
In this paper, we present the Role Playing Learning (RPL) scheme for a mobile robot to navigate socially with its human companion in populated environments.
