Search Results for author:
Found 7 papers, 6 papers with code
Uncovering Selective State Space Model's Capabilities in Lifelong Sequential Recommendation
We conduct extensive experiments to evaluate the performance of representative sequential recommendation models in the setting of lifelong sequences.
OpenGraph: Open-Vocabulary Hierarchical 3D Graph Representation in Large-Scale Outdoor Environments
In this work, we propose OpenGraph, the first open-vocabulary hierarchical graph representation designed for large-scale outdoor environments.
Cross-Layer Retrospective Retrieving via Layer Attention
Motivated by this, we devise a cross-layer attention mechanism, called multi-head recurrent layer attention (MRLA), that sends a query representation of the current layer to all previous layers to retrieve query-related information from different levels of receptive fields.
Recurrence along Depth: Deep Convolutional Neural Networks with Recurrent Layer Aggregation
This paper introduces a concept of layer aggregation to describe how information from previous layers can be reused to better extract features at the current layer.
DMBGN: Deep Multi-Behavior Graph Networks for Voucher Redemption Rate Prediction
In this paper, we propose a Deep Multi-behavior Graph Networks (DMBGN) to shed light on this field for the voucher redemption rate prediction.
Do RNN and LSTM have Long Memory?
The LSTM network was proposed to overcome the difficulty in learning long-term dependence, and has made significant advancements in applications.
Compact Autoregressive Network
Autoregressive networks can achieve promising performance in many sequence modeling tasks with short-range dependence.
