On-device research index

arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,742 papers · 148 categories

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4 results for “Sequence-Aware”

Recurrent autoencoder with sequence-aware encodingcs.LG

Paper proposes a faster RAE with sequence-aware encoding.

problem Training recurrent autoencoders is challenging and time-consuming.

method Introduces a recurrent autoencoder with sequence-aware encoding using 1D convolutional layers.

result The proposed autoencoder trains faster than standard RAE.

Recurrent Autoencoder Sequence-aware Encoding Training Speed 1D Convolutional Layer RNN

2020-09-15abs ↗pdf ↗

DVE: Dynamic Variational Embeddings with Applications in Recommender Systemscs.IR

DVE models dynamic changes in feature embeddings for better sequence-aware applications.

problem Dynamic characterization of feature variations in time-varying data.

method Dynamic Variational Embedding (DVE) using recurrent neural networks.

result DVE models intrinsic nature and temporal variation of nodes effectively.

embedding recommender systems dynamic sequence-aware neural networks

2020-08-27abs ↗pdf ↗

Sequence-Aware Factorization Machines for Temporal Predictive Analyticscs.LG

SeqFM models dynamic and sequential features for better predictive analytics.

problem Inadequate handling of sequential dependencies in existing FM-based models.

method Introduces SeqFM, a novel model that incorporates multi-view self-attention to model static, dynamic, and their interactions.

result SeqFM outperforms existing models in ranking, classification, and regression tasks on six large-scale datasets.

Factorization Machines Temporal Predictive Analytics Sequence-Aware Dynamic Features Sequential Dependencies

2019-11-07abs ↗pdf ↗

Sequence-aware item recommendations for multiply repeated user-item interactionscs.IR

Improved item recommendations for repeat interactions using sequence analysis.

problem Limited effectiveness of traditional recommender systems in handling repeated user-item interactions.

method Designed a recommender system that considers sequences of item interactions for each user.

result Empirically shown to give highly accurate predictions and increase sales by 5%.

recommender systems sequence analysis item recommendations user-item interactions matrix completion

2023-04-02abs ↗pdf ↗