SCASRec unifies ranking and redundancy elimination for route lists via stepwise corrective rewards and an adaptive end-of-recommendation token, claiming SOTA results on two datasets and real deployment.
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CodeXGLUE supplies a standardized collection of 10 code-related tasks, 14 datasets, an evaluation platform, and BERT-, GPT-, and encoder-decoder-style baselines.
TREASURE is a transformer model for payment transactions that boosts abnormal behavior detection performance by 111% over production systems and improves recommendation models by 104% when used as an embedding provider.
A framework learns collection embeddings from runway images and applies RNN/LSTM to predict next-season designs at 78.42% average AUC over 32 years of data.
MA-DNN augments DNNs with per-user memory vectors capturing likes and dislikes to exploit historical behavior for CTR prediction while remaining simpler than RNNs.
Neural models using topic modeling for task-aware command recommendation and help prediction outperform baselines on analytics software logs.
A seq2seq model is proposed to learn universal embeddings from wearable and ambient sensor data for ADL recognition and semi-supervised learning.
citing papers explorer
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SCASRec: A Self-Correcting and Auto-Stopping Model for Generative Route List Recommendation
SCASRec unifies ranking and redundancy elimination for route lists via stepwise corrective rewards and an adaptive end-of-recommendation token, claiming SOTA results on two datasets and real deployment.
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CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation
CodeXGLUE supplies a standardized collection of 10 code-related tasks, 14 datasets, an evaluation platform, and BERT-, GPT-, and encoder-decoder-style baselines.
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TREASURE: The Visa Payment Foundation Model for High-Volume Transaction Understanding
TREASURE is a transformer model for payment transactions that boosts abnormal behavior detection performance by 111% over production systems and improves recommendation models by 104% when used as an embedding provider.
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Predicting Next-Season Designs on High Fashion Runway
A framework learns collection embeddings from runway images and applies RNN/LSTM to predict next-season designs at 78.42% average AUC over 32 years of data.
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Click-Through Rate Prediction with the User Memory Network
MA-DNN augments DNNs with per-user memory vectors capturing likes and dislikes to exploit historical behavior for CTR prediction while remaining simpler than RNNs.
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Stuck? No worries!: Task-aware Command Recommendation and Proactive Help for Analysts
Neural models using topic modeling for task-aware command recommendation and help prediction outperform baselines on analytics software logs.
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Activity2Vec: Learning ADL Embeddings from Sensor Data with a Sequence-to-Sequence Model
A seq2seq model is proposed to learn universal embeddings from wearable and ambient sensor data for ADL recognition and semi-supervised learning.