RNNs with ranking loss outperform item-to-item baselines for session-based recommendations on two datasets.
On the properties of neural machine translation: Encoder-decoder approaches
9 Pith papers cite this work. Polarity classification is still indexing.
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TCD-Arena is a new customizable testing framework that runs millions of experiments to map how 33 different assumption violations affect time series causal discovery methods and shows ensembles can boost overall robustness.
New seq2seq architectures for permutation indexing outperform baselines on synthetic reference-resolution tasks and reduce real decompilation error rates by 42%.
Primate ventral stream encodes visual stimuli through evolving neural dynamics that carry category information beyond any fixed spatial pattern during the initial feedforward pass.
ACARec attends over artist catalogs to generate CF embeddings for new tracks, more than doubling recall and NDCG versus content-only baselines in music recommendation.
EHR-RAGp is a retrieval-augmented EHR foundation model that employs prototype-guided retrieval to dynamically integrate relevant historical patient context, outperforming prior models on clinical prediction tasks.
Stable-GFlowNet improves training stability and attack diversity in LLM red-teaming by eliminating Z estimation via contrastive trajectory balance while preserving GFN optimality.
Delta6 delivers a low-cost 6-DOF force-sensing end-effector with 3.8% FS accuracy using sequence models, validated on robot-arm tasks like buffing and tight assembly.
The paper surveys key large language models, their training methods, datasets, evaluation benchmarks, and future research directions in the field.
citing papers explorer
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Session-based Recommendations with Recurrent Neural Networks
RNNs with ranking loss outperform item-to-item baselines for session-based recommendations on two datasets.
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TCD-Arena: Assessing Robustness of Time Series Causal Discovery Methods Against Assumption Violations
TCD-Arena is a new customizable testing framework that runs millions of experiments to map how 33 different assumption violations affect time series causal discovery methods and shows ensembles can boost overall robustness.
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Neural architectures for resolving references in program code
New seq2seq architectures for permutation indexing outperform baselines on synthetic reference-resolution tasks and reduce real decompilation error rates by 42%.
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The illusory simplicity of the feedforward pass: evidence for the dynamical nature of stimulus encoding along the primate ventral stream
Primate ventral stream encodes visual stimuli through evolving neural dynamics that carry category information beyond any fixed spatial pattern during the initial feedforward pass.
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Leveraging Artist Catalogs for Cold-Start Music Recommendation
ACARec attends over artist catalogs to generate CF embeddings for new tracks, more than doubling recall and NDCG versus content-only baselines in music recommendation.
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EHR-RAGp: Retrieval-Augmented Prototype-Guided Foundation Model for Electronic Health Records
EHR-RAGp is a retrieval-augmented EHR foundation model that employs prototype-guided retrieval to dynamically integrate relevant historical patient context, outperforming prior models on clinical prediction tasks.
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Stable-GFlowNet: Toward Diverse and Robust LLM Red-Teaming via Contrastive Trajectory Balance
Stable-GFlowNet improves training stability and attack diversity in LLM red-teaming by eliminating Z estimation via contrastive trajectory balance while preserving GFN optimality.
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Delta6: A Low-Cost, 6-DOF Force-Sensing Flexible End-Effector
Delta6 delivers a low-cost 6-DOF force-sensing end-effector with 3.8% FS accuracy using sequence models, validated on robot-arm tasks like buffing and tight assembly.
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Large Language Models: A Survey
The paper surveys key large language models, their training methods, datasets, evaluation benchmarks, and future research directions in the field.