Customer-obsessed science
Research areas
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November 20, 20254 min readA new evaluation pipeline called FiSCo uncovers hidden biases and offers an assessment framework that evolves alongside language models.
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September 2, 20253 min read
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Featured news
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2024Evaluating a new ranking policy using data logged by a previously deployed policy requires a counterfactual (off-policy) estimator that corrects for presentation and selection biases. Some estimators (e.g., the position-based model) perform this correction by making strong assumptions about user behavior, which can lead to high bias if the assumptions are not met. Other estimators (e.g., the item-position
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CVPR 2024 Workshop on Fine-Grained Visual Categorization2024Extracting product attributes from images involves classifying subtle differences between similar objects. Visual Transformers (ViT) are powerful but usually supervised, while e-commerce labels are noisy or expensive to clean. In this work, we demonstrate the benefit for e-commerce of semi-supervised techniques like Semi-ViT, a ViT model fine-tuned with unlabeled data. We demonstrate that when Semi-ViT
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ICCSM 20242024Security Attacks (SA) refer to a kind of malicious activity in which SA causes information destruction. Existing works have failed to concentrate on various attacks that limit the model’s performance. Therefore, this paper presents Cumulative Distribution-GELU-Convolutional Neural Network (CD-GELU-CNN) and Feature Map-based-Lattice Rainbow Quantum Cryp-tography (FMLRQC) techniques for SA detection and prevention
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2024Music and media search spell correction is distinct as it involves named entities like artist, album and podcast names, keywords from track titles and catchy phrases from lyrics. Users often mix artist names and keywords from track title or lyrics making spell correction highly contextual. Data drift in search queries caused during calendar event days or a newly released music album, brings a unique challenge
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NAACL 2024 Workshop on Bridging Human-Computer Interaction and Natural Language Processing2024When users want to write a story with a language model (LM) assistant such as ChatGPT, it is often very difficult to provide a prompt that clearly specifies all their interests. For the providers of LM assistants, it is also difficult to ensure their output stories come from a dataset without copyright concerns. Motivated by these limitations, we propose a coarse-to-fine (C2F) tree-based story generation
Collaborations
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