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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October 2, 20253 min read
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September 2, 20253 min read
Featured news
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ICCV 20232023Multi-turn textual feedback-based fashion image retrieval focuses on a real-world setting, where users can iteratively provide information to refine retrieval results until they find an item that fits all their requirements. In this work, we present a novel memory-based method, called FashionNTM, for such a multi-turn system. Our framework incorporates a new Cascaded Memory Neural Turing Machine (CM-NTM
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ICCV 20232023We present the first active learning tool for fine-grained 3D part labeling, a problem which challenges even the most advanced deep learning (DL) methods due to the significant structural variations among the intricate parts. For the same reason, the necessary effort to annotate training data is tremendous, motivating approaches to minimize human involvement. Our labeling tool iteratively verifies or modifies
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International Conference on Neural Information Processing (ICONIP2023)2023In this work, we introduce a framework for cross-lingual speech synthesis, which involves an upstream Voice Conversion (VC) model and a downstream Text-To-Speech (TTS) model. The proposed framework consists of 4 stages. In the first two stages, we use a VC model to convert utterances in the target locale to the voice of the target speaker. In the third stage, the converted data is combined with the linguistic
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RecSys 2023 Workshop on Causality, Counterfactuals & Sequential Decision-Making (CONSEQUENCES)2023We propose two new estimators for off-policy evaluation of ranking policies, based on the idea of self-normalization. Importantly, these estimators are parameter-free and asymptotically unbiased. Experiments with synthetic data demonstrate that our estimators can be more accurate than other importance weighting estimators, owing to their ability to control variance, while adding minimal bias. From this,
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INLG 20232023Prior art investigating task-oriented dialog and automatic generation of such dialogs have focused on single-user dialogs between a single user and an agent. However, there is limited study on adapting such AI agents to multiuser conversations (involving multiple users and an agent). Multi-user conversations are richer than single-user conversations containing social banter and collaborative decision making
Collaborations
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