paradigm
发表于 2025-3-23 10:54:29
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AMEND
发表于 2025-3-23 14:53:43
On the Robustness of Global Feature Effect Explanationsrvised learning are an essential diagnostic tool for model debugging and scientific discovery in applied sciences. However, how vulnerable they are to data and model perturbations remains an open research question. We introduce several theoretical bounds for evaluating the robustness of partial depe
委屈
发表于 2025-3-23 19:58:52
Federated Learning with Flexible Architecturesciencies and potential inaccuracies in model training. This limitation hinders the widespread adoption of FL in diverse and resource-constrained environments, such as those with client devices ranging from powerful servers to mobile devices. To address this need, this paper introduces Federated Lear
不自然
发表于 2025-3-24 01:39:21
A Unified Data Augmentation Framework for Low-Resource Multi-domain Dialogue Generationning datasets are insufficient or entirely absent. To tackle this challenge, we propose a novel data .ugmentation framework for .ulti-.omain .ialogue .eneration, referred to as .. The AMD.G framework consists of a data augmentation process and a two-stage training approach: domain-agnostic training
NICHE
发表于 2025-3-24 03:33:02
Improving Diversity in Black-Box Few-Shot Knowledge Distillationst KD methods require a large training set and internal access to the teacher, which are rarely available due to various restrictions. These challenges have originated a more practical setting known as ., where the student is trained with . and a . teacher. Recent approaches typically generate addit
Ingredient
发表于 2025-3-24 08:43:30
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esthetician
发表于 2025-3-24 14:15:58
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FIN
发表于 2025-3-24 17:01:05
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deceive
发表于 2025-3-24 20:21:23
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Contend
发表于 2025-3-25 01:15:39
ADR: An Adversarial Approach to Learn Decomposed Representations for Causal Inference the pre-treatment covariates is the common practice for the inclusion of all possible confounders, it may aggravate the issue of data imbalance. In this paper, we theoretically show that including extra information would increase the variance lower bound. Based on the causal graph, we decompose the