Tag: Representation Learning
All the articles with the tag "Representation Learning".
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Style Feature Extraction Using Contrastive Conditioned Variational Autoencoders with Mutual Information Constraints
Published: at 10:25 AM
93.98 🤔This paper proposes a novel method combining contrastive learning with conditional variational autoencoders and mutual information constraints to extract style features from unlabeled data, demonstrating effectiveness on simple datasets like MNIST while facing challenges with natural image datasets due to augmentation limitations and qualitative evaluation.
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Test-time Correlation Alignment
Published: at 12:21 AM 93.31 🤔本文提出测试时相关性对齐（TCA）范式，通过构建伪源域相关性并应用线性变换对齐测试数据特征，显著提升测试时适应（TTA）性能，同时保持高效性和源域知识。
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Nonparametric learning of covariate-based Markov jump processes using RKHS techniques
Published: at 12:18 AM 88.71 🤔本文提出了一种基于再生核希尔伯特空间（RKHS）的非参数化方法，通过频率学和贝叶斯框架建模连续时间马尔可夫链（CTMC）中协变量驱动的非线性转移率，显著提升了个体化状态转移预测的准确性。
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Communicating Activations Between Language Model Agents
Published: at 10:59 AM 87.71 🤔This paper introduces Activation Communication (AC), a novel method for inter-LLM communication using intermediate activations instead of natural language, achieving up to 27% performance improvement over traditional methods with significantly reduced compute across coordination games and reasoning benchmarks.
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SEAL: Steerable Reasoning Calibration of Large Language Models for Free
Published: at 06:16 PM 87.52 🤔SEAL, a training-free method, calibrates the reasoning process of Large Language Models by steering latent representations to reduce redundant thoughts, achieving up to 14.1% accuracy improvement and 50.4% token reduction across diverse benchmarks.