Paper-Conference

am-ELO: A Stable Framework for Arena-based LLM Evaluation
Arena-based evaluation is a fundamental yet significant evaluation paradigm for modern AI models, especially large language models …
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ConvTimeNet: A Deep Hierarchical Fully Convolutional Model for Multivariate Time Series Analysis
Designing effective models for learning time series representations is foundational for time series analysis. Many previous works …
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Cross-Domain Pre-training with Language Models for Transferable Time Series Representations
Pre-training universal models across multiple domains to enhance downstream tasks is a prevalent learning paradigm. However, there has …
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InstrucTime: Advancing Time Series Classification with Multimodal Language Modeling
For the advancement of time series classification, we can summarize that most existing methods adopt a common learning-to-classify …
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Recognizing unseen objects via multimodal intensive knowledge graph propagation
Zero-Shot Learning (ZSL), which aims at automatically recognizing unseen objects, is a promising learning paradigm to understand new …
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Towards Personalized Evaluation of Large Language Models with An Anonymous Crowd-Sourcing Platform
Large language model evaluation plays a pivotal role in the enhancement of its capacity. Previously, numerous methods for evaluating …
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Exploring large language model for graph data understanding in online job recommendations
Large Language Models (LLMs) have revolutionized natural language processing tasks, demonstrating their exceptional capabilities in …
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Adaptive Normalization for Non-stationary Time Series Forecasting: A Temporal Slice Perspective
Deep learning models have progressively advanced time series forecasting due to their powerful capacity in capturing sequence …
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AdaptSSR: Pre-training User Model with Augmentation-Adaptive Self-Supervised Ranking
Federated recommendation (FedRec) can train personalized recommenders without collecting user data, but the decentralized nature makes …
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Federated News Recommendation with Fine-grained Interpolation and Dynamic Clustering
Researchers have successfully adapted the privacy-preserving Federated Learning (FL) to news recommendation tasks to better protect …
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