🏢 Central South University
What Factors Affect Multi-Modal In-Context Learning? An In-Depth Exploration
·2619 words·13 mins·
loading
·
loading
Multimodal Learning
Vision-Language Models
🏢 Central South University
Unlocking the full potential of multi-modal in-context learning requires understanding its core factors. This research systematically explores these factors, highlighting the importance of a multi-mod…
Ordering-Based Causal Discovery for Linear and Nonlinear Relations
·2689 words·13 mins·
loading
·
loading
AI Generated
AI Theory
Causality
🏢 Central South University
Causal discovery algorithm CaPS efficiently handles mixed linear and nonlinear relationships in observational data, outperforming existing methods on synthetic and real-world datasets.
Frequency Adaptive Normalization For Non-stationary Time Series Forecasting
·3101 words·15 mins·
loading
·
loading
Machine Learning
Deep Learning
🏢 Central South University
Frequency Adaptive Normalization (FAN) significantly boosts non-stationary time series forecasting accuracy by using Fourier transforms to identify and model dynamic trends and seasonal patterns, achi…
Fine Tuning Out-of-Vocabulary Item Recommendation with User Sequence Imagination
·2088 words·10 mins·
loading
·
loading
Recommendation Systems
🏢 Central South University
User Sequence Imagination (USIM) revolutionizes out-of-vocabulary item recommendation by leveraging user sequence imagination and RL fine-tuning, achieving superior performance in real-world e-commerc…
Confusion-Resistant Federated Learning via Diffusion-Based Data Harmonization on Non-IID Data
·2139 words·11 mins·
loading
·
loading
AI Generated
Machine Learning
Federated Learning
🏢 Central South University
CRFed, a novel federated learning framework, uses diffusion-based data harmonization and confusion-resistant strategies to significantly boost accuracy and robustness in non-IID data scenarios.