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Found 6 papers, 4 papers with code
BeamVQ: Aligning Space-Time Forecasting Model via Self-training on Physics-aware Metrics
Data-driven deep learning has emerged as the new paradigm to model complex physical space-time systems.
Surge Phenomenon in Optimal Learning Rate and Batch Size Scaling
First, we raise the scaling law between batch sizes and optimal learning rates in the sign of gradient case, in which we prove that the optimal learning rate first rises and then falls as the batch size increases.
Retrieval-Augmented Generation for AI-Generated Content: A Survey
We first classify RAG foundations according to how the retriever augments the generator, distilling the fundamental abstractions of the augmentation methodologies for various retrievers and generators.
Experimental Analysis of Large-scale Learnable Vector Storage Compression
Learnable embedding vector is one of the most important applications in machine learning, and is widely used in various database-related domains.
Advancing Transformer Architecture in Long-Context Large Language Models: A Comprehensive Survey
Transformer-based Large Language Models (LLMs) have been applied in diverse areas such as knowledge bases, human interfaces, and dynamic agents, and marking a stride towards achieving Artificial General Intelligence (AGI).
Learning from Training Dynamics: Identifying Mislabeled Data Beyond Manually Designed Features
While mislabeled or ambiguously-labeled samples in the training set could negatively affect the performance of deep models, diagnosing the dataset and identifying mislabeled samples helps to improve the generalization power.
