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Found 5 papers, 2 papers with code
How Does the Textual Information Affect the Retrieval of Multimodal In-Context Learning?
This effectiveness, however, hinges on the appropriate selection of in-context examples, a process that is currently biased towards visual data, overlooking textual information.
Sparse MeZO: Less Parameters for Better Performance in Zeroth-Order LLM Fine-Tuning
While fine-tuning large language models (LLMs) for specific tasks often yields impressive results, it comes at the cost of memory inefficiency due to back-propagation in gradient-based training.
Helen: Optimizing CTR Prediction Models with Frequency-wise Hessian Eigenvalue Regularization
We explore the typical data characteristics and optimization statistics of CTR prediction, revealing a strong positive correlation between the top hessian eigenvalue and feature frequency.
Inhomogeneous Social Recommendation with Hypergraph Convolutional Networks
With the hypergraph convolutional networks, the social relations can be modeled in a more fine-grained manner, which more accurately depicts real users' preferences, and benefits the recommendation performance.
Towards More Efficient Federated Learning with Better Optimization Objects
Federated Learning (FL) is a privacy-protected machine learning paradigm that allows model to be trained directly at the edge without uploading data.
