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Found 4 papers, 1 papers with code
XC-Cache: Cross-Attending to Cached Context for Efficient LLM Inference
Just-in-time processing of a context is inefficient due to the quadratic cost of self-attention operations, and caching is desirable.
Expecting The Unexpected: Towards Broad Out-Of-Distribution Detection
Our findings reveal that while these methods excel in detecting unknown classes, their performance is inconsistent when encountering other types of distribution shifts.
Flaky Performances when Pretraining on Relational Databases
We explore the downstream task performances for graph neural network (GNN) self-supervised learning (SSL) methods trained on subgraphs extracted from relational databases (RDBs).
On the Value of ML Models
We argue that, when establishing and benchmarking Machine Learning (ML) models, the research community should favour evaluation metrics that better capture the value delivered by their model in practical applications.
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