Performance Analysis
Kernel PCA for Out-of-Distribution Detection Kun Fang
Out-of-Distribution (OoD) detection is vital for the reliability of Deep Neural Networks (DNNs).
ChronoEpilogi: Scalable Time-Series Variable Selection with Multiple Solutions
Lasso performance, with the added benefit of providing multiple solutions.
Detecting Bugs with Substantial Monetary Consequences by LLM and Rule-based Reasoning
According to a recent study, accounting bugs (i.e., incorrect implementations of domain-specific financial models) are the most prevalent type of vulnerability, and are one of the most difficult to find,
CODE: Contrasting Self-generated Description to Combat Hallucination in Large Multi-modal Models Junho Kim Hyun Jun Kim Y eon Ju Kim Yong Man Ro
Large Multi-modal Models (LMMs) have recently demonstrated remarkable abilities in visual context understanding and coherent response generation.
FOOGD: Federated Collaboration for Both Out-of-distribution Generalization and Detection Xinting Liao
FL further makes efforts to adapt real-world scenarios, i.e., adapting
Approximating mutual information of high-dimensional variables using learned representations
However, estimating MI between multidimensional variables is challenging because the number of samples necessary to converge to an accurate estimate scales unfavorably with dimensionality.
The iNaturalist Sounds Dataset
A current gap in the acoustic space is a large-scale, fine-grained dataset of species sounds that is easy to download and easy to use.
Long-Tailed Out-of-Distribution Detection via Normalized Outlier Distribution Adaptation
One key challenge in Out-of-Distribution (OOD) detection is the absence of ground-truth OOD samples during training.
Energy-based Hopfield Boosting for Out-of-Distribution Detection
Hopfield Boosting, a boosting approach, which leverages modern Hopfield energy to sharpen the decision boundary between the in-distribution and OOD data.