Education
Right this way: Can VLMs Guide Us to See More to Answer Questions?
In question-answering scenarios, humans can assess whether the available information is sufficient and seek additional information if necessary, rather than providing a forced answer.
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
Towards Understanding Evolving Patterns in Sequential Data
In many machine learning tasks, data is inherently sequential.
Universal Sample Coding
This is known as'channel simulation' or'reverse Shannon coding' (Bennett et al.,
Chain of Agents: Large Language Models Collaborating on Long-Context Tasks
Input reduction reduces the length of the input context before feeding to downstream LLMs.
SDformer: Similarity-driven Discrete Transformer For Time Series Generation
Comprehensive experiments show that our method significantly outperforms competing approaches in terms of the generated time series quality while also ensuring a short inference time.
A Label is Worth a Thousand Images in Dataset Distillation
Understanding how and why data distillation methods work is vital not only for improving these methods but also for revealing fundamental characteristics of "good" training data.
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.