Deep Learning
cdf6f8e9fd9aeaf79b6024caec24f15b-Paper-Datasets_and_Benchmarks_Track.pdf
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Buffer of Thoughts: Thought-Augmented Reasoning with Large Language Models Ling Y ang 1, Zhaochen Y u
BoT has the potential to surpass Llama3-70B model.
Bridging The Gap between Low-rank and Orthogonal Adaptation via Householder Reflection Adaptation
HR-based orthogonal fine-tuning is equivalent to an adaptive low-rank adaptation.
Copycats: the many lives of a publicly available medical imaging dataset Amelia Jiménez-Sánchez
Medical Imaging (MI) datasets are fundamental to artificial intelligence in healthcare.
Decision-Making Behavior Evaluation Framework for LLMs under Uncertain Context
When making decisions under uncertainty, individuals often deviate from rational behavior, which can be evaluated across three dimensions: risk preference, probability weighting, and loss aversion.
DeltaDock: A Unified Framework for Accurate, Efficient, and Physically Reliable Molecular Docking Jiaxian Y an
Notably, in the blind docking setting, DeltaDock achieves a 31% relative improvement over the docking success rate compared with the previous state-of-the-art GDL model DiffDock.
Efficient Generative LLM Inference with R ecallable K ey-V al ue Eviction
Large Language Models (LLMs) are widely used in today's tasks of natural language processing.
Prediction-Powered Ranking of Large Language Models
If the rank-set of a model is large (small), it means that there is high (low) uncertainty in the ranking position of the model.
Customizing Language Models with Instance-wise LoRA for Sequential Recommendation
Sequential recommendation systems predict the next interaction item based on users' past interactions, aligning recommendations with individual preferences.
