Deep Learning
Understanding Hallucinations in Diffusion Models through Mode Interpolation
But where do such hallucinations come from?
Understanding the Differences in Foundation Models: Attention, State Space Models, and Recurrent Neural Networks
Recurrent Neural Networks (RNNs) have been considered as more efficient alternatives.
Semantic Density: Uncertainty Quantification for Large Language Models through Confidence Measurement in Semantic Space
A new framework is proposed in this paper to address these issues.
IaC-Eval: A Code Generation Benchmark for Cloud Infrastructure-as-Code Programs
Infrastructure-as-Code (IaC), an important component of cloud computing, allows the definition of cloud infrastructure in high-level programs.
Learning Where to Edit Vision Transformers Y unqiao Y ang 1 Long-Kai Huang
In this paper, we take initial steps towards correcting predictive errors of ViTs, particularly those arising from subpopulation shifts.
InfoRM: Mitigating Reward Hacking in RLHF via Information-Theoretic Reward Modeling Y uchun Miao
With the advent of large language models (LLMs), reinforcement learning from human feedback (RLHF) has emerged as a pivotal technological paradigm to align models' behaviors with human values [
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.
Meta-Controller: Few-Shot Imitation of Unseen Embodiments and Tasks in Continuous Control
Robot embodiments vary widely in their morphological ( e.g., number and connectivity