Deep Learning
Decoupled Kullback-Leibler Divergence Loss
Firstly, we address the limitation of KL/DKL in scenarios like knowledge distillation by breaking its asymmetric optimization property.
AgentBoard: An Analytical Evaluation Board of Multi-turn LLM Agents Chang Ma
However, the evaluation process presents substantial challenges.
Unity by Diversity: Improved Representation Learning for Multimodal V AEs Thomas M. Sutter
Such architectures impose hard constraints on the model.
8754ad68d557e633e5ce9dd02781e9cc-Paper-Conference.pdf
Proceedings of the International Conference on Machine Learning 2020
Tiny Time Mixers (TTMs): Fast Pre-trained Models for Enhanced Zero/Few-shot Forecasting of Multivariate Time Series Vijay Ekambaram IBM Research Bangalore, India
Consequently, recent research efforts have focused on developing pre-trained TS forecasting models.
Membership Inference on Text-to-image Diffusion Models via Conditional Likelihood Discrepancy
Membership inference arises in these contexts as a potential auditing method for detecting unauthorized data usage.
Efficient Lifelong Model Evaluation in an Era of Rapid Progress
Standardized benchmarks drive progress in machine learning.
Vaccine: Perturbation-aware Alignment for Large Language Models against Harmful Fine-tuning Attack
Inspired by our findings, we propose V accine, a perturbation-aware alignment technique to mitigate the security risk of users fine-tuning.