4 citations · 4 across the 5 of their papers we have counts for
5 papers
LLM-JEPA: Large Language Models Meet Joint Embedding Predictive Architectures
Hai Huang, Yann LeCun, Randall Balestriero
Large Language Model (LLM) pretraining, finetuning, and evaluation rely on input-space reconstruction and generative capabilities. Yet, it has been observed in vision that embeddin…
RecBase: Generative Foundation Model Pretraining for Zero-Shot Recommendation
Sashuai Zhou, Weinan Gan, Qijiong Liu +7
Recent advances in LLM-based recommendation have shown promise, yet their cross-domain generalization is hindered by a fundamental mismatch between language-centric pretraining and…
IRBridge: Solving Image Restoration Bridge with Pre-trained Generative Diffusion Models
Hanting Wang, Tao Jin, Wang Lin +4
Bridge models in image restoration construct a diffusion process from degraded to clear images. However, existing methods typically require training a bridge model from scratch for…
RDI: An adversarial robustness evaluation metric for deep neural networks based on model statistical features
Jialei Song, Xingquan Zuo, Feiyang Wang +2
Deep neural networks (DNNs) are highly susceptible to adversarial samples, raising concerns about their reliability in safety-critical tasks. Currently, methods of evaluating adver…
Rethinking Evaluation Metrics of Open-Vocabulary Segmentaion
Hao Zhou, Tiancheng Shen, Xu Yang +4
In this paper, we highlight a problem of evaluation metrics adopted in the open-vocabulary segmentation. That is, the evaluation process still heavily relies on closed-set metrics…