Grounding Language Models to Images for Multimodal Inputs and Outputs
Jing Yu Koh, Ruslan Salakhutdinov, Daniel Fried · arxiv.org
Query conditions: topic=machine-learning, and publish_at in 202306
Jing Yu Koh, Ruslan Salakhutdinov, Daniel Fried · arxiv.org
Tao Gong, Chengqi Lyu, Shilong Zhang, Yudong Wang, Miao Zheng, Qian Zhao, Kuikun Liu, Wenwei Zhang, Ping Luo, Kai Chen · arxiv.org
Junnan Li, Dongxu Li, Silvio Savarese, Steven Hoi · arxiv.org
Lei Li, Yuwei Yin, Shicheng Li, Liang Chen, Peiyi Wang, Shuhuai Ren, Mukai Li, Yazheng Yang, Jingjing Xu, Xu Sun, Lingpeng Kong, Qi Liu · arxiv.org
This page ranks Machine Learning content by topic match, content-type filter, checkout momentum, and freshness. The ranking is recalculated as new items and engagement signals arrive, so the top results stay practical for current workflows instead of remaining static or purely chronological.
The time suffix in this URL defines the publish-date window used for ranking. Year paths include items in that year, and YYYYMM paths include one calendar month. This makes comparisons cleaner when you want a focused snapshot rather than an all-time aggregate.
No. This ranking is editorial and signal-driven, not sponsored placement. Attendemia evaluates public metadata, source context, and usage momentum to rank candidates. Payment does not buy position, so readers can interpret the list as a curation surface rather than advertising inventory.