My name is Yihong Chen. I research on AI knowledge acquisition, specifically on how different AI systems can learn to abstract, represent and use concepts/symbols efficiecntly.
I am open to collaborations on topics related to embedding learning, link prediction, and language modeling. If you would like to get in touch, you can reach me by emailing yihong-chen AT outlook DOT com, or simply booking a Zoom meeting with me.
๐ฅ Mar 2024, Quanta Magazine covers our research on periodical embedding forgetting. Check out the article here.
๐ฅ Dec 2023, I will present our forgetting paper at NeurIPS 2023. Check out the poster here.
๐ฅ Sep 2023, our latest work Improving Language Plasticity via Pretraining with Active Forgetting is accepted by NeurIPS 2023!
๐ฅ Sep 2023, I presented our latest work on forgetting at IST-Unbabel seminar.
๐ฅ Jul 2023, I presented our latest work on forgetting language modelling at ELLIS Unconference 2023. The slides are available here. Feel free to leave your comments.
๐ฅ Jul 2023, discover the power of forgetting in language modelling! Our latest work, Improving Language Plasticity via Pretraining with Active Forgetting, shows how pretraining a language model with active forgetting can help it quickly learn new languages. You'll be amazed by the model plasticity imbued via pretraining with forgetting. Check it out :)
๐ฅ Nov 2022, our paper, REFACTOR GNNS: Revisiting Factorisation-based Models from a Message-Passing Perspective, will appear in NeurIPS 2022! If you're interested in understanding why FMs can be some special GNNs and make them usable on new graphs, check it out!
๐ฅ Jun 2022, if you're looking for a hands-on repo to start experimenting with link prediction, check out our repo ssl-relation-prediction. Simple code, easy to hack ๐