Yuhui Ding
@yuhui_ding
PhD in machine learning @ETH. Prev: @Tsinghua_Uni
Is equivariance necessary for a good 3D molecule generative model? Check out our #icml2025 paper, which closes the performance gap between non-equivariant and equivariant diffusion models via rotational alignment, while also being more efficient (1/7): arxiv.org/abs/2506.10186
I'll be at #ICML2025 in Vancouver next week presenting our GIDD (Generalized Interpolating Discrete Diffusion) paper together with @yuhui_ding and @orvieto_antonio! Come chat to us at: 📅 Tue, July 15, 11:00–13:30 📍 Poster Session 1 East
Ever wondered how the loss landscape of Transformers differs from that of other architectures? Or which Transformer components make its loss landscape unique? With @unregularized & @f_dangel, we explore this via the Hessian in our #ICLR2025 spotlight paper! Key insights👇 1/8
Great work led by @dvruette !
🚨 NEW PAPER DROP! Wouldn't it be nice if LLMs could spot and correct their own mistakes? And what if we could do so directly from pre-training, without any SFT or RL? We present a new class of discrete diffusion models, called GIDD, that are able to do just that: 🧵1/12
Tuesday 1:30pm-3pm, Hall C 4-9 #515. Drop by our poster if you are interested in SSMs for graphs👇! Code: github.com/skeletondyh/GR…
Announcing AlphaFold 3: our state-of-the-art AI model for predicting the structure and interactions of all life’s molecules. 🧬 Here’s how we built it with @IsomorphicLabs and what it means for biology. 🧵 dpmd.ai/3URDiNo
Excited that our paper about SSM on graphs has been accepted by #ICML2024 ! See you in Vienna!
Inspired by recent breakthroughs in SSMs, we propose a new architecture, Graph Recurrent Encoding by Distance (GRED), for long-range graph representation learning: arxiv.org/abs/2312.01538 with @orvieto_antonio, @bobby_he and Thomas Hofmann (1/4)
I’ll be presenting "Scaling MLPs" at #NeurIPS2023, tomorrow (Wed) at 10:45am! Hyped to discuss things like inductive bias, the bitter lesson, compute-optimality and scaling laws 👷⚖️📈