Post #798

Fresh ICLR’24 Submissions OpenReview has finally opened all submissions on OpenReview! Here is a fresh batch of papers I found interesting: Diffusion-based generation: Plug-And-Play Controllable Graph Generation With Diffusion Models Sparse Training of Discrete Diffusion Models for Graph Generation GraphMaker: Can Diffusion Models Generate Large Attributed Graphs? Graph Generation with Destination-Predicting Diffusion Mixture DIFUSCO-LNS: Diffusion-Guided Large Neighbourhood Search for Integer Linear Programming Graph Generation with K2 Trees Proteins: EquiPocket: an E(3)-Equivariant Geometric Graph Neural Network for Ligand Binding Site Prediction DiffDock-Pocket: Diffusion for Pocket-Level Docking with Sidechain Flexibility DiffSim: Aligning Diffusion Model and Molecular Dynamics Simulation for Accurate Blind Docking Rigid Protein-Protein Docking via Equivariant Elliptic-Paraboloid Interface Prediction Crystals and Material Generation: Space Group Constrained Crystal Generation Scalable Diffusion for Materials Generation Hierarchical GFlownet for Crystal Structure Generation MOFDiff: Coarse-grained Diffusion for Metal-Organic Framework Design Equivariant nets: Generalizing Denoising to Non-Equilibrium Structures Improves Equivariant Force Fields Orbit-Equivariant Graph Neural Networks E(3) Equivariant Scalar Interaction Network Rethinking the Benefits of Steerable Features in 3D Equivariant Graph Neural Networks Clifford Group Equivariant Simplicial Message Passing Networks Theory, Weisfeiler & Leman go: G2N2: Weisfeiler and Lehman go grammatical Attacking Graph Neural Networks with Bit Flips: Weisfeiler and Lehman Go Indifferent Beyond Weisfeiler-Lehman: A Quantitative Framework for GNN Expressiveness How Graph Neural Networks Learn: Lessons from Training Dynamics in Function Space New GNN architectures: How Powerful are Graph Neural Networks with Random Weights? Non-backtracking Graph Neural Networks Neural Priority Queues for Graph Neural Networks (GNNs) Graph Transformers: too many, too similar 😅 LLMs + Graphs: tons, I'd better stay away 🫠