Forwarded from Data Mining & Machine learning (Hirah Tang)
Medium
Understand Classifier Guidance and Classifier-free Guidance in diffusion models via Python pseudo-code
We introduce conditional controls in diffusion models in generative AI, which involves classifier guidance and classifier-free guidance.
Forwarded from Graph Machine Learning
GraphML News (Oct 5th) - ICLR 2025 Graph and Geometric DL Submissions
📚 Brace yourselves, for your browser is about to endure 50+ new tabs. All accepted NeurIPS 2024 papers are now visible (titles and abstracts), and a new batch of goodies from ICLR’25 has just arrived. Tried to select the papers that haven't yet appeared during the ICML/NeurIPS cycles. PDFs will be available on the respective OpenReview pages shortly:
Towards Graph Foundation Models:
GraphProp: Training the Graph Foundation Models using Graph Properties
GFSE: A Foundational Model For Graph Structural Encoding
Towards Neural Scaling Laws for Foundation Models on Temporal Graphs
Graph Generative Models:
Quality Measures for Dynamic Graph Generative Models
Improving Graph Generation with Flow Matching and Optimal Transport
Equivariant Denoisers Cannot Copy Graphs: Align Your Graph Diffusion Models
Topology-aware Graph Diffusion Model with Persistent Homology
Hierarchical Equivariant Graph Generation
Smooth Probabilistic Interpolation Benefits Generative Modeling for Discrete Graphs
GNN Theory:
Towards a Complete Logical Framework for GNN Expressiveness
Rethinking the Expressiveness of GNNs: A Computational Model Perspective
Learning Efficient Positional Encodings with Graph Neural Networks
Equivariant GNNs:
Improving Equivariant Networks with Probabilistic Symmetry Breaking
Does equivariance matter at scale?
Beyond Canonicalization: How Tensorial Messages Improve Equivariant Message Passing
Spacetime E(n) Transformer: Equivariant Attention for Spatio-temporal Graphs
Rethinking Efficient 3D Equivariant Graph Neural Networks
Generative modeling with molecules (hundreds of them actually):
AssembleFlow: Rigid Flow Matching with Inertial Frames for Molecular Assembly
RoFt-Mol: Benchmarking Robust Fine-tuning with Molecular Graph Foundation Models
Multi-Modal Foundation Models Induce Interpretable Molecular Graph Languages
MolSpectra: Pre-training 3D Molecular Representation with Multi-modal Energy Spectra
Reaction Graph: Toward Modeling Chemical Reactions with 3D Molecular Structures
Accelerating 3D Molecule Generation via Jointly Geometric Optimal Transport
📚 Brace yourselves, for your browser is about to endure 50+ new tabs. All accepted NeurIPS 2024 papers are now visible (titles and abstracts), and a new batch of goodies from ICLR’25 has just arrived. Tried to select the papers that haven't yet appeared during the ICML/NeurIPS cycles. PDFs will be available on the respective OpenReview pages shortly:
Towards Graph Foundation Models:
GraphProp: Training the Graph Foundation Models using Graph Properties
GFSE: A Foundational Model For Graph Structural Encoding
Towards Neural Scaling Laws for Foundation Models on Temporal Graphs
Graph Generative Models:
Quality Measures for Dynamic Graph Generative Models
Improving Graph Generation with Flow Matching and Optimal Transport
Equivariant Denoisers Cannot Copy Graphs: Align Your Graph Diffusion Models
Topology-aware Graph Diffusion Model with Persistent Homology
Hierarchical Equivariant Graph Generation
Smooth Probabilistic Interpolation Benefits Generative Modeling for Discrete Graphs
GNN Theory:
Towards a Complete Logical Framework for GNN Expressiveness
Rethinking the Expressiveness of GNNs: A Computational Model Perspective
Learning Efficient Positional Encodings with Graph Neural Networks
Equivariant GNNs:
Improving Equivariant Networks with Probabilistic Symmetry Breaking
Does equivariance matter at scale?
Beyond Canonicalization: How Tensorial Messages Improve Equivariant Message Passing
Spacetime E(n) Transformer: Equivariant Attention for Spatio-temporal Graphs
Rethinking Efficient 3D Equivariant Graph Neural Networks
Generative modeling with molecules (hundreds of them actually):
AssembleFlow: Rigid Flow Matching with Inertial Frames for Molecular Assembly
RoFt-Mol: Benchmarking Robust Fine-tuning with Molecular Graph Foundation Models
Multi-Modal Foundation Models Induce Interpretable Molecular Graph Languages
MolSpectra: Pre-training 3D Molecular Representation with Multi-modal Energy Spectra
Reaction Graph: Toward Modeling Chemical Reactions with 3D Molecular Structures
Accelerating 3D Molecule Generation via Jointly Geometric Optimal Transport