Tommi Jaakkola

33

Papers

1,173

Total Citations

Papers (33)

Style Transfer from Non-Parallel Text by Cross-Alignment

NeurIPS 2017arXiv

Predicting Organic Reaction Outcomes with Weisfeiler-Lehman Network

NeurIPS 2017arXiv

Learning Tree Structured Potential Games

Scaling Inference Time Compute for Diffusion Models

Correcting Diffusion Generation through Resampling

Local Aggregative Games

Principal Differences Analysis: Interpretable Characterization of Differences between Distributions

Mol2Image: Improved Conditional Flow Models for Molecule to Image Synthesis

AlphaFold Meets Flow Matching for Generating Protein Ensembles

Dirichlet Flow Matching with Applications to DNA Sequence Design

Generative Flows on Discrete State-Spaces: Enabling Multimodal Flows with Applications to Protein Co-Design

Harmonic Self-Conditioned Flow Matching for joint Multi-Ligand Docking and Binding Site Design

Subspace Diffusion Generative Models

DisCo-Diff: Enhancing Continuous Diffusion Models with Discrete Latents

From random walks to distances on unweighted graphs

Deriving Neural Architectures from Sequence and Graph Kernels

Sequence to Better Sequence: Continuous Revision of Combinatorial Structures

Junction Tree Variational Autoencoder for Molecular Graph Generation

Functional Transparency for Structured Data: a Game-Theoretic Approach

Towards Robust Interpretability with Self-Explaining Neural Networks

Direct Optimization through $\arg \max$ for Discrete Variational Auto-Encoder

A Game Theoretic Approach to Class-wise Selective Rationalization

Solving graph compression via optimal transport

Generative Models for Graph-Based Protein Design

Tight Certificates of Adversarial Robustness for Randomly Smoothed Classifiers

Understanding Interlocking Dynamics of Cooperative Rationalization

GeoMol: Torsional Geometric Generation of Molecular 3D Conformer Ensembles

Poisson Flow Generative Models

Torsional Diffusion for Molecular Conformer Generation

Compositional Sculpting of Iterative Generative Processes

Compositional Foundation Models for Hierarchical Planning

Restart Sampling for Improving Generative Processes

Learning Population-Level Diffusions with Generative RNNs