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Found 7 papers, 5 papers with code
Flow Matching for Collaborative Filtering
Generative models have shown great promise in collaborative filtering by capturing the underlying distribution of user interests and preferences.
CaLMFlow: Volterra Flow Matching using Causal Language Models
CaLMFlow enables the direct application of LLMs to learn complex flows by formulating flow matching as a sequence modeling task, bridging discrete language modeling and continuous generative modeling.
A Survey on Diffusion Models for Recommender Systems
While traditional recommendation techniques have made significant strides in the past decades, they still suffer from limited generalization performance caused by factors like inadequate collaborative signals, weak latent representations, and noisy data.
DiffPack: A Torsional Diffusion Model for Autoregressive Protein Side-Chain Packing
In this work, we present DiffPack, a torsional diffusion model that learns the joint distribution of side-chain torsional angles, the only degrees of freedom in side-chain packing, by diffusing and denoising on the torsional space.
E3Bind: An End-to-End Equivariant Network for Protein-Ligand Docking
In silico prediction of the ligand binding pose to a given protein target is a crucial but challenging task in drug discovery.
PEER: A Comprehensive and Multi-Task Benchmark for Protein Sequence Understanding
However, there is a lack of a standard benchmark to evaluate the performance of different methods, which hinders the progress of deep learning in this field.
TorchDrug: A Powerful and Flexible Machine Learning Platform for Drug Discovery
However, lacking domain knowledge (e. g., which tasks to work on), standard benchmarks and data preprocessing pipelines are the main obstacles for machine learning researchers to work in this domain.
