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Found 20 papers, 16 papers with code
EquiBind: Geometric Deep Learning for Drug Binding Structure Prediction
Predicting how a drug-like molecule binds to a specific protein target is a core problem in drug discovery.
Ranked #6 on
Blind Docking
on PDBBind
Independent SE(3)-Equivariant Models for End-to-End Rigid Protein Docking
Protein complex formation is a central problem in biology, being involved in most of the cell's processes, and essential for applications, e. g. drug design or protein engineering.
Crystal Diffusion Variational Autoencoder for Periodic Material Generation
Generating the periodic structure of stable materials is a long-standing challenge for the material design community.
GeoMol: Torsional Geometric Generation of Molecular 3D Conformer Ensembles
Prediction of a molecule's 3D conformer ensemble from the molecular graph holds a key role in areas of cheminformatics and drug discovery.
Message Passing Networks for Molecules with Tetrahedral Chirality
Molecules with identical graph connectivity can exhibit different physical and biological properties if they exhibit stereochemistry-a spatial structural characteristic.
Optimal Transport Graph Neural Networks
Current graph neural network (GNN) architectures naively average or sum node embeddings into an aggregated graph representation -- potentially losing structural or semantic information.
Ranked #1 on
Graph Regression
on Lipophilicity
(using extra training data)
Computationally Tractable Riemannian Manifolds for Graph Embeddings
Representing graphs as sets of node embeddings in certain curved Riemannian manifolds has recently gained momentum in machine learning due to their desirable geometric inductive biases, e. g., hierarchical structures benefit from hyperbolic geometry.
Mixed-curvature Variational Autoencoders
Euclidean geometry has historically been the typical "workhorse" for machine learning applications due to its power and simplicity.
Constant Curvature Graph Convolutional Networks
Interest has been rising lately towards methods representing data in non-Euclidean spaces, e. g. hyperbolic or spherical, that provide specific inductive biases useful for certain real-world data properties, e. g. scale-free, hierarchical or cyclical.
Noise Contrastive Variational Autoencoders
We take steps towards understanding the "posterior collapse (PC)" difficulty in variational autoencoders (VAEs),~i. e.
Breaking the Softmax Bottleneck via Learnable Monotonic Pointwise Non-linearities
The Softmax function on top of a final linear layer is the de facto method to output probability distributions in neural networks.
Riemannian Adaptive Optimization Methods
Several first order stochastic optimization methods commonly used in the Euclidean domain such as stochastic gradient descent (SGD), accelerated gradient descent or variance reduced methods have already been adapted to certain Riemannian settings.
Learning and Evaluating Sparse Interpretable Sentence Embeddings
Previous research on word embeddings has shown that sparse representations, which can be either learned on top of existing dense embeddings or obtained through model constraints during training time, have the benefit of increased interpretability properties: to some degree, each dimension can be understood by a human and associated with a recognizable feature in the data.
End-to-End Neural Entity Linking
Entity Linking (EL) is an essential task for semantic text understanding and information extraction.
Ranked #1 on
Entity Linking
on OKE-2015
Hyperbolic Neural Networks
However, the representational power of hyperbolic geometry is not yet on par with Euclidean geometry, mostly because of the absence of corresponding hyperbolic neural network layers.
Hyperbolic Entailment Cones for Learning Hierarchical Embeddings
Learning graph representations via low-dimensional embeddings that preserve relevant network properties is an important class of problems in machine learning.
Ranked #1 on
Link Prediction
on WordNet
Deep Joint Entity Disambiguation with Local Neural Attention
We propose a novel deep learning model for joint document-level entity disambiguation, which leverages learned neural representations.
Ranked #4 on
Entity Disambiguation
on WNED-CWEB
Neural Multi-Step Reasoning for Question Answering on Semi-Structured Tables
Second, paraphrases of logical forms and questions are embedded in a jointly learned vector space using word and character convolutional neural networks.
Probabilistic Bag-Of-Hyperlinks Model for Entity Linking
We demonstrate the accuracy of our approach on a wide range of benchmark datasets, showing that it matches, and in many cases outperforms, existing state-of-the-art methods.
