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Found 8 papers, 4 papers with code
Large scale paired antibody language models
Antibodies are proteins produced by the immune system that can identify and neutralise a wide variety of antigens with high specificity and affinity, and constitute the most successful class of biotherapeutics.
Inverse folding for antibody sequence design using deep learning
We consider the problem of antibody sequence design given 3D structural information.
Multilingual End to End Entity Linking
Entity Linking is one of the most common Natural Language Processing tasks in practical applications, but so far efficient end-to-end solutions with multilingual coverage have been lacking, leading to complex model stacks.
Polar Ducks and Where to Find Them: Enhancing Entity Linking with Duck Typing and Polar Box Embeddings
Inspired by duck typing in programming languages, we propose to define the type of an entity based on the relations that it has with other entities in a knowledge graph.
Leveraging universality of jet taggers through transfer learning
A significant challenge in the tagging of boosted objects via machine-learning technology is the prohibitive computational cost associated with training sophisticated models.
Jet tagging in the Lund plane with graph networks
The identification of boosted heavy particles such as top quarks or vector bosons is one of the key problems arising in experimental studies at the Large Hadron Collider.
Lund jet images from generative and cycle-consistent adversarial networks
We introduce a generative model to simulate radiation patterns within a jet using the Lund jet plane.
Jet grooming through reinforcement learning
We introduce a novel implementation of a reinforcement learning (RL) algorithm which is designed to find an optimal jet grooming strategy, a critical tool for collider experiments.
