activity
20162024
most citedLearning from learning machines: a new generation of AI technology to meet the needs of science

3 citations · 4 across the 8 of their papers we have counts for

collaborators

8 papers

q-bio.BM2024

Challenges and Guidelines in Deep Generative Protein Design: Four Case Studies

Tianyuan Zheng, Alessandro Rondina, Gos Micklem +1

Deep generative models show promise for protein design, yet reliably producing designs that are geometrically plausible, evolutionarily consistent, functionally…

cs.LG2024

HyperBERT: Mixing Hypergraph-Aware Layers with Language Models for Node Classification on Text-Attributed Hypergraphs

Adrián Bazaga, Pietro Liò, Gos Micklem

Hypergraphs are characterized by complex topological structure, representing higher-order interactions among multiple entities through hyperedges. Lately, hypergraph-based deep lea…

cs.CL2023★ 1 cited

Language Model Knowledge Distillation for Efficient Question Answering in Spanish

Adrián Bazaga, Pietro Liò, Gos Micklem

Recent advances in the development of pre-trained Spanish language models has led to significant progress in many Natural Language Processing (NLP) tasks, such as question answerin…

cs.CL2023

SQLformer: Deep Auto-Regressive Query Graph Generation for Text-to-SQL Translation

Adrián Bazaga, Pietro Liò, Gos Micklem

In recent years, the task of text-to-SQL translation, which converts natural language questions into executable SQL queries, has gained significant attention for its potential to d…

cs.CL2023

Unsupervised Pretraining for Fact Verification by Language Model Distillation

Adrián Bazaga, Pietro Liò, Gos Micklem

Fact verification aims to verify a claim using evidence from a trustworthy knowledge base. To address this challenge, algorithms must produce features for every claim that are both…

cs.LG2021★ 3 cited

Learning from learning machines: a new generation of AI technology to meet the needs of science

Luca Pion-Tonachini, Kristofer Bouchard, Hector Garcia Martin +33

We outline emerging opportunities and challenges to enhance the utility of AI for scientific discovery. The distinct goals of AI for industry versus the goals of AI for science cre…

cs.CL2021

Translating synthetic natural language to database queries: a polyglot deep learning framework

Adrián Bazaga, Nupur Gunwant, Gos Micklem

The number of databases as well as their size and complexity is increasing. This creates a barrier to use especially for non-experts, who have to come to grips with the nature of t…

q-bio.QM2016

Understanding the Systems Biology of Pathogen Virulence Using Semantic Methodologies

David Rhee, Kevin Shieh, Julie Sullivan +3

Systems biology approaches to the integrative study of cells, organs and organisms offer the best means of understanding in a holistic manner the diversity of molecular assays that…