Pasquale Minervini

research

∙ 09/16/2023

Temporal Smoothness Regularisers for Neural Link Predictors

Most algorithms for representation learning and link prediction on relat...

0 Manuel Dileo, et al. ∙

research

∙ 08/12/2023

Approximate Answering of Graph Queries

Knowledge graphs (KGs) are inherently incomplete because of incomplete w...

0 Michael Cochez, et al. ∙

research

∙ 07/12/2023

No Train No Gain: Revisiting Efficient Training Algorithms For Transformer-based Language Models

The computation necessary for training Transformer-based language models...

0 Jean Kaddour, et al. ∙

research

∙ 07/06/2023

Parameter-Efficient Fine-Tuning of LLaMA for the Clinical Domain

Adapting pretrained language models to novel domains, such as clinical a...

0 Aryo Pradipta Gema, et al. ∙

research

∙ 05/31/2023

Knowledge Graph Embeddings in the Biomedical Domain: Are They Useful? A Look at Link Prediction, Rule Learning, and Downstream Polypharmacy Tasks

Knowledge graphs are powerful tools for representing and organising comp...

0 Aryo Pradipta Gema, et al. ∙

research

∙ 05/22/2023

SPARSEFIT: Few-shot Prompting with Sparse Fine-tuning for Jointly Generating Predictions and Natural Language Explanations

Explaining the decisions of neural models is crucial for ensuring their ...

0 Jesus Solano, et al. ∙

research

∙ 05/22/2023

Logical Reasoning for Natural Language Inference Using Generated Facts as Atoms

State-of-the-art neural models can now reach human performance levels ac...

0 Joe Stacey, et al. ∙

research

∙ 01/29/2023

Adapting Neural Link Predictors for Complex Query Answering

Answering complex queries on incomplete knowledge graphs is a challengin...

0 Erik Arakelyan, et al. ∙

research

∙ 11/17/2022

Machine Learning-Assisted Recurrence Prediction for Early-Stage Non-Small-Cell Lung Cancer Patients

Background: Stratifying cancer patients according to risk of relapse can...

0 Adrianna Janik, et al. ∙

research

∙ 10/30/2022

An Efficient Memory-Augmented Transformer for Knowledge-Intensive NLP Tasks

Access to external knowledge is essential for many natural language proc...

0 Yuxiang WU, et al. ∙

research

∙ 10/27/2022

Learning Discrete Directed Acyclic Graphs via Backpropagation

Recently continuous relaxations have been proposed in order to learn Dir...

0 Andrew J. Wren, et al. ∙

research

∙ 09/11/2022

Adaptive Perturbation-Based Gradient Estimation for Discrete Latent Variable Models

The integration of discrete algorithmic components in deep learning arch...

0 Pasquale Minervini, et al. ∙

research

∙ 07/20/2022

ReFactorGNNs: Revisiting Factorisation-based Models from a Message-Passing Perspective

Factorisation-based Models (FMs), such as DistMult, have enjoyed endurin...

4 Yihong Chen, et al. ∙

research

∙ 05/23/2022

Logical Reasoning with Span Predictions: Span-level Logical Atoms for Interpretable and Robust NLI Models

Current Natural Language Inference (NLI) models achieve impressive resul...

12 Joe Stacey, et al. ∙

research

∙ 04/12/2022

XQA-DST: Multi-Domain and Multi-Lingual Dialogue State Tracking

In a task-oriented dialogue system, Dialogue State Tracking (DST) keeps ...

0 Han Zhou, et al. ∙

research

∙ 04/10/2022

MedDistant19: A Challenging Benchmark for Distantly Supervised Biomedical Relation Extraction

Relation Extraction in the biomedical domain is challenging due to the l...

10 Saadullah Amin, et al. ∙

research

∙ 03/20/2022

Differentiable Reasoning over Long Stories – Assessing Systematic Generalisation in Neural Models

Contemporary neural networks have achieved a series of developments and ...

0 Wanshui Li, et al. ∙

research

∙ 10/25/2021

A Probabilistic Framework for Knowledge Graph Data Augmentation

We present NNMFAug, a probabilistic framework to perform data augmentati...

11 Jatin Chauhan, et al. ∙

research

∙ 10/06/2021

Relation Prediction as an Auxiliary Training Objective for Improving Multi-Relational Graph Representations

Learning good representations on multi-relational graphs is essential to...

6 Yihong Chen, et al. ∙

research

∙ 07/05/2021

Training Adaptive Computation for Open-Domain Question Answering with Computational Constraints

Adaptive Computation (AC) has been shown to be effective in improving th...

0 Yuxiang WU, et al. ∙

research

∙ 06/03/2021

Implicit MLE: Backpropagating Through Discrete Exponential Family Distributions

Integrating discrete probability distributions and combinatorial optimiz...

0 Mathias Niepert, et al. ∙

research

∙ 02/13/2021

PAQ: 65 Million Probably-Asked Questions and What You Can Do With Them

Open-domain Question Answering models which directly leverage question-a...

0 Patrick Lewis, et al. ∙

research

∙ 02/08/2021

Grid-to-Graph: Flexible Spatial Relational Inductive Biases for Reinforcement Learning

Although reinforcement learning has been successfully applied in many do...

0 Zhengyao Jiang, et al. ∙

research

∙ 01/24/2021

Stereotype and Skew: Quantifying Gender Bias in Pre-trained and Fine-tuned Language Models

This paper proposes two intuitive metrics, skew and stereotype, that qua...

0 Daniel de Vassimon Manela, et al. ∙

research

∙ 01/01/2021

NeurIPS 2020 EfficientQA Competition: Systems, Analyses and Lessons Learned

We review the EfficientQA competition from NeurIPS 2020. The competition...

15 Sewon Min, et al. ∙

research

∙ 11/10/2020

Don't Read Too Much into It: Adaptive Computation for Open-Domain Question Answering

Most approaches to Open-Domain Question Answering consist of a light-wei...

1 Yuxiang WU, et al. ∙

research

∙ 11/06/2020

Complex Query Answering with Neural Link Predictors

Neural link predictors are immensely useful for identifying missing edge...

31 Erik Arakelyan, et al. ∙

research

∙ 07/17/2020

WordCraft: An Environment for Benchmarking Commonsense Agents

The ability to quickly solve a wide range of real-world tasks requires a...

17 Minqi Jiang, et al. ∙

research

∙ 07/13/2020

Learning Reasoning Strategies in End-to-End Differentiable Proving

Attempts to render deep learning models interpretable, data-efficient, a...

42 Pasquale Minervini, et al. ∙

research

∙ 04/30/2020

Knowledge Graph Embeddings and Explainable AI

Knowledge graph embeddings are now a widely adopted approach to knowledg...

52 Federico Bianchi, et al. ∙

research

∙ 04/16/2020

There is Strength in Numbers: Avoiding the Hypothesis-Only Bias in Natural Language Inference via Ensemble Adversarial Training

Natural Language Inference (NLI) datasets contain annotation artefacts r...

8 Joe Stacey, et al. ∙

research

∙ 02/15/2020

Undersensitivity in Neural Reading Comprehension

Current reading comprehension models generalise well to in-distribution ...

0 Johannes Welbl, et al. ∙

research

∙ 12/17/2019

Differentiable Reasoning on Large Knowledge Bases and Natural Language

Reasoning with knowledge expressed in natural language and Knowledge Bas...

1 Pasquale Minervini, et al. ∙

research

∙ 06/14/2019

NLProlog: Reasoning with Weak Unification for Question Answering in Natural Language

Rule-based models are attractive for various tasks because they inherent...

0 Leon Weber, et al. ∙

research

∙ 06/12/2019

Neural Variational Inference For Estimating Uncertainty in Knowledge Graph Embeddings

Recent advances in Neural Variational Inference allowed for a renaissanc...

2 Alexander I. Cowen-Rivers, et al. ∙

research

∙ 12/16/2018

Embedding Cardinality Constraints in Neural Link Predictors

Neural link predictors learn distributed representations of entities and...

0 Emir Muñoz, et al. ∙

research

∙ 08/26/2018

Adversarially Regularising Neural NLI Models to Integrate Logical Background Knowledge

Adversarial examples are inputs to machine learning models designed to c...

0 Pasquale Minervini, et al. ∙

research

∙ 07/21/2018

Towards Neural Theorem Proving at Scale

Neural models combining representation learning and reasoning in an end-...

0 Pasquale Minervini, et al. ∙

research

∙ 06/20/2018

Jack the Reader - A Machine Reading Framework

Many Machine Reading and Natural Language Understanding tasks require re...

0 Dirk Weissenborn, et al. ∙

research

∙ 05/17/2018

Extrapolation in NLP

We argue that extrapolation to examples outside the training space will ...

0 Jeff Mitchell, et al. ∙

research

∙ 07/24/2017

Adversarial Sets for Regularising Neural Link Predictors

In adversarial training, a set of models learn together by pursuing comp...

0 Pasquale Minervini, et al. ∙

Pasquale Minervini

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