19 citations · 61 across the 15 of their papers we have counts for
18 papers
Expansive operators on Fréchet spaces without continuous norm
Antoni López-Martínez, Quentin Menet
We investigate the existence of (metrically) expansive operators on Fréchet spaces without continuous norm. We first solve a problem posed by Bernardes, Caraballo, Darji, Fávaro, a…
Question-Specific Knowledge Graphs for Efficient Visual Reasoning
Ting-Chih Chen, Emile van Krieken, Shujian Yu +1
Recent work in visual question answering has shown that vision-language models can exhibit strong reasoning capabilities by translating visual inputs into textual representations.…
The RAIL Principles for Neurosymbolic AI: Reasoning, Assurances, Interfacing and Learning
Agnese Chiatti, Michael Cochez, Cristina Cornelio +14
Neurosymbolic AI systems that integrate machine learning and symbolic reasoning are rapidly gaining attention. They complement the data-intensive statistical approaches of neural n…
Neurosymbolic Diffusion Models
Emile van Krieken, Pasquale Minervini, Edoardo Ponti +1
Neurosymbolic (NeSy) predictors combine neural perception with symbolic reasoning to solve tasks like visual reasoning. However, standard NeSy predictors assume conditional indepen…
A Neuro-Symbolic Benchmark Suite for Concept Quality and Reasoning Shortcuts
Samuele Bortolotti, Emanuele Marconato, Tommaso Carraro +5
The advent of powerful neural classifiers has increased interest in problems that require both learning and reasoning. These problems are critical for understanding important prope…
Are We Done with MMLU?
Aryo Pradipta Gema, Joshua Ong Jun Leang, Giwon Hong +13
Maybe not. We identify and analyse errors in the popular Massive Multitask Language Understanding (MMLU) benchmark. Even though MMLU is widely adopted, our analysis demonstrates nu…
ULLER: A Unified Language for Learning and Reasoning
Emile van Krieken, Samy Badreddine, Robin Manhaeve +1
The field of neuro-symbolic artificial intelligence (NeSy), which combines learning and reasoning, has recently experienced significant growth. There now are a wide variety of NeSy…
On the Independence Assumption in Neurosymbolic Learning
Emile van Krieken, Pasquale Minervini, Edoardo M. Ponti +1
State-of-the-art neurosymbolic learning systems use probabilistic reasoning to guide neural networks towards predictions that conform to logical constraints over symbols. Many such…
BEARS Make Neuro-Symbolic Models Aware of their Reasoning Shortcuts
Emanuele Marconato, Samuele Bortolotti, Emile van Krieken +3
Neuro-Symbolic (NeSy) predictors that conform to symbolic knowledge - encoding, e.g., safety constraints - can be affected by Reasoning Shortcuts (RSs): They learn concepts consist…
Optimisation in Neurosymbolic Learning Systems
Emile van Krieken
Neurosymbolic AI aims to integrate deep learning with symbolic AI. This integration has many promises, such as decreasing the amount of data required to train a neural network, imp…
GRAPES: Learning to Sample Graphs for Scalable Graph Neural Networks
Taraneh Younesian, Daniel Daza, Emile van Krieken +2
Graph neural networks (GNNs) learn to represent nodes by aggregating information from their neighbors. As GNNs increase in depth, their receptive field grows exponentially, leading…
IntelliGraphs: Datasets for Benchmarking Knowledge Graph Generation
Thiviyan Thanapalasingam, Emile van Krieken, Peter Bloem +1
Knowledge Graph Embedding (KGE) models are used to learn continuous representations of entities and relations. A key task in the literature is predicting missing links between enti…
Prompting as Probing: Using Language Models for Knowledge Base Construction
Dimitrios Alivanistos, Selene Báez Santamaría, Michael Cochez +3
Language Models (LMs) have proven to be useful in various downstream applications, such as summarisation, translation, question answering and text classification. LMs are becoming…
Refining neural network predictions using background knowledge
Alessandro Daniele, Emile van Krieken, Luciano Serafini +1
Recent work has shown logical background knowledge can be used in learning systems to compensate for a lack of labeled training data. Many methods work by creating a loss function…
Storchastic: A Framework for General Stochastic Automatic Differentiation
Emile van Krieken, Jakub M. Tomczak, Annette ten Teije
Modelers use automatic differentiation (AD) of computation graphs to implement complex Deep Learning models without defining gradient computations. Stochastic AD extends AD to stoc…
Analyzing Differentiable Fuzzy Implications
Emile van Krieken, Erman Acar, Frank van Harmelen
Combining symbolic and neural approaches has gained considerable attention in the AI community, as it is often argued that the strengths and weaknesses of these approaches are comp…
Analyzing Differentiable Fuzzy Logic Operators
Emile van Krieken, Erman Acar, Frank van Harmelen
The AI community is increasingly putting its attention towards combining symbolic and neural approaches, as it is often argued that the strengths and weaknesses of these approaches…
Semi-Supervised Learning using Differentiable Reasoning
Emile van Krieken, Erman Acar, Frank van Harmelen
We introduce Differentiable Reasoning (DR), a novel semi-supervised learning technique which uses relational background knowledge to benefit from unlabeled data. We apply it to the…