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Found 31 papers, 7 papers with code
Sentence Selection Strategies for Distilling Word Embeddings from BERT
Many applications crucially rely on the availability of high-quality word vectors.
Enhancing DR Classification with Swin Transformer and Shifted Window Attention
Diabetic retinopathy (DR) is a leading cause of blindness worldwide, underscoring the importance of early detection for effective treatment.
Grounding Agent Reasoning in Image Schemas: A Neurosymbolic Approach to Embodied Cognition
Despite advances in embodied AI, agent reasoning systems still struggle to capture the fundamental conceptual structures that humans naturally use to understand and interact with their environment.
Modelling Multi-modal Cross-interaction for ML-FSIC Based on Local Feature Selection
A key feature of the multi-label setting is that images often have several labels, which typically refer to objects appearing in different regions of the image.
REFINE-LM: Mitigating Language Model Stereotypes via Reinforcement Learning
With the introduction of (large) language models, there has been significant concern about the unintended bias such models may inherit from their training data.
Modelling Commonsense Commonalities with Multi-Facet Concept Embeddings
We show that this leads to embeddings which capture a more diverse range of commonsense properties, and consistently improves results in downstream tasks such as ultra-fine entity typing and ontology completion.
Ontology Completion with Natural Language Inference and Concept Embeddings: An Analysis
One line of work treats this task as a Natural Language Inference (NLI) problem, thus relying on the knowledge captured by language models to identify the missing knowledge.
Vector Field Oriented Diffusion Model for Crystal Material Generation
Discovering crystal structures with specific chemical properties has become an increasingly important focus in material science.
What do Deck Chairs and Sun Hats Have in Common? Uncovering Shared Properties in Large Concept Vocabularies
We show that by augmenting the label set with shared properties, we can improve the performance of the state-of-the-art models for this task.
Unified Model for Crystalline Material Generation
One of the greatest challenges facing our society is the discovery of new innovative crystal materials with specific properties.
Optimized Crystallographic Graph Generation for Material Science
With the aim of training graph-based generative models of new material discovery, we propose an efficient tool to generate cutoff graphs and k-nearest-neighbours graphs of periodic structures within GPU optimization.
Ultra-Fine Entity Typing with Prior Knowledge about Labels: A Simple Clustering Based Strategy
In this paper, we show that the performance of existing methods can be improved using a simple technique: we use pre-trained label embeddings to cluster the labels into semantic domains and then treat these domains as additional types.
Distilling Semantic Concept Embeddings from Contrastively Fine-Tuned Language Models
Second, concept embeddings should capture the semantic properties of concepts, whereas contextualised word vectors are also affected by other factors.
Equivariant Message Passing Neural Network for Crystal Material Discovery
Automatic material discovery with desired properties is a fundamental challenge for material sciences.
Region-Based Merging of Open-Domain Terminological Knowledge
This paper introduces a novel method for merging open-domain terminological knowledge.
Inferring Prototypes for Multi-Label Few-Shot Image Classification with Word Vector Guided Attention
A key feature of the multi-label setting is that images often have multiple labels, which typically refer to different regions of the image.
Deriving Word Vectors from Contextualized Language Models using Topic-Aware Mention Selection
Second, rather than learning a word vector directly, we use a topic model to partition the contexts in which words appear, and then learn different topic-specific vectors for each word.
Aligning Visual Prototypes with BERT Embeddings for Few-Shot Learning
While the use of class names has already been explored in previous work, our approach differs in two key aspects.
Few-shot Image Classification with Multi-Facet Prototypes
The aim of few-shot learning (FSL) is to learn how to recognize image categories from a small number of training examples.
Modelling General Properties of Nouns by Selectively Averaging Contextualised Embeddings
While the success of pre-trained language models has largely eliminated the need for high-quality static word vectors in many NLP applications, such vectors continue to play an important role in tasks where words need to be modelled in the absence of linguistic context.
A Mixture-of-Experts Model for Learning Multi-Facet Entity Embeddings
This is exploited by an associated gating network, which uses pre-trained word vectors to encourage the properties that are modelled by a given embedding to be semantically coherent, i. e. to encourage each of the individual embeddings to capture a meaningful facet.
From Shallow to Deep Interactions Between Knowledge Representation, Reasoning and Machine Learning (Kay R. Amel group)
Some common concerns are identified and discussed such as the types of used representation, the roles of knowledge and data, the lack or the excess of information, or the need for explanations and causal understanding.
Modelling Semantic Categories using Conceptual Neighborhood
Unfortunately, meaningful regions can be difficult to estimate, especially since we often have few examples of individuals that belong to a given category.
Inducing Relational Knowledge from BERT
Starting from a few seed instances of a given relation, we first use a large text corpus to find sentences that are likely to express this relation.
Learning Conceptual Spaces with Disentangled Facets
To address this gap, we analyze how, and to what extent, a given vector space embedding can be decomposed into meaningful facets in an unsupervised fashion.
Relation Induction in Word Embeddings Revisited
Given a set of instances of some relation, the relation induction task is to predict which other word pairs are likely to be related in the same way.
Unsupervised Learning of Distributional Relation Vectors
Word embedding models such as GloVe rely on co-occurrence statistics to learn vector representations of word meaning.
Learning Conceptual Space Representations of Interrelated Concepts
Several recently proposed methods aim to learn conceptual space representations from large text collections.
Modeling Semantic Relatedness using Global Relation Vectors
Word embedding models such as GloVe rely on co-occurrence statistics from a large corpus to learn vector representations of word meaning.
Probabilistic Relation Induction in Vector Space Embeddings
Word embeddings have been found to capture a surprisingly rich amount of syntactic and semantic knowledge.
A General Modifier-based Framework for Inconsistency-Tolerant Query Answering
We propose a general framework for inconsistency-tolerant query answering within existential rule setting.
