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Found 22 papers, 1 papers with code
Would You Trust an AI Doctor? Building Reliable Medical Predictions with Kernel Dropout Uncertainty
The growing capabilities of AI raise questions about their trustworthiness in healthcare, particularly due to opaque decision-making and limited data availability.
BayesJudge: Bayesian Kernel Language Modelling with Confidence Uncertainty in Legal Judgment Prediction
Predicting legal judgments with reliable confidence is paramount for responsible legal AI applications.
IDoFew: Intermediate Training Using Dual-Clustering in Language Models for Few Labels Text Classification
Although some approaches have attempted to address this problem through single-stage clustering as an intermediate training step coupled with a pre-trained language model, which generates pseudo-labels to improve classification, these methods are often error-prone due to the limitations of the clustering algorithms.
Topics in Contextualised Attention Embeddings
Contextualised word vectors obtained via pre-trained language models encode a variety of knowledge that has already been exploited in applications.
CLUE: Contextualised Unified Explainable Learning of User Engagement in Video Lectures
As a result, there has been a steep rise in developing computational methods to predict a user engagement score that is indicative of some form of possible user engagement, i. e., to what level a user would tend to engage with the content.
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.
Emojional: Emoji Embeddings
For example, the use of the clown emoji Open image in a new window to signify someone is making a fool of themself, or the collective spamming of the snake emoji Open image in a new window to “cancel” someone, both show seemingly innocent emojis being used as clear forms of aggression online.
Cultural Vocal Bursts Intensity Prediction
Sentiment Analysis
DepressionNet: A Novel Summarization Boosted Deep Framework for Depression Detection on Social Media
Twitter is currently a popular online social media platform which allows users to share their user-generated content.
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.
Dynamic Topic Tracker for KB-to-Text Generation
Recently, many KB-to-text generation tasks have been proposed to bridge the gap between knowledge bases and natural language by directly converting a group of knowledge base triples into human-readable sentences.
Explainable Depression Detection with Multi-Modalities Using a Hybrid Deep Learning Model on Social Media
Model interpretability has become important to engenders appropriate user trust by providing the insight into the model prediction.
Word and Document Embedding with vMF-Mixture Priors on Context Word Vectors
To this end, our model relies on the assumption that context word vectors are drawn from a mixture of von Mises-Fisher (vMF) distributions, where the parameters of this mixture distribution are jointly optimized with the word vectors.
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.
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.
Modeling Context Words as Regions: An Ordinal Regression Approach to Word Embedding
Although region representations of word meaning offer a natural alternative to word vectors, only few methods have been proposed that can effectively learn word regions.
Jointly Learning Word Embeddings and Latent Topics
Word embedding models such as Skip-gram learn a vector-space representation for each word, based on the local word collocation patterns that are observed in a text corpus.
D-GloVe: A Feasible Least Squares Model for Estimating Word Embedding Densities
We propose a new word embedding model, inspired by GloVe, which is formulated as a feasible least squares optimization problem.
Entity Embeddings with Conceptual Subspaces as a Basis for Plausible Reasoning
Conceptual spaces are geometric representations of conceptual knowledge, in which entities correspond to points, natural properties correspond to convex regions, and the dimensions of the space correspond to salient features.
