Search Results for author:
Found 47 papers, 15 papers with code
In Data We Trust: A Critical Analysis of Hate Speech Detection Datasets
Recently, a few studies have discussed the limitations of datasets collected for the task of detecting hate speech from different viewpoints.
Genetic Programming for Explainable Manifold Learning
Our experimental analysis demonstrates that GP-EMaL is able to match the performance of the existing approach in most cases, while using simpler, smaller, and more interpretable tree structures.
Explaining Genetic Programming Trees using Large Language Models
In this research, we investigate the potential of leveraging eXplainable AI (XAI) and large language models (LLMs) like ChatGPT to improve the interpretability of GP-based non-linear dimensionality reduction.
Fast and Efficient Local Search for Genetic Programming Based Loss Function Learning
In this paper, we develop upon the topic of loss function learning, an emergent meta-learning paradigm that aims to learn loss functions that significantly improve the performance of the models trained under them.
Predicting postoperative risks using large language models
Adapting models through self-supervised finetuning further improved performance by 3. 2% for AUROC & 1. 5% for AUPRC Incorporating labels into the finetuning procedure further boosted performances, with semi-supervised finetuning improving by 1. 8% for AUROC & 2% for AUPRC & foundational modelling improving by 3. 6% for AUROC & 2. 6% for AUPRC compared to self-supervised finetuning.
A Consistent Lebesgue Measure for Multi-label Learning
The consistency of surrogate loss functions is not proven and is exacerbated by the conflicting nature of multi-label loss functions.
Improving Buoy Detection with Deep Transfer Learning for Mussel Farm Automation
To establish an effective deep learning model for buoy detection with a limited number of labeled data, we employ transfer learning techniques.
Assisting Clinical Decisions for Scarcely Available Treatment via Disentangled Latent Representation
TVAE is specifically designed to address the modeling challenges like ECMO with strong treatment selection bias and scarce treatment cases.
SR-Forest: A Genetic Programming based Heterogeneous Ensemble Learning Method
Ensemble learning methods have been widely used in machine learning in recent years due to their high predictive performance.
Online Loss Function Learning
Loss function learning is a new meta-learning paradigm that aims to automate the essential task of designing a loss function for a machine learning model.
An Efficient Evolutionary Deep Learning Framework Based on Multi-source Transfer Learning to Evolve Deep Convolutional Neural Networks
In this paper, inspired by transfer learning, a new evolutionary computation based framework is proposed to efficiently evolve CNNs without compromising the classification accuracy.
Explaining Deep Convolutional Neural Networks for Image Classification by Evolving Local Interpretable Model-agnostic Explanations
The evolved local explanations on four images, randomly selected from ImageNet, are presented, which show that the evolved local explanations are straightforward to be recognised by humans.
Self-explaining Hierarchical Model for Intraoperative Time Series
Towards this end, we propose a hierarchical model combining the strength of both attention and recurrent models for intraoperative time series.
Genetic Programming-Based Evolutionary Deep Learning for Data-Efficient Image Classification
The new approach can automatically evolve variable-length models using many important operators from both image and classification domains.
Learning Symbolic Model-Agnostic Loss Functions via Meta-Learning
In this paper, we develop upon the emerging topic of loss function learning, which aims to learn loss functions that significantly improve the performance of the models trained under them.
A Survey on Evolutionary Computation for Computer Vision and Image Analysis: Past, Present, and Future Trends
This survey aims to provide a better understanding of evolutionary computer vision (ECV) by discussing the contributions of different approaches and exploring how and why EC is used for CV and image analysis.
Survey on Evolutionary Deep Learning: Principles, Algorithms, Applications and Open Issues
Specifically, we firstly illuminate EDL from machine learning and EC and regard EDL as an optimization problem.
Impacts of Real Hands on 5G Millimeter-Wave Cellphone Antennas: Measurements and Electromagnetic Models
Their radiation properties are evaluated through near-field scanning of the two prototypes, first in free space for calibration of the antenna measurement system and for building simplified models of the cellphone arrays.
Surgical Prediction with Interpretable Latent Representation
Given the risks and cost of surgeries, there has been significant interest in exploiting predictive models to improve perioperative care.
Genetic Programming for Manifold Learning: Preserving Local Topology
Recently, genetic programming has emerged as a very promising approach to manifold learning by evolving functional mappings from the original space to an embedding.
BenchENAS: A Benchmarking Platform for Evolutionary Neural Architecture Search
The paper conducts efficient comparison experiments on eight ENAS algorithms with high GPU utilization on this platform.
Learning and Sharing: A Multitask Genetic Programming Approach to Image Feature Learning
Therefore, this paper develops a multitask GP approach to image feature learning for classification with limited training data.
Evolving Character-level Convolutional Neural Networks for Text Classification
Researchers have not applied EDL techniques to search the architecture space of char-CNNs for text classification tasks.
A Survey on Evolutionary Neural Architecture Search
Deep Neural Networks (DNNs) have achieved great success in many applications.
Evolving Deep Convolutional Neural Networks for Hyperspectral Image Denoising
Hyperspectral images (HSIs) are susceptible to various noise factors leading to the loss of information, and the noise restricts the subsequent HSIs object detection and classification tasks.
Surrogate-assisted Particle Swarm Optimisation for Evolving Variable-length Transferable Blocks for Image Classification
A new effective and efficient surrogate-assisted particle swarm optimisation algorithm is proposed to automatically evolve convolutional neural networks.
ArcText: A Unified Text Approach to Describing Convolutional Neural Network Architectures
Data mining on existing CNN can discover useful patterns and fundamental sub-comments from their architectures, providing researchers with strong prior knowledge to design proper CNN architectures when they have no expertise in CNNs.
An Adaptive and Near Parameter-free Evolutionary Computation Approach Towards True Automation in AutoML
We apply this to the area of automated machine learning (by extending TPOT), to produce pipelines which can effectively be claimed to be free from human input, and show that the results are competitive with existing state-of-the-art which use hand-selected hyperparameter values.
Genetic Programming for Evolving a Front of Interpretable Models for Data Visualisation
Many domains require an understanding of the data in terms of the original features; there is hence a need for powerful visualisation methods which use understandable models.
Improving generalisation of AutoML systems with dynamic fitness evaluations
A common problem machine learning developers are faced with is overfitting, that is, fitting a pipeline too closely to the training data that the performance degrades for unseen data.
Multi-Objective Genetic Programming for Manifold Learning: Balancing Quality and Dimensionality
This method required the dimensionality of the embedding to be known a priori, which makes it hard to use when little is known about a dataset.
KerGM: Kernelized Graph Matching
Graph matching plays a central role in such fields as computer vision, pattern recognition, and bioinformatics.
Genetic Programming for Evolving Similarity Functions for Clustering: Representations and Analysis
In this paper, we propose a new approach to automatically evolving similarity functions for a given clustering algorithm by using genetic programming.
Genetic Programming and Gradient Descent: A Memetic Approach to Binary Image Classification
Image classification is an essential task in computer vision, which aims to categorise a set of images into different groups based on some visual criteria.
Improving the Results of De novo Peptide Identification via Tandem Mass Spectrometry Using a Genetic Programming-based Scoring Function for Re-ranking Peptide-Spectrum Matches
The GP method along with RF and SVR, each is used for post-processing the results of peptide identification by PEAKS, a commonly used de novo sequencing method.
Particle Swarm Optimisation for Evolving Deep Neural Networks for Image Classification by Evolving and Stacking Transferable Blocks
Deep Convolutional Neural Networks (CNNs) have been widely used in image classification tasks, but the process of designing CNN architectures is very complex, so Neural Architecture Search (NAS), automatically searching for optimal CNN architectures, has attracted more and more research interests.
An Automated Ensemble Learning Framework Using Genetic Programming for Image Classification
An ensemble consists of multiple learners and can achieve a better generalisation performance than a single learner.
Evolving Deep Neural Networks by Multi-objective Particle Swarm Optimization for Image Classification
In recent years, convolutional neural networks (CNNs) have become deeper in order to achieve better classification accuracy in image classification.
A Hybrid GA-PSO Method for Evolving Architecture and Short Connections of Deep Convolutional Neural Networks
Three major contributions of this work are: Firstly, a new encoding strategy is proposed to encode a CNN, where the architecture and the shortcut connections are encoded separately; Secondly, a hybrid two-level EC method, which combines particle swarm optimisation and genetic algorithms, is developed to search for the optimal CNNs; Lastly, an adjustable learning rate is introduced for the fitness evaluations, which provides a better learning rate for the training process given a fixed number of epochs.
Can Genetic Programming Do Manifold Learning Too?
Exploratory data analysis is a fundamental aspect of knowledge discovery that aims to find the main characteristics of a dataset.
Automatically Evolving CNN Architectures Based on Blocks
The proposed algorithm is evaluated on CIFAR10 and CIFAR100 against 18 state-of-the-art peer competitors.
A Hybrid Differential Evolution Approach to Designing Deep Convolutional Neural Networks for Image Classification
In this paper, a new hybrid differential evolution (DE) algorithm with a newly added crossover operator is proposed to evolve the architectures of CNNs of any lengths, which is named DECNN.
Automatically designing CNN architectures using genetic algorithm for image classification
Convolutional Neural Networks (CNNs) have gained a remarkable success on many image classification tasks in recent years.
Evolving Deep Convolutional Neural Networks by Variable-length Particle Swarm Optimization for Image Classification
Convolutional neural networks (CNNs) are one of the most effective deep learning methods to solve image classification problems, but the best architecture of a CNN to solve a specific problem can be extremely complicated and hard to design.
Generating Redundant Features with Unsupervised Multi-Tree Genetic Programming
Recently, feature selection has become an increasingly important area of research due to the surge in high-dimensional datasets in all areas of modern life.
A Particle Swarm Optimization-based Flexible Convolutional Auto-Encoder for Image Classification
Convolutional auto-encoders have shown their remarkable performance in stacking to deep convolutional neural networks for classifying image data during past several years.
Evolving Deep Convolutional Neural Networks for Image Classification
Evolutionary computation methods have been successfully applied to neural networks since two decades ago, while those methods cannot scale well to the modern deep neural networks due to the complicated architectures and large quantities of connection weights.
