Search Results for author: Noel O'Connor

Found 18 papers, 10 papers with code

Dataset Clustering for Improved Offline Policy Learning

1 code implementation14 Feb 2024 Qiang Wang, Yixin Deng, Francisco Roldan Sanchez, Keru Wang, Kevin McGuinness, Noel O'Connor, Stephen J. Redmond

Offline policy learning aims to discover decision-making policies from previously-collected datasets without additional online interactions with the environment.

Clustering Continuous Control +2

Privacy-Aware Energy Consumption Modeling of Connected Battery Electric Vehicles using Federated Learning

1 code implementation12 Dec 2023 Sen Yan, Hongyuan Fang, Ji Li, Tomas Ward, Noel O'Connor, Mingming Liu

Our findings show that FL methods can effectively improve the performance of BEV energy consumption prediction while maintaining user privacy.

Federated Learning

Learning Saliency From Fixations

no code implementations23 Nov 2023 Yasser Abdelaziz Dahou Djilali, Kevin McGuiness, Noel O'Connor

We present a novel approach for saliency prediction in images, leveraging parallel decoding in transformers to learn saliency solely from fixation maps.

Decoder Saliency Prediction

Learning and reusing primitive behaviours to improve Hindsight Experience Replay sample efficiency

1 code implementation3 Oct 2023 Francisco Roldan Sanchez, Qiang Wang, David Cordova Bulens, Kevin McGuinness, Stephen Redmond, Noel O'Connor

Hindsight Experience Replay (HER) is a technique used in reinforcement learning (RL) that has proven to be very efficient for training off-policy RL-based agents to solve goal-based robotic manipulation tasks using sparse rewards.

Reinforcement Learning (RL)

A Review on AI Algorithms for Energy Management in E-Mobility Services

no code implementations26 Sep 2023 Sen Yan, Maqsood Hussain Shah, Ji Li, Noel O'Connor, Mingming Liu

E-mobility, or electric mobility, has emerged as a pivotal solution to address pressing environmental and sustainability concerns in the transportation sector.

energy management Management

BaseTransformers: Attention over base data-points for One Shot Learning

1 code implementation5 Oct 2022 Mayug Maniparambil, Kevin McGuinness, Noel O'Connor

In this paper we propose to make use of the well-trained feature representations of the base dataset that are closest to each support instance to improve its representation during meta-test time.

Few-Shot Image Classification One-Shot Learning

Dexterous Robotic Manipulation using Deep Reinforcement Learning and Knowledge Transfer for Complex Sparse Reward-based Tasks

1 code implementation19 May 2022 Qiang Wang, Francisco Roldan Sanchez, Robert McCarthy, David Cordova Bulens, Kevin McGuinness, Noel O'Connor, Manuel Wüthrich, Felix Widmaier, Stefan Bauer, Stephen J. Redmond

Here we extend this method, by modifying the task of Phase 1 of the RRC to require the robot to maintain the cube in a particular orientation, while the cube is moved along the required positional trajectory.

Transfer Learning

Semi-supervised dry herbage mass estimation using automatic data and synthetic images

no code implementations26 Oct 2021 Paul Albert, Mohamed Saadeldin, Badri Narayanan, Brian Mac Namee, Deirdre Hennessy, Aisling O'Connor, Noel O'Connor, Kevin McGuinness

Deep learning for computer vision is a powerful tool in this context as it can accurately estimate the dry biomass of a herbage parcel using images of the grass canopy taken using a portable device.

Semantic Segmentation Synthetic Data Generation

Addressing out-of-distribution label noise in webly-labelled data

no code implementations26 Oct 2021 Paul Albert, Diego Ortego, Eric Arazo, Noel O'Connor, Kevin McGuinness

We propose a simple solution to bridge the gap with a fully clean dataset using Dynamic Softening of Out-of-distribution Samples (DSOS), which we design on corrupted versions of the CIFAR-100 dataset, and compare against state-of-the-art algorithms on the web noise perturbated MiniImageNet and Stanford datasets and on real label noise datasets: WebVision 1. 0 and Clothing1M.

Image Classification

Solving the Real Robot Challenge using Deep Reinforcement Learning

2 code implementations30 Sep 2021 Robert McCarthy, Francisco Roldan Sanchez, Qiang Wang, David Cordova Bulens, Kevin McGuinness, Noel O'Connor, Stephen J. Redmond

This paper details our winning submission to Phase 1 of the 2021 Real Robot Challenge; a challenge in which a three-fingered robot must carry a cube along specified goal trajectories.

reinforcement-learning Reinforcement Learning (RL) +1

Attention-based Stylisation for Exemplar Image Colourisation

1 code implementation4 May 2021 Marc Gorriz Blanch, Issa Khalifeh, Alan Smeaton, Noel O'Connor, Marta Mrak

Stylised outputs are then obtained by computing similarities between both feature representations in order to transfer the style of the reference to the content of the target input.

Style Transfer

The Importance of Importance Sampling for Deep Budgeted Training

no code implementations1 Jan 2021 Eric Arazo, Diego Ortego, Paul Albert, Noel O'Connor, Kevin McGuinness

For example, training in CIFAR-10/100 with 30% of the full training budget, a uniform sampling strategy with certain data augmentation surpasses the performance of 100% budget models trained with standard data augmentation.

Data Augmentation

Temporal Bilinear Encoding Network of Audio-Visual Features at Low Sampling Rates

no code implementations18 Dec 2020 Feiyan Hu, Eva Mohedano, Noel O'Connor, Kevin McGuinness

Current deep learning based video classification architectures are typically trained end-to-end on large volumes of data and require extensive computational resources.

Classification General Classification +1

ATSal: An Attention Based Architecture for Saliency Prediction in 360 Videos

1 code implementation20 Nov 2020 Yasser Dahou, Marouane Tliba, Kevin McGuinness, Noel O'Connor

The spherical domain representation of 360 video/image presents many challenges related to the storage, processing, transmission and rendering of omnidirectional videos (ODV).

Saliency Prediction

Shallow and Deep Convolutional Networks for Saliency Prediction

1 code implementation CVPR 2016 Junting Pan, Kevin McGuinness, Elisa Sayrol, Noel O'Connor, Xavier Giro-i-Nieto

The prediction of salient areas in images has been traditionally addressed with hand-crafted features based on neuroscience principles.

Saliency Prediction

Exploring EEG for Object Detection and Retrieval

no code implementations9 Apr 2015 Eva Mohedano, Amaia Salvador, Sergi Porta, Xavier Giró-i-Nieto, Graham Healy, Kevin McGuinness, Noel O'Connor, Alan F. Smeaton

We show that it is indeed possible to detect such objects in complex images and, also, that users with previous knowledge on the dataset or experience with the RSVP outperform others.

Content-Based Image Retrieval EEG +4

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