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Found 18 papers, 10 papers with code
Dataset Clustering for Improved Offline Policy Learning
Offline policy learning aims to discover decision-making policies from previously-collected datasets without additional online interactions with the environment.
Privacy-Aware Energy Consumption Modeling of Connected Battery Electric Vehicles using Federated Learning
Our findings show that FL methods can effectively improve the performance of BEV energy consumption prediction while maintaining user privacy.
Learning Saliency From Fixations
We present a novel approach for saliency prediction in images, leveraging parallel decoding in transformers to learn saliency solely from fixation maps.
Learning and reusing primitive behaviours to improve Hindsight Experience Replay sample efficiency
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.
A Review on AI Algorithms for Energy Management in E-Mobility Services
E-mobility, or electric mobility, has emerged as a pivotal solution to address pressing environmental and sustainability concerns in the transportation sector.
An Ensemble Deep Learning Approach for COVID-19 Severity Prediction Using Chest CT Scans
Chest X-rays have been widely used for COVID-19 screening; however, 3D computed tomography (CT) is a more effective modality.
BaseTransformers: Attention over base data-points for One Shot Learning
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.
Dexterous Robotic Manipulation using Deep Reinforcement Learning and Knowledge Transfer for Complex Sparse Reward-based Tasks
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.
Addressing out-of-distribution label noise in webly-labelled data
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.
Semi-supervised dry herbage mass estimation using automatic data and synthetic images
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.
Solving the Real Robot Challenge using Deep Reinforcement Learning
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.
Attention-based Stylisation for Exemplar Image Colourisation
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.
Evaluating Contrastive Models for Instance-based Image Retrieval
In this work, we evaluate contrastive models for the task of image retrieval.
The Importance of Importance Sampling for Deep Budgeted Training
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.
Temporal Bilinear Encoding Network of Audio-Visual Features at Low Sampling Rates
Current deep learning based video classification architectures are typically trained end-to-end on large volumes of data and require extensive computational resources.
ATSal: An Attention Based Architecture for Saliency Prediction in 360 Videos
The spherical domain representation of 360 video/image presents many challenges related to the storage, processing, transmission and rendering of omnidirectional videos (ODV).
Shallow and Deep Convolutional Networks for Saliency Prediction
The prediction of salient areas in images has been traditionally addressed with hand-crafted features based on neuroscience principles.
Exploring EEG for Object Detection and Retrieval
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.
