Search Results for author:
Found 23 papers, 8 papers with code
Music to Dance as Language Translation using Sequence Models
We train our method on AIST++ and PhantomDance data sets to teach a robotic arm to dance, but our method can be applied to a full humanoid robot.
Robust Energy Consumption Prediction with a Missing Value-Resilient Metaheuristic-based Neural Network in Mobile App Development
Energy consumption is a fundamental concern in mobile application development, bearing substantial significance for both developers and end-users.
A Metaheuristic-based Machine Learning Approach for Energy Prediction in Mobile App Development
Energy consumption plays a vital role in mobile App development for developers and end-users, and it is considered one of the most crucial factors for purchasing a smartphone.
Are Neural Architecture Search Benchmarks Well Designed? A Deeper Look Into Operation Importance
We found that only a subset of the operation pool is required to generate architectures close to the upper-bound of the performance range.
A Survey of Demonstration Learning
This paper provides a survey of demonstration learning, where we formally introduce the demonstration problem along with its main challenges and provide a comprehensive overview of the process of learning from demonstrations from the creation of the demonstration data set, to learning methods from demonstrations, and optimization by combining demonstration learning with different machine learning methods.
Hierarchical Decision Transformer
Our method outperforms the baselines in eight out of ten tasks of varied horizons and reward frequencies without prior task knowledge, showing the advantages of the hierarchical model approach for learning from demonstrations using a sequence model.
Energy-Aware JPEG Image Compression: A Multi-Objective Approach
Also, two Pareto-based methods, including a non-dominated sorting genetic algorithm (NSGA-II) and a reference-point-based NSGA-II (NSGA-III) are used for the embedding scheme, and two Pareto-based algorithms, EnNSGAII and EnNSGAIII, are presented.
Guided Evolutionary Neural Architecture Search With Efficient Performance Estimation
GEA guides the evolution by exploring the search space by generating and evaluating several architectures in each generation at initialisation stage using a zero-proxy estimator, where only the highest-scoring architecture is trained and kept for the next generation.
Ranked #16 on
Neural Architecture Search
on NAS-Bench-201, CIFAR-100
LudVision -- Remote Detection of Exotic Invasive Aquatic Floral Species using Drone-Mounted Multispectral Data
We used images collected by a drone-mounted multispectral sensor to achieve this, creating our LudVision data set.
Towards Less Constrained Macro-Neural Architecture Search
In this paper, we propose LCMNAS, a method that pushes NAS to less constrained search spaces by performing macro-search without relying on pre-defined heuristics or bounded search spaces.
Contrastive Learning from Demonstrations
This paper presents a framework for learning visual representations from unlabeled video demonstrations captured from multiple viewpoints.
MPF6D: Masked Pyramid Fusion 6D Pose Estimation
We present a new method to estimate the 6D pose of objects that improves upon the accuracy of current proposals and can still be used in real-time.
Guided Evolution for Neural Architecture Search
The rationale behind G-EA, is to explore the search space by generating and evaluating several architectures in each generation at initialization stage using a zero-proxy estimator, where only the highest-scoring network is trained and kept for the next generation.
An AutoML-based Approach to Multimodal Image Sentiment Analysis
Sentiment analysis is a research topic focused on analysing data to extract information related to the sentiment that it causes.
Ranked #1 on
Multimodal Sentiment Analysis
on B-T4SA
EPE-NAS: Efficient Performance Estimation Without Training for Neural Architecture Search
In this paper, we propose EPE-NAS, an efficient performance estimation strategy, that mitigates the problem of evaluating networks, by scoring untrained networks and creating a correlation with their trained performance.
Ranked #26 on
Neural Architecture Search
on NAS-Bench-201, CIFAR-100
Improving Makespan in Dynamic Task Scheduling for Cloud Robotic Systems with Time Window Constraints
Load balancing allocation and scheduling ensures that the time between when the first node completes its scheduled tasks and when all other nodes complete their scheduled tasks is as short as possible.
Robotics
Auto-Classifier: A Robust Defect Detector Based on an AutoML Head
The dominant approach for surface defect detection is the use of hand-crafted feature-based methods.
Ranked #1 on
Defect Detection
on DAGM2007
HMCNAS: Neural Architecture Search using Hidden Markov Chains and Bayesian Optimization
Neural Architecture Search has achieved state-of-the-art performance in a variety of tasks, out-performing human-designed networks.
MaskedFusion: Mask-based 6D Object Pose Estimation
MaskedFusion is a framework to estimate the 6D pose of objects using RGB-D data, with an architecture that leverages multiple sub-tasks in a pipeline to achieve accurate 6D poses.
Ranked #2 on
6D Pose Estimation using RGBD
on YCB-Video
An Overview of Blockchain Integration with Robotics and Artificial Intelligence
Blockchain technology is growing everyday at a fast-passed rhythm and it's possible to integrate it with many systems, namely Robotics with AI services.
SeNA-CNN: Overcoming Catastrophic Forgetting in Convolutional Neural Networks by Selective Network Augmentation
Lifelong learning aims to develop machine learning systems that can learn new tasks while preserving the performance on previous learned tasks.
Stacked Denoising Autoencoders and Transfer Learning for Immunogold Particles Detection and Recognition
In this paper we present a system for the detection of immunogold particles and a Transfer Learning (TL) framework for the recognition of these immunogold particles.
Distribution-Based Categorization of Classifier Transfer Learning
As it is natural in a fast evolving area, a wide variety of TL methods, settings and nomenclature have been proposed so far.
