Search Results for author:
Found 14 papers, 7 papers with code
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.
MANAS: Multi-Agent Neural Architecture Search
The Neural Architecture Search (NAS) problem is typically formulated as a graph search problem where the goal is to learn the optimal operations over edges in order to maximise a graph-level global objective.
GANprintR: Improved Fakes and Evaluation of the State of the Art in Face Manipulation Detection
The availability of large-scale facial databases, together with the remarkable progresses of deep learning technologies, in particular Generative Adversarial Networks (GANs), have led to the generation of extremely realistic fake facial content, raising obvious concerns about the potential for misuse.
A Hybrid Method for Training Convolutional Neural Networks
Artificial Intelligence algorithms have been steadily increasing in popularity and usage.
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.
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
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
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
REGINA - Reasoning Graph Convolutional Networks in Human Action Recognition
It is known that the kinematics of the human body skeleton reveals valuable information in action recognition.
Generative Adversarial Graph Convolutional Networks for Human Action Synthesis
Synthesising the spatial and temporal dynamics of the human body skeleton remains a challenging task, not only in terms of the quality of the generated shapes, but also of their diversity, particularly to synthesise realistic body movements of a specific action (action conditioning).
Ranked #1 on
Human action generation
on Human3.6M
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.
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.
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
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.
