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Lovm: Language-only vision model selection
Pre-trained multi-modal vision-language models (VLMs) are becoming increasingly popular
due to their exceptional performance on downstream vision applications, particularly in the …
due to their exceptional performance on downstream vision applications, particularly in the …
Tapas: Train-less accuracy predictor for architecture search
In recent years an increasing number of researchers and practitioners have been
suggesting algorithms for large-scale neural network architecture search: genetic …
suggesting algorithms for large-scale neural network architecture search: genetic …
Precision-weighted federated learning
Federated Learning using the Federated Averaging algorithm has shown great advantages
for large-scale applications that rely on collaborative learning, especially when the training …
for large-scale applications that rely on collaborative learning, especially when the training …
A comparative analysis of deep neural network architectures for sentence classification using genetic algorithm
Because of the number of different architectures, numerous settings of their hyper-
parameters and disparity among their sizes, it is difficult to equitably compare various deep …
parameters and disparity among their sizes, it is difficult to equitably compare various deep …
Clustered redundant keypoint elimination method for image mosaicing using a new Gaussian-weighted blending algorithm
In this paper, a new method for image mosaicing (image stitching) is introduced based on
Scale Invariant Feature transform (SIFT). One of the main drawbacks of SIFT is the …
Scale Invariant Feature transform (SIFT). One of the main drawbacks of SIFT is the …
Multiclass classification by Min–Max ECOC with Hamming distance optimization
Two questions often arise in the field of the ensemble in multiclass classification problems,(i)
how to combine base classifiers and (ii) how to design possible binary classifiers. Error …
how to combine base classifiers and (ii) how to design possible binary classifiers. Error …
Predicting the Encoding Error of SIRENs
Implicit Neural Representations (INRs), which encode signals such as images, videos, and
3D shapes in the weights of neural networks, are becoming increasingly popular. Among …
3D shapes in the weights of neural networks, are becoming increasingly popular. Among …
What can we Learn by Predicting Accuracy?
This paper seeks to answer the following question:" What can we learn by predicting
accuracy?". Indeed, classification is one of the most popular tasks in machine learning, and …
accuracy?". Indeed, classification is one of the most popular tasks in machine learning, and …
Constrained deep neural network architecture search for IoT devices accounting for hardware calibration
Deep neural networks achieve outstanding results for challenging image classification tasks.
However, the design of network topologies is a complex task, and the research community is …
However, the design of network topologies is a complex task, and the research community is …
An intelligent hierarchical residual attention learning‐based conjoined twin neural network for Alzheimer's stage detection and prediction
Alzheimer's disorder (AD) causes permanent impairment in the brain's memory of the
cellular system, leading to the initiation of dementia. Earlier detection of Alzheimer's disease …
cellular system, leading to the initiation of dementia. Earlier detection of Alzheimer's disease …