Search Results for author:
Found 28 papers, 3 papers with code
Edge2Vec: A High Quality Embedding for the Jigsaw Puzzle Problem
Pairwise compatibility measure (CM) is a key component in solving the jigsaw puzzle problem (JPP) and many of its recently proposed variants.
Gator: Customizable Channel Pruning of Neural Networks with Gating
The rise of neural network (NN) applications has prompted an increased interest in compression, with a particular focus on channel pruning, which does not require any additional hardware.
TEN: Twin Embedding Networks for the Jigsaw Puzzle Problem with Eroded Boundaries
This paper introduces the novel CNN-based encoder Twin Embedding Network (TEN), for the jigsaw puzzle problem (JPP), which represents a puzzle piece with respect to its boundary in a latent embedding space.
Stealing Knowledge from Protected Deep Neural Networks Using Composite Unlabeled Data
The best way to extract or steal knowledge from such networks is by querying them using a large dataset of random samples and recording their output, followed by training a student network to mimic these outputs, without making any assumption about the original networks.
DeepEthnic: Multi-Label Ethnic Classification from Face Images
Ethnic group classification is a well-researched problem, which has been pursued mainly during the past two decades via traditional approaches of image processing and machine learning.
A Novel Hybrid Scheme Using Genetic Algorithms and Deep Learning for the Reconstruction of Portuguese Tile Panels
Specifically, we introduce an enhanced GA-based puzzle solver, whose integration with a novel DL-based compatibility measure (DLCM) yields state-of-the-art performance, regarding the above application.
Handwriting-Based Gender Classification Using End-to-End Deep Neural Networks
Handwriting-based gender classification is a well-researched problem that has been approached mainly by traditional machine learning techniques.
Supervised and Unsupervised End-to-End Deep Learning for Gene Ontology Classification of Neural In Situ Hybridization Images
In recent years, large datasets of high-resolution mammalian neural images have become available, which has prompted active research on the analysis of gene expression data.
End-to-End Deep Neural Networks and Transfer Learning for Automatic Analysis of Nation-State Malware
We record the dynamic behavior of the APT when run in a sandbox and use it as raw input for the neural network, allowing the DNN to learn high level feature abstractions of the APTs itself.
DeepMimic: Mentor-Student Unlabeled Data Based Training
In addition, the time required for training the student model to reach the mentor's performance level is shorter, as a result of a simplified architecture and more available data.
Ground Truth Simulation for Deep Learning Classification of Mid-Resolution Venus Images Via Unmixing of High-Resolution Hyperspectral Fenix Data
Training a deep neural network for classification constitutes a major problem in remote sensing due to the lack of adequate field data.
DeepAPT: Nation-State APT Attribution Using End-to-End Deep Neural Networks
The task of attributing an APT to a specific nation-state is extremely challenging for several reasons.
DeepBrain: Functional Representation of Neural In-Situ Hybridization Images for Gene Ontology Classification Using Deep Convolutional Autoencoders
This paper presents a novel deep learning-based method for learning a functional representation of mammalian neural images.
DeepChess: End-to-End Deep Neural Network for Automatic Learning in Chess
We present an end-to-end learning method for chess, relying on deep neural networks.
DeepPainter: Painter Classification Using Deep Convolutional Autoencoders
In this paper we describe the problem of painter classification, and propose a novel approach based on deep convolutional autoencoder neural networks.
DNN-Buddies: A Deep Neural Network-Based Estimation Metric for the Jigsaw Puzzle Problem
This paper introduces the first deep neural network-based estimation metric for the jigsaw puzzle problem.
DeepSign: Deep Learning for Automatic Malware Signature Generation and Classification
While conventional signature and token based methods for malware detection do not detect a majority of new variants for existing malware, the results presented in this paper show that signatures generated by the DBN allow for an accurate classification of new malware variants.
Genetic Algorithms for Evolving Computer Chess Programs
The evaluation function of the program is evolved by learning from databases of (human) grandmaster games.
Genetic Algorithms for Evolving Deep Neural Networks
In recent years, deep learning methods applying unsupervised learning to train deep layers of neural networks have achieved remarkable results in numerous fields.
Expert-Driven Genetic Algorithms for Simulating Evaluation Functions
In this paper we demonstrate how genetic algorithms can be used to reverse engineer an evaluation function's parameters for computer chess.
Genetic Algorithms for Mentor-Assisted Evaluation Function Optimization
In this paper we demonstrate how genetic algorithms can be used to reverse engineer an evaluation function's parameters for computer chess.
Simulating Human Grandmasters: Evolution and Coevolution of Evaluation Functions
This paper demonstrates the use of genetic algorithms for evolving a grandmaster-level evaluation function for a chess program.
A Two-Phase Genetic Algorithm for Image Registration
The goal is to search for the "optimal" instance of the transformation model assumed with respect to a similarity measure in question.
Genetic Algorithm-Based Solver for Very Large Multiple Jigsaw Puzzles of Unknown Dimensions and Piece Orientation
In this paper we propose the first genetic algorithm (GA)-based solver for jigsaw puzzles of unknown puzzle dimensions and unknown piece location and orientation.
An Automatic Solver for Very Large Jigsaw Puzzles Using Genetic Algorithms
In this paper we propose the first effective genetic algorithm (GA)-based jigsaw puzzle solver.
A Generalized Genetic Algorithm-Based Solver for Very Large Jigsaw Puzzles of Complex Types
In this paper we introduce new types of square-piece jigsaw puzzles, where in addition to the unknown location and orientation of each piece, a piece might also need to be flipped.
A Genetic Algorithm-Based Solver for Very Large Jigsaw Puzzles
In this paper we propose the first effective automated, genetic algorithm (GA)-based jigsaw puzzle solver.
Image Registration of Very Large Images via Genetic Programming
Image registration (IR) is a fundamental task in image processing for matching two or more images of the same scene taken at different times, from different viewpoints and/or by different sensors.
