23 papers · ranked by Valyu relevance
Sidahmed Benabderrahmane, James Cheney, Talal Rahwan
Advanced Persistent Threats (APTs) pose a significant challenge in cybersecurity due to their stealthy and long-term nature. Modern supervised learning methods require extensive labeled data, which is often scarce in real-world cybersecurity environments. In this paper, we propose an innovative approach that leverages…
Gadirov, Hamid
M AC H I N E L E A R N I N G F O R S C I E N T I F I C V I S UA L I Z AT I O N : E N S E M B L E DATA A N A LY S I S
Mohamed A. Abdel-Rahman, Ala Saleh Alluhaidan, Sahar A. El-Rahman, Ahmed E. Masnour + 5 more
Intrusion detection systems (IDS) play a vital role in protecting computer networks from malicious activities. Dimensionality reduction techniques are commonly employed to enhance the effectiveness and accuracy of machine learning based IDS. In this study, we proposed an effective dimensionality reduction technique…
Mohaimen Mohammed, Mesut Çevik, Stefano Savazzi
This paper presents a Deep Autoencoder-LDPC-OFDM (DAE-LDPC-OFDM) transceiver architecture that integrates a learned belief propagation (BP) decoder to achieve robust, energy-efficient, and adaptive wireless communication. Unlike conventional modular systems that treat encoding, modulation, and decoding as independent…
Kui Tuo, Shengfeng Deng, Yuxiang Yang, Yanyang Wang + 4 more
The local rules of elementary cellular automata (ECA) with one-dimensional three-cell neighborhoods are represented by eight-bit binary numbers that encode deterministic update rules. This class of systems is also commonly referred to as the Wolfram cellular automata. These automata are widely utilized to investigate…
Iclal Cetin Tas
In biomedical imaging, noise is a fundamental problem negatively impacting diagnostic quality, and effective noise reduction methods are critical for preserving structural details. This study proposes a hybrid noise reduction framework integrated with a CNN-based fusion network, combining a convolutional autoencoder, a…
Authors not listed
The discovery of chemically novel or structurally anomalous metal-organic frameworks (MOFs) is essential for expanding reticular design space and enhancing dataset reliability. We present CHEM-AD (Chemically Unusual Metal–organic Frameworks via Autoencoder-based Detection), a label-free, CPU-efficient pipeline that…
Evan Gorstein, Mengze Tang, Hailey Bruzzone, Claudia Solís-Lemus
Standard methods for ancestral sequence reconstruction (ASR) rely on substitution models for the residues in a biological sequence and assume independent evolution across these sites, ignoring the epistatic interactions that shape molecular evolution. In contrast, deep learning models like variational autoencoders…
Rishi Sonthalia, Raj Rao Nadakuditi
We introduce a novel regularization scheme for autoencoders based on matricial free energy. Our approach defines a differentiable loss function in terms of the singular values of the code matrix (code dimension × batch size). From the standpoint of free probability and random matrix theory, this loss achieves its…
Kairui Ding
Sparse identification of nonlinear dynamics (SINDy) has been widely used to discover the governing equations of a dynamical system from data. It uses sparse regression techniques to identify parsimonious models of unknown systems from a library of candidate functions. Therefore, it relies on the assumption that the…
Yasin Atilkan, Berk Kirik, Eren Tuna Acikbas, Fatih Ekinci + 5 more
Crayfish play an important role in freshwater ecosystems, and sex classification is crucial for analyzing their demographic structures. This study performed binary classification using traditional machine learning and deep learning models on tabular and image datasets with an imbalanced class distribution. For tabular…
Ahmed A. Harby, Farhana Zulkernine, Hanady M. Abdulsalam
The rapid growth of multimedia content has increased the demand for effective methods to reduce storage requirements while maintaining quality and enabling fast data transmission. Existing standards and generative model approaches often involve high computational cost, require extensive parameter tuning, and produce…
Gananath R
Variational Autoencoders (VAEs) belong to a family of autoencoders with probabilistic properties, making them well suited for generating data by producing a smooth and continuous latent space. Despite being introduced over a decade ago, the method continues to be widely adopted in both research and industry for diverse…
Mohammad Arif Rasyidi, Omar Alhussein, Sami Muhaidat, Ernesto Damiani
- We present a large-scale, systematic evaluation of hybrid quantumclassical (HQC) autoencoders for network intrusion detection systems (NIDS), iterating over key design choices, including quantum layer placement, measurement approach, variational and non-variational formulations, latent-space regularization, and the…
María Peña Fernández, Lara Lloret Iglesias, Jesús Marco de Lucas
One of the most compelling ideas for bridging neuroscience and artificial neural networks is the establishment of a framework based on three main components: network architecture, optimization mechanism, and loss (or objective) function to be minimized. While the first two components have been extensively explored, the…
Juan José Burred, Carmine-Emanuele Cella
We propose the use of Non-Negative Autoencoders (NAEs) for sound deconstruction and user-guided manipulation of sounds for creative purposes. NAEs offer a versatile and scalable extension of traditional Non-Negative Matrix Factorization (NMF)-based approaches for interpretable audio decomposition. By enforcing…
Zikang Wan, Marko Zolo Gozano Untalan, Danilo Vasconcellos Vargas
Bulk tissue RNA-sequencing data from large-scale consortia such as GTEx provide comprehensive gene expression profiles across diverse human tissues. However, the high-dimensional nature of bulk RNA-seq data, combined with technical noise and batch effects, poses challenges for downstream analyses. While dimensionality…
Maxence Lapatrie, Jason da Silva Castanheira, Idil Aydin, Sylvain Baillet
Human brain activity contains stable, individual-specific features that persist over months to years, forming neurophysiological profiles. Most model-based profiling approaches use participant labels or supervised objectives, making it difficult to determine whether successful differentiation reflects stable biology or…
Authors not listed
The automated discovery of chemical and catalytic reactions remains a major challenge in computational chemistry, particularly in complex systems where conventional methods struggle to identify optimal searching directions. Here, we propose Loxodynamics, a machine-learning-driven approach for reaction exploration via…
Jeanie Schreiber, Tyrus Berry, Zeeshan Ahmed
Principal Component Analysis or PCA-like properties (orthogonality, variance ranking) are seldom realized in deep autoencoder architectures. In this work, we present ODIN (Orthogonal Dendritic Intrinsic Network), a novel autoencoder architecture that recovers PCA-like latent structure in a fully non-linear regime. By…
Yeowon Kim, Yul H. R. Kang
Navigation requires perception: location must be inferred from noisy and ambiguous egocentric sensory inputs, as in visual estimation of distance. However, many classical models of spatial representation implicitly assume that allocentric location is directly observable, thereby neglecting perceptual uncertainty. Here…
Authors not listed
This comprehensive review examines the evolution of autonomous materials synthesis laboratories that integrate artificial intelligence with advanced robotics to accelerate discovery. Traditional materials development pipelines typically require 10-20 years, but self-driving laboratories (SDLs) and Materials…
Authors not listed
Machine learning models are increasingly applied to heterogeneous materials datasets spanning different synthesis routes, measurement protocols, and structural classes. Although multi-task and representation-learning approaches are commonly used to improve predictive performance, the latent representations learned by…