The paper proposes a novel ResNet-34 encoder with a lightweight decoder for highly accurate and computationally efficient segmentation of complex fetal brain structures in MRI.

The paper introduces Residualized Sparse Autoencoders (ReSAEs) to improve multi-layer interventions in transformers by training each layer on the residual activation, which better preserves cross-laye…

RASER introduces a family of cheap, router-based systems that selectively decide whether to perform expensive multi-hop retrieval, significantly reducing LLM token costs while maintaining state-of-the…

The elasticAI.explorer is an extensible, unified Python framework that simplifies hardware-aware Neural Architecture Search (NAS) by decoupling search space definition from model implementation and de…

The paper proposes GloResNet, a lightweight 3D CNN that effectively predicts brain injury in preterm infants using T2-weighted MRI, achieving an average accuracy of 75.18%.

This paper demonstrates that in-domain pretraining of BERT significantly improves the detection of DNS exfiltration, particularly in maintaining a low false positive rate.

The paper proposes two novel multi-column RBFN architectures, MC-PSO and MC-APSO, that combine parallel RBFN structures with swarm optimization to significantly outperform existing methods in accuracy…

A hybrid deep learning model combining ResNet-1D, BiGRU, and Multi-Head Attention achieves high accuracy and low latency for robust cyberattack detection in Industrial IoT environments.

The paper introduces Automatically Differentiable Nonlinear Tensor Networks (ADNTNs) to achieve massive, structured compression of deep neural networks, demonstrating compression ratios up to 77,000x…

The paper proposes RESSAP, a novel ensemble framework that significantly enhances the robustness of machine learning classifiers against adversarial evasion attacks by combining feature selection, ens…

This paper proposes a new approach for designing Radio Access Network (RAN) slices in 5G and beyond networks using descriptors that consider both transmission rate and latency requirements to support…

This paper analyzes darknet traffic to characterize advanced, AI-assisted bot reconnaissance, finding that modern evasion techniques allow most bot traffic to bypass standard IDS thresholds.

The paper introduces Graph Cascades, a mesoscopic rewiring technique that enhances Graph Neural Networks by promoting node pairs with strong multi-hop connections to direct edges, improving performanc…

The paper introduces Regularized Large Neighborhood Search (RLNS), a method that adapts the LNS heuristic into an efficient MCMC sampler for combinatorial optimization, allowing end-to-end learning wi…

The paper proposes an extended version of Hypencoder, a retrieval approach that encodes queries as shallow neural networks, achieving comparable effectiveness with fewer active parameters and higher s…

This paper presents a sustainable, low-cost Desktop Virtualization Infrastructure using discarded electronic equipment for digital education.

The paper introduces CEON, a Circular Economy Ontology Network, designed to improve semantic interoperability and knowledge representation across diverse industry sectors throughout the product life c…