MNIST - Edgentiq

TRISKELION-1: Unifying AI’s Descriptive, Predictive, and Generative Powers

Hybrid Quantum-Classical Recurrent Neural Networks: A New Frontier in Sequence Learning

Enhancing Neural Network Quantization with a Novel QUBO-Based ADAROUND Method

Unveiling Weight Distribution Patterns in Neural Networks During Training

Exploring the Reach of Logic Gate Networks in Large-Scale Classification

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Recently Added

Hybrid Quantum-Classical Models Outperform Traditional AI in Image Classification

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Unpacking the ‘Black Box’: Distinguishing Interpretability from Explainability in Deep Learning

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Efficient Neural Network Training: The Proto-PINV+H Method

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How Vision Transformers Boost Quantum Support Vector Machines

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C2G-KD: Generating Synthetic Data with Geometric Constraints for AI Training

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Unpacking Neural Network Behavior: Capacity, Sparsity, and Resilience in Smaller Models

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APTx Neuron: Unifying Activation and Computation in Neural Networks

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Smarter Pruning: Optimizing Deep Neural Networks for Application-Specific Performance

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Advanced AI Combines Neural Networks for Superior Handwritten Digit Recognition

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Optimizing Logic Gate Networks: A Path to More Efficient AI Hardware

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Introducing SoftReMish: A New Activation Function Boosting CNN Performance for Visual Recognition

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Gen AI News and Updates

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TRISKELION-1: Unifying AI’s Descriptive, Predictive, and Generative Powers

November 4, 2025

Hybrid Quantum-Classical Recurrent Neural Networks: A New Frontier in Sequence Learning

October 31, 2025

Enhancing Neural Network Quantization with a Novel QUBO-Based ADAROUND Method

October 21, 2025