Model Pruning - Edgentiq

Unlocking Efficient Subnetworks in Transformer Attention Mechanisms

New Neural Network Design Uses Discrete Cosine Transform for Smarter AI

Advancing Medical Diagnostics with Real-Time Deep Learning for Image Analysis

BALDWHISPER: Making AI Speech Models Faster and Smaller for Languages with Limited Data

Enhanced KV Cache Eviction for Large Language Models

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The Pruning Paradox: When Model Compression Becomes a Security Risk

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Energy-Efficient Intrusion Detection for IoT Using Optimized Federated Learning

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Optimizing Vision-Language-Action Models with Smart Pruning

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Streamlining Large Language Models for Faster Inference

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PLaMo 2: Hybrid Architecture and Synthetic Data Drive Japanese LLM Performance

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Improving Real-Time Seizure Detection Through Neural Network Pruning

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DaMoC: Streamlining Optimal LLM Selection for Specialized Tasks

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Adaptive Pruning for Federated Person Re-Identification

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Unpacking Pruning Strategies: A Deep Dive into One-Shot, Iterative, and Hybrid Model Compression

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Streamlining Large Reasoning Models: A New Approach to Shorter, Smarter Outputs

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OverFill: Enhancing Language Model Efficiency Through Dual-Stage Processing

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Beyond Efficiency: Structured Sparsity Improves Transformer Generalization

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Un-pruning: Enabling Data Forgetting in the Structure of Sparse AI Models

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Clearer and Leaner AI for Medical Imaging

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FedPhD: Enhancing Diffusion Model Training in Federated Learning with Pruning and Hierarchical Aggregation

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Gen AI News and Updates

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Unlocking Efficient Subnetworks in Transformer Attention Mechanisms

November 7, 2025

New Neural Network Design Uses Discrete Cosine Transform for Smarter AI

November 6, 2025

Advancing Medical Diagnostics with Real-Time Deep Learning for Image Analysis

October 21, 2025