SOTAVerified

Computational Efficiency

Methods and optimizations to reduce the computational resources (e.g., time, memory, or power) needed for training and inference in models. This involves techniques that streamline processing, optimize algorithms, or leverage hardware to enhance performance without compromising accuracy.

Papers

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Showing 1531–1540 of 4891 papers

TitleStatusHype
Multi-Agent Reinforcement Learning with Selective State-Space Models—0
LArctan-SKAN: Simple and Efficient Single-Parameterized Kolmogorov-Arnold Networks using Learnable Trigonometric FunctionCode0
Not All Heads Matter: A Head-Level KV Cache Compression Method with Integrated Retrieval and ReasoningCode1
FastSurvival: Hidden Computational Blessings in Training Cox Proportional Hazards Models—0
Less Discriminatory Alternative and Interpretable XGBoost Framework for Binary Classification—0
Taipan: Efficient and Expressive State Space Language Models with Selective Attention—0
GeoLoRA: Geometric integration for parameter efficient fine-tuning—0
Automated Defect Detection and Grading of Piarom Dates Using Deep Learning—0
ExpertFlow: Optimized Expert Activation and Token Allocation for Efficient Mixture-of-Experts Inference—0
YOLOv11: An Overview of the Key Architectural EnhancementsCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified