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 291–300 of 4891 papers

TitleStatusHype
Efficient Long Sequence Modeling via State Space Augmented TransformerCode1
Efficient Aggregated Kernel Tests using Incomplete U-statisticsCode1
Accel-GCN: High-Performance GPU Accelerator Design for Graph Convolution NetworksCode1
Efficient and Accurate Pneumonia Detection Using a Novel Multi-Scale Transformer ApproachCode1
2D-TPE: Two-Dimensional Positional Encoding Enhances Table Understanding for Large Language ModelsCode1
Efficient and Compact Convolutional Neural Network Architectures for Non-temporal Real-time Fire DetectionCode1
Efficient Multi-agent Reinforcement Learning by PlanningCode1
Prompt Tuned Embedding Classification for Multi-Label Industry Sector AllocationCode1
AdaRank: Adaptive Rank Pruning for Enhanced Model MergingCode1
Dynamic Multimodal FusionCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified