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 301–310 of 4891 papers

TitleStatusHype
TabMixer: advancing tabular data analysis with an enhanced MLP-mixer approachCode1
H3DE-Net: Efficient and Accurate 3D Landmark Detection in Medical ImagingCode1
Exploiting Deblurring Networks for Radiance FieldsCode1
Table-Critic: A Multi-Agent Framework for Collaborative Criticism and Refinement in Table ReasoningCode1
Reduced Order Modeling with Shallow Recurrent Decoder NetworksCode1
CalibQuant: 1-Bit KV Cache Quantization for Multimodal LLMsCode1
Medicine on the Edge: Comparative Performance Analysis of On-Device LLMs for Clinical ReasoningCode1
PTZ-Calib: Robust Pan-Tilt-Zoom Camera CalibrationCode1
InTAR: Inter-Task Auto-Reconfigurable Accelerator Design for High Data Volume Variation in DNNsCode1
Hi-End-MAE: Hierarchical encoder-driven masked autoencoders are stronger vision learners for medical image segmentationCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified