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 21–30 of 4891 papers

TitleStatusHype
Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of ExpertsCode4
On the limits of agency in agent-based modelsCode4
T-MAC: CPU Renaissance via Table Lookup for Low-Bit LLM Deployment on EdgeCode4
RaDe-GS: Rasterizing Depth in Gaussian SplattingCode4
Universal and Extensible Language-Vision Models for Organ Segmentation and Tumor Detection from Abdominal Computed TomographyCode4
LLMC: Benchmarking Large Language Model Quantization with a Versatile Compression ToolkitCode4
An Image is Worth 1/2 Tokens After Layer 2: Plug-and-Play Inference Acceleration for Large Vision-Language ModelsCode4
Mamba-UNet: UNet-Like Pure Visual Mamba for Medical Image SegmentationCode4
TRIPS: Trilinear Point Splatting for Real-Time Radiance Field RenderingCode4
RL4CO: an Extensive Reinforcement Learning for Combinatorial Optimization BenchmarkCode4
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified