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 581–590 of 4891 papers

TitleStatusHype
AsymFormer: Asymmetrical Cross-Modal Representation Learning for Mobile Platform Real-Time RGB-D Semantic SegmentationCode1
Boosting Light-Weight Depth Estimation Via Knowledge DistillationCode1
Change State Space Models for Remote Sensing Change DetectionCode1
CobBO: Coordinate Backoff Bayesian Optimization with Two-Stage KernelsCode1
Hexatagging: Projective Dependency Parsing as TaggingCode1
Hi-End-MAE: Hierarchical encoder-driven masked autoencoders are stronger vision learners for medical image segmentationCode1
AI Accelerator Survey and TrendsCode1
Efficient Learning of Mesh-Based Physical Simulation with BSMS-GNNCode1
Glance and Focus: a Dynamic Approach to Reducing Spatial Redundancy in Image ClassificationCode1
Birdie: Advancing State Space Models with Reward-Driven Objectives and CurriculaCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified