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 1701–1710 of 4891 papers

TitleStatusHype
DecoupleNet: A Lightweight Backbone Network With Efficient Feature Decoupling for Remote Sensing Visual TasksCode1
Beyond Conformal Predictors: Adaptive Conformal Inference with Confidence Predictors—0
Harmonic Path Integral Diffusion—0
A High-Performance External Validity Index for Clustering with a Large Number of Clusters—0
Data-driven Modeling of Linearizable Power Flow for Large-scale Grid Topology Optimization—0
Balancing LoRA Performance and Efficiency with Simple Shard SharingCode2
TACO-RL: Task Aware Prompt Compression Optimization with Reinforcement Learning—0
Deep Transfer Hashing for Adaptive Learning on Federated Streaming Data—0
MambaClinix: Hierarchical Gated Convolution and Mamba-Based U-Net for Enhanced 3D Medical Image SegmentationCode1
Optimal Control for Discrete-Time Systems under Bounded Disturbances—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified