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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2331–2340 of 4891 papers

TitleStatusHype
Multi-criteria Token Fusion with One-step-ahead Attention for Efficient Vision TransformersCode1
T4P: Test-Time Training of Trajectory Prediction via Masked Autoencoder and Actor-specific Token MemoryCode1
MEDPNet: Achieving High-Precision Adaptive Registration for Complex Die Castings—0
Efficient Convolutional Forward Modeling and Sparse Coding in Multichannel Imaging—0
TimeMachine: A Time Series is Worth 4 Mambas for Long-term ForecastingCode3
FakeWatch: A Framework for Detecting Fake News to Ensure Credible Elections—0
StainFuser: Controlling Diffusion for Faster Neural Style Transfer in Multi-Gigapixel Histology ImagesCode1
Hyper-CL: Conditioning Sentence Representations with HypernetworksCode1
Mitigating Data Consistency Induced Discrepancy in Cascaded Diffusion Models for Sparse-view CT Reconstruction—0
Sparse Bayesian Learning-Based Hierarchical Construction for 3D Radio Environment Maps Incorporating Channel Shadowing—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified