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 3011–3020 of 4891 papers

TitleStatusHype
Model Consistency for Mechanical Design: Bridging Lumped and Distributed Parameter Models with A Priori Guarantees—0
Treasure What You Have: Exploiting Similarity in Deep Neural Networks for Efficient Video Processing—0
SlicerTMS: Real-Time Visualization of Transcranial Magnetic Stimulation for Mental Health TreatmentCode1
Coil Sketching for computationally-efficient MR iterative reconstruction—0
CosmoPower-JAX: high-dimensional Bayesian inference with differentiable cosmological emulatorsCode1
Towards Better Graph Representation Learning with Parameterized Decomposition & FilteringCode1
Medical supervised masked autoencoders: Crafting a better masking strategy and efficient fine-tuning schedule for medical image classificationCode0
TaLU: A Hybrid Activation Function Combining Tanh and Rectified Linear Unit to Enhance Neural NetworksCode0
RFR-WWANet: Weighted Window Attention-Based Recovery Feature Resolution Network for Unsupervised Image RegistrationCode0
Camera-Based HRV Prediction for Remote Learning EnvironmentsCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified