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 2001–2025 of 4891 papers

TitleStatusHype
Constrained Gaussian Process Motion Planning via Stein Variational Newton Inference—0
Formulations and Approximations of the Branch Flow Model for Mesh Power Networks—0
Constrained Trajectory Optimization for Hybrid Dynamical Systems—0
Forward and Backward Bellman equations improve the efficiency of EM algorithm for DEC-POMDP—0
Gradient-based Sample Selection for Faster Bayesian Optimization—0
Forward-Backward Selection with Early Dropping—0
Foundation Model for Wireless Technology Recognition Using IQ Timeseries—0
Foundations for Transfer in Reinforcement Learning: A Taxonomy of Knowledge Modalities—0
Different thresholding methods on Nearest Shrunken Centroid algorithm—0
Fourier-DeepONet: Fourier-enhanced deep operator networks for full waveform inversion with improved accuracy, generalizability, and robustness—0
Context-Aware Neural Gradient Mapping for Fine-Grained Instruction Processing—0
"FRAME: Forward Recursive Adaptive Model Extraction -- A Technique for Advance Feature Selection"—0
Context-aware recommendations from implicit data via scalable tensor factorization—0
Frames and vertex-frequency representations in graph fractional Fourier domain—0
Differential Privacy of Hierarchical Census Data: An Optimization Approach—0
FRC-TOuNN: Topology Optimization of Continuous Fiber Reinforced Composites using Neural Network—0
Free-DyGS: Camera-Pose-Free Scene Reconstruction based on Gaussian Splatting for Dynamic Surgical Videos—0
Context-CrackNet: A Context-Aware Framework for Precise Segmentation of Tiny Cracks in Pavement images—0
Context-endcoding for neural network based skull stripping in magnetic resonance imaging—0
Differentially Private Training of Mixture of Experts Models—0
PrivateMail: Supervised Manifold Learning of Deep Features With Differential Privacy for Image Retrieval—0
FrePolad: Frequency-Rectified Point Latent Diffusion for Point Cloud Generation—0
FreqINR: Frequency Consistency for Implicit Neural Representation with Adaptive DCT Frequency Loss—0
FreqMoE: Dynamic Frequency Enhancement for Neural PDE Solvers—0
Accelerating Convolutional Neural Network Pruning via Spatial Aura Entropy—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified