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 1626–1650 of 4891 papers

TitleStatusHype
A Memetic Walrus Algorithm with Expert-guided Strategy for Adaptive Curriculum Sequencing—0
sparseGeoHOPCA: A Geometric Solution to Sparse Higher-Order PCA Without Covariance Estimation—0
Embedded Federated Feature Selection with Dynamic Sparse Training: Balancing Accuracy-Cost Tradeoffs—0
Distributed Differential Dynamic Programming Architectures for Large-Scale Multi-Agent Control—0
Embedding Recurrent Layers with Dual-Path Strategy in a Variant of Convolutional Network for Speaker-Independent Speech Separation—0
Distributed Cross-Channel Hierarchical Aggregation for Foundation Models—0
Distributed Consensus Optimization with Consensus ALADIN—0
Advancing Distributed AC Optimal Power Flow for Integrated Transmission-Distribution Systems—0
Emergent functions of noise-driven spontaneous activity: Homeostatic maintenance of criticality and memory consolidation—0
EmoDM: A Diffusion Model for Evolutionary Multi-objective Optimization—0
Empirical Comparison between Cross-Validation and Mutation-Validation in Model Selection—0
Empirical Fourier Decomposition: An Accurate Adaptive Signal Decomposition Method—0
Cross-Scan Mamba with Masked Training for Robust Spectral Imaging—0
Axial Attention Transformer Networks: A New Frontier in Breast Cancer Detection—0
Emulating the interstellar medium chemistry with neural operators—0
Enabling Fast, Accurate, and Efficient Real-Time Genome Analysis via New Algorithms and Techniques—0
Encoding Categorical Variables with Conjugate Bayesian Models for WeWork Lead Scoring Engine—0
Canonical Bayesian Linear System Identification—0
End-to-End Imitation Learning for Optimal Asteroid Proximity Operations—0
End-to-End JPEG Decoding and Artifacts Suppression Using Heterogeneous Residual Convolutional Neural Network—0
DistilQwen2.5: Industrial Practices of Training Distilled Open Lightweight Language Models—0
An even-load-distribution design for composite bolted joints using a novel circuit model and artificial neural networks—0
End-to-end View Synthesis for Light Field Imaging with Pseudo 4DCNN—0
Distilling Diversity and Control in Diffusion Models—0
Avoiding Obfuscation with Prover-Estimator Debate—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified