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 1326–1350 of 4891 papers

TitleStatusHype
A Multiple Source Hourglass Deep Network for Multi-Focus Image Fusion—0
Accelerating IoV Intrusion Detection: Benchmarking GPU-Accelerated vs CPU-Based ML Libraries—0
Bayesian Sequential Stacking Algorithm for Concurrently Designing Molecules and Synthetic Reaction Networks—0
Bayesian Quantum Neural Network for Renewable-Rich Power Flow with Training Efficiency and Generalization Capability Improvements—0
HER2 Expression Prediction with Flexible Multi-Modal Inputs via Dynamic Bidirectional Reconstruction—0
Efficient Ensembles Improve Training Data Attribution—0
Efficient Folded Attention for 3D Medical Image Reconstruction and Segmentation—0
Efficient Malware Detection with Optimized Learning on High-Dimensional Features—0
Bayesian Probabilistic Matrix Factorization—0
Bayesian Patchworks: An Approach to Case-Based Reasoning—0
A Multimodal Approach for Advanced Pest Detection and Classification—0
Bayesian Optimization for Hyperparameters Tuning in Neural Networks—0
Bayesian Optimization by Kernel Regression and Density-based Exploration—0
Accelerating Inference of Retrieval-Augmented Generation via Sparse Context Selection—0
Efficient Distance Metric Learning by Adaptive Sampling and Mini-Batch Stochastic Gradient Descent (SGD)—0
Bayesian Optimal Control of Smoothly Parameterized Systems: The Lazy Posterior Sampling Algorithm—0
Bayesian Off-Policy Evaluation and Learning for Large Action Spaces—0
A Multi-Document Coverage Reward for RELAXed Multi-Document Summarization—0
Bayesian Neural Networks with Variance Propagation for Uncertainty Evaluation—0
Amulet: ReAlignment During Test Time for Personalized Preference Adaptation of LLMs—0
Adaptive Cyclic Diffusion for Inference Scaling—0
Efficient Diffusion Models for Vision: A Survey—0
Efficient Dual-Blind Deconvolution for Joint Radar-Communication Systems Using ADMM: Enhancing Channel Estimation and Signal Recovery in 5G mmWave Networks—0
Bayesian Models of Data Streams with Hierarchical Power Priors—0
Bayesian l_0-regularized Least Squares—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified