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 2826–2850 of 4891 papers

TitleStatusHype
Contextual Multinomial Logit Bandits with General Value Functions—0
On Computationally Efficient Multi-Class Calibration—0
Differentially Private Training of Mixture of Experts Models—0
Evolution and Efficiency in Neural Architecture Search: Bridging the Gap Between Expert Design and Automated Optimization—0
Domain Adaptable Fine-Tune Distillation Framework For Advancing Farm SurveillanceCode0
Peeking with PEAK: Sequential, Nonparametric Composite Hypothesis Tests for Means of Multiple Data StreamsCode0
Sparse-VQ Transformer: An FFN-Free Framework with Vector Quantization for Enhanced Time Series Forecasting—0
Model-Based RL for Mean-Field Games is not Statistically Harder than Single-Agent RLCode0
Selective Forgetting: Advancing Machine Unlearning Techniques and Evaluation in Language Models—0
AttnLRP: Attention-Aware Layer-Wise Relevance Propagation for Transformers—0
On the Completeness of Invariant Geometric Deep Learning ModelsCode0
Majority Kernels: An Approach to Leverage Big Model Dynamics for Efficient Small Model Training—0
Curriculum reinforcement learning for quantum architecture search under hardware errors—0
Partially Stochastic Infinitely Deep Bayesian Neural NetworksCode0
A Survey on Graph Condensation—0
Nonlinear subspace clustering by functional link neural networks—0
Unveiling Delay Effects in Traffic Forecasting: A Perspective from Spatial-Temporal Delay Differential Equations—0
A Robust Super-resolution Gridless Imaging Framework for UAV-borne SAR Tomography—0
kNN Algorithm for Conditional Mean and Variance Estimation with Automated Uncertainty Quantification and Variable Selection—0
Parallel Spiking Unit for Efficient Training of Spiking Neural Networks—0
Reimagining Reality: A Comprehensive Survey of Video Inpainting Techniques—0
Improving Global Weather and Ocean Wave Forecast with Large Artificial Intelligence Models—0
Data-Driven Filter Design in FBP: Transforming CT Reconstruction with Trainable Fourier SeriesCode0
EmoDM: A Diffusion Model for Evolutionary Multi-objective Optimization—0
Brain Tumor Diagnosis Using Quantum Convolutional Neural Networks—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified