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 4151–4175 of 4891 papers

TitleStatusHype
Advancing Distributed AC Optimal Power Flow for Integrated Transmission-Distribution Systems—0
Distributed Consensus Optimization with Consensus ALADIN—0
Distributed Cross-Channel Hierarchical Aggregation for Foundation Models—0
Distributed Differential Dynamic Programming Architectures for Large-Scale Multi-Agent Control—0
Distributed Differentially Private Data Analytics via Secure Sketching—0
Distributed Experiment Design and Control for Multi-agent Systems with Gaussian Processes—0
Distributed Flexible Nonlinear Tensor Factorization—0
Distributed Sketching Methods for Privacy Preserving Regression—0
Distributed Traffic Signal Control via Coordinated Maximum Pressure-plus-Penalty—0
Distributionally Robust Model Predictive Control with Total Variation Distance—0
Distribution-Aware Sampling and Weighted Model Counting for SAT—0
DITTO: Diffusion Inference-Time T-Optimization for Music Generation—0
Diverse super-resolution with pretrained deep hiererarchical VAEs—0
Divide-and-Conquer Strategy for Large-Scale Dynamic Bayesian Network Structure Learning—0
Divide, Specialize, and Route: A New Approach to Efficient Ensemble Learning—0
DLCM: a versatile multi-level solver for heterogeneous multicellular systems—0
DMFC-GraspNet: Differentiable Multi-Fingered Robotic Grasp Generation in Cluttered Scenes—0
DMRA: An Adaptive Line Spectrum Estimation Method through Dynamical Multi-Resolution of Atoms—0
DNA cyclization and looping in the wormlike limit: normal modes and the validity of the harmonic approximation—0
DNN-Compressed Domain Visual Recognition with Feature Adaptation—0
DN-ResNet: Efficient Deep Residual Network for Image Denoising—0
Do global forecasting models require frequent retraining?—0
DOLLAR: Few-Step Video Generation via Distillation and Latent Reward Optimization—0
Domain Adaptation Broad Learning System Based on Locally Linear Embedding—0
Domain Adaptation Extreme Learning Machines for Drift Compensation in E-nose Systems—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified