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 3151–3160 of 4891 papers

TitleStatusHype
A Fast Bootstrap Algorithm for Causal Inference with Large DataCode0
Surrogate uncertainty estimation for your time series forecasting black-box: learn when to trust—0
Efficient End-to-End Video Question Answering with Pyramidal Multimodal TransformerCode0
Towards Practical Preferential Bayesian Optimization with Skew Gaussian ProcessesCode1
Diffusion Models for High-Resolution Solar Forecasts—0
Few-Shot Image-to-Semantics Translation for Policy Transfer in Reinforcement LearningCode0
A Modular Multi-stage Lightweight Graph Transformer Network for Human Pose and Shape Estimation from 2D Human Pose—0
Skeleton-based Human Action Recognition via Convolutional Neural Networks (CNN)—0
Low-Carbon Economic Dispatch of Bulk Power Systems Using Nash Bargaining Game—0
Approximating DTW with a convolutional neural network on EEG data—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified