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 3991–4000 of 4891 papers

TitleStatusHype
Towards human-level performance on automatic pose estimation of infant spontaneous movements—0
Mining Truck Platooning Patterns Through Massive Trajectory Data—0
Glance and Focus: a Dynamic Approach to Reducing Spatial Redundancy in Image ClassificationCode1
Robust Behavioral Cloning for Autonomous Vehicles using End-to-End Imitation LearningCode1
SWIFT: Scalable Wasserstein Factorization for Sparse Nonnegative Tensors—0
Reward-Biased Maximum Likelihood Estimation for Linear Stochastic Bandits—0
Model-Free Non-Stationary RL: Near-Optimal Regret and Applications in Multi-Agent RL and Inventory Control—0
A Transformer-based Framework for Multivariate Time Series Representation LearningCode1
Quantifying Statistical Significance of Neural Network-based Image Segmentation by Selective InferenceCode0
DCT-SNN: Using DCT to Distribute Spatial Information over Time for Learning Low-Latency Spiking Neural NetworksCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified