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 3071–3080 of 4891 papers

TitleStatusHype
Neural network-based CUSUM for online change-point detection—0
Towards Reusable Network Components by Learning Compatible Representations—0
Training or Architecture? How to Incorporate Invariance in Neural Networks—0
Training Sparse Neural Network by Constraining Synaptic Weight on Unit Lp Sphere—0
Training Structured Efficient Convolutional Layers—0
Trajectory Data Suffices for Statistically Efficient Learning in Offline RL with Linear q^π-Realizability and Concentrability—0
Constrained Trajectory Optimization on Matrix Lie Groups via Lie-Algebraic Differential Dynamic Programming—0
Trajectory PHD and CPHD Filters with Unknown Detection Profile—0
Trajectory Tracking for UAVs: An Interpolating Control Approach—0
TrajFM: A Vehicle Trajectory Foundation Model for Region and Task Transferability—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified