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 2011–2020 of 4891 papers

TitleStatusHype
EROAM: Event-based Camera Rotational Odometry and Mapping in Real-time—0
LoRA Unlearns More and Retains More (Student Abstract)Code0
STLight: a Fully Convolutional Approach for Efficient Predictive Learning by Spatio-Temporal joint Processing—0
Y-MAP-Net: Real-time depth, normals, segmentation, multi-label captioning and 2D human pose in RGB images—0
DiffFNO: Diffusion Fourier Neural Operator—0
A Secure Estimator with Gaussian Bernoulli Mixture Model—0
The Good, The Efficient and the Inductive Biases: Exploring Efficiency in Deep Learning Through the Use of Inductive Biases—0
Advancing Diffusion Models: Alias-Free Resampling and Enhanced Rotational Equivariance—0
Neural Graph Simulator for Complex Systems—0
When Mamba Meets xLSTM: An Efficient and Precise Method with the xLSTM-VMUNet Model for Skin lesion SegmentationCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified