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 2401–2410 of 4891 papers

TitleStatusHype
WaveMamba: Spatial-Spectral Wavelet Mamba for Hyperspectral Image Classification—0
Multi-head Spatial-Spectral Mamba for Hyperspectral Image ClassificationCode0
GNN-SKAN: Harnessing the Power of SwallowKAN to Advance Molecular Representation Learning with GNNs—0
NeuralFactors: A Novel Factor Learning Approach to Generative Modeling of Equities—0
Hybrid Coordinate Descent for Efficient Neural Network Learning Using Line Search and Gradient Descent—0
MonoMM: A Multi-scale Mamba-Enhanced Network for Real-time Monocular 3D Object Detection—0
Cross-Scan Mamba with Masked Training for Robust Spectral Imaging—0
High Performance Im2win and Direct Convolutions using Three Tensor Layouts on SIMD Architectures—0
Exploiting Change Blindness for Video Coding: Perspectives from a Less Promising User Study—0
An Extended Kalman Filter Integrated Latent Feature Model on Dynamic Weighted Directed Graphs—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified