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 2961–2970 of 4891 papers

TitleStatusHype
Temporal-Spatial Attention Network (TSAN) for DoS Attack Detection in Network Traffic—0
Tensor-Based Backpropagation in Neural Networks with Non-Sequential Input—0
Tensor Decomposition with Unaligned Observations—0
Low-rank Tensor Grid for Image Completion—0
TensorSocket: Shared Data Loading for Deep Learning Training—0
Test-time Adaptation for Foundation Medical Segmentation Model without Parametric Updates—0
TetSphere Splatting: Representing High-Quality Geometry with Lagrangian Volumetric Meshes—0
Texture Superpixel Clustering from Patch-based Nearest Neighbor Matching—0
The 4th AI City Challenge—0
The Best of Both Worlds: Bridging Quality and Diversity in Data Selection with Bipartite Graph—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified