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 611–620 of 4891 papers

TitleStatusHype
Algorithmic Differentiation for Automated Modeling of Machine Learned Force FieldsCode1
Fast ground penetrating radar dual-parameter full waveform inversion method accelerated by hybrid compilation of CUDA kernel function and PyTorchCode1
Attention, Please! Revisiting Attentive Probing for Masked Image ModelingCode1
Attention U-Net: Learning Where to Look for the PancreasCode1
DCT-SNN: Using DCT to Distribute Spatial Information over Time for Learning Low-Latency Spiking Neural NetworksCode1
Deep Generalization of Structured Low-Rank Algorithms (Deep-SLR)Code1
Fast Sequence-Based Embedding with Diffusion GraphsCode1
A Simple Local Minimal Intensity Prior and An Improved Algorithm for Blind Image DeblurringCode1
AdaFocus V2: End-to-End Training of Spatial Dynamic Networks for Video RecognitionCode1
DAM: Dynamic Attention Mask for Long-Context Large Language Model Inference AccelerationCode1
Show:102550
← PrevPage 62 of 490Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified