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 441–450 of 4891 papers

TitleStatusHype
T4P: Test-Time Training of Trajectory Prediction via Masked Autoencoder and Actor-specific Token MemoryCode1
Hyper-CL: Conditioning Sentence Representations with HypernetworksCode1
StainFuser: Controlling Diffusion for Faster Neural Style Transfer in Multi-Gigapixel Histology ImagesCode1
Fine-Grained Pillar Feature Encoding Via Spatio-Temporal Virtual Grid for 3D Object DetectionCode1
Fast Kernel Scene FlowCode1
ARNN: Attentive Recurrent Neural Network for Multi-channel EEG Signals to Identify Epileptic SeizuresCode1
Fast and Interpretable 2D Homography Decomposition: Similarity-Kernel-Similarity and Affine-Core-Affine TransformationsCode1
Efficient Temporal Extrapolation of Multimodal Large Language Models with Temporal Grounding BridgeCode1
FViT: A Focal Vision Transformer with Gabor FilterCode1
Model Editing by Standard Fine-TuningCode1
Show:102550
← PrevPage 45 of 490Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified