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 1681–1690 of 4891 papers

TitleStatusHype
MoJE: Mixture of Jailbreak Experts, Naive Tabular Classifiers as Guard for Prompt Attacks—0
Unifying Dimensions: A Linear Adaptive Approach to Lightweight Image Super-ResolutionCode0
Exploring Brain Network Organization in Alzheimer Disease and Frontotemporal Dementia: A Crossplot Transition Entropy Approach—0
Most Influential Subset Selection: Challenges, Promises, and BeyondCode0
A parametric framework for kernel-based dynamic mode decomposition using deep learningCode0
HDFlow: Enhancing LLM Complex Problem-Solving with Hybrid Thinking and Dynamic WorkflowsCode1
Learning Representation for Multitask learning through Self Supervised Auxiliary learning—0
CNN Mixture-of-Depths—0
Classification of Gleason Grading in Prostate Cancer Histopathology Images Using Deep Learning Techniques: YOLO, Vision Transformers, and Vision MambaCode0
3DDX: Bone Surface Reconstruction from a Single Standard-Geometry Radiograph via Dual-Face Depth EstimationCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified