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 3226–3250 of 4891 papers

TitleStatusHype
XPose: eXplainable Human Pose Estimation—0
xTrimoABFold: De novo Antibody Structure Prediction without MSA—0
XVoxel-Based Parametric Design Optimization of Feature Models—0
Y-MAP-Net: Real-time depth, normals, segmentation, multi-label captioning and 2D human pose in RGB images—0
YOLO11 to Its Genesis: A Decadal and Comprehensive Review of The You Only Look Once (YOLO) Series—0
YOLOv12: A Breakdown of the Key Architectural Features—0
YOLOv8-Based Visual Detection of Road Hazards: Potholes, Sewer Covers, and Manholes—0
YotoR-You Only Transform One Representation—0
You are out of context!—0
ZeroLM: Data-Free Transformer Architecture Search for Language Models—0
ZeroQuant-FP: A Leap Forward in LLMs Post-Training W4A8 Quantization Using Floating-Point Formats—0
Zero-Shot Detection of AI-Generated Images—0
Zero-shot Image Captioning by Anchor-augmented Vision-Language Space Alignment—0
Zero-Shot Multi-task Hallucination Detection—0
ZigzagPointMamba: Spatial-Semantic Mamba for Point Cloud Understanding—0
1.58-bit FLUX—0
Improving Harmful Text Detection with Joint Retrieval and External Knowledge—0
Improving Human Decision-Making by Discovering Efficient Strategies for Hierarchical Planning—0
Improving LoRA in Privacy-preserving Federated Learning—0
Improving Low-Fidelity Models of Li-ion Batteries via Hybrid Sparse Identification of Nonlinear Dynamics—0
Improving Out-of-Distribution Detection via Epistemic Uncertainty Adversarial Training—0
Improving Out-of-Distribution Detection with Markov Logic Networks—0
Improving Prostate Gland Segmenting Using Transformer based Architectures—0
Improving QA Efficiency with DistilBERT: Fine-Tuning and Inference on mobile Intel CPUs—0
Improving Sampling Accuracy of Stochastic Gradient MCMC Methods via Non-uniform Subsampling of Gradients—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified