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 911–920 of 4891 papers

TitleStatusHype
Hybridizing Traditional and Next-Generation Reservoir Computing to Accurately and Efficiently Forecast Dynamical SystemsCode0
Hybrid Inexact BCD for Coupled Structured Matrix Factorization in Hyperspectral Super-ResolutionCode0
Interpretable breast cancer classification using CNNs on mammographic imagesCode0
Interpretable Ensembles of Hyper-Rectangles as Base ModelsCode0
Classification of Gleason Grading in Prostate Cancer Histopathology Images Using Deep Learning Techniques: YOLO, Vision Transformers, and Vision MambaCode0
I-SplitEE: Image classification in Split Computing DNNs with Early ExitsCode0
Hybrid Isolation Forest - Application to Intrusion DetectionCode0
COMMA: Coordinate-aware Modulated Mamba Network for 3D Dispersed Vessel SegmentationCode0
CLAQ: Pushing the Limits of Low-Bit Post-Training Quantization for LLMsCode0
CLAP. I. Resolving miscalibration for deep learning-based galaxy photometric redshift estimationCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified