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

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Showing 711–720 of 4891 papers

TitleStatusHype
HiPart: Hierarchical Divisive Clustering ToolboxCode1
An adaptive augmented Lagrangian method for training physics and equality constrained artificial neural networksCode1
BUFFER: Balancing Accuracy, Efficiency, and Generalizability in Point Cloud RegistrationCode1
Calibrating LLMs with Information-Theoretic Evidential Deep LearningCode1
How Can We Be So Dense? The Benefits of Using Highly Sparse RepresentationsCode1
Hi-End-MAE: Hierarchical encoder-driven masked autoencoders are stronger vision learners for medical image segmentationCode1
ProtoOcc: Accurate, Efficient 3D Occupancy Prediction Using Dual Branch Encoder-Prototype Query DecoderCode1
Benchmark Dataset for Precipitation Forecasting by Post-Processing the Numerical Weather PredictionCode1
Hexatagging: Projective Dependency Parsing as TaggingCode1
Highly accurate and efficient deep learning paradigm for full-atom protein loop modeling with KarmaLoopCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified