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 861–870 of 4891 papers

TitleStatusHype
Adversarial Contrastive Learning by Permuting Cluster Assignments—0
Solving Satisfiability Modulo Counting Exactly with Probabilistic Circuits—0
Advancing Semantic Caching for LLMs with Domain-Specific Embeddings and Synthetic Data—0
A Point-Based Approach to Efficient LiDAR Multi-Task Perception—0
A Physics-informed machine learning model for time-dependent wave runup prediction—0
Advancing Physics Data Analysis through Machine Learning and Physics-Informed Neural Networks—0
ConvNeXt-ChARM: ConvNeXt-based Transform for Efficient Neural Image Compression—0
A Physics-Informed Machine Learning Approach for Solving Distributed Order Fractional Differential Equations—0
A Pathway to Near Tissue Computing through Processing-in-CTIA Pixels for Biomedical Applications—0
Advancing Neuromorphic Computing: Mixed-Signal Design Techniques Leveraging Brain Code Units and Fundamental Code Units—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified