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 851–875 of 4891 papers

TitleStatusHype
Applications of Knowledge Distillation in Remote Sensing: A Survey—0
Adversarial Purification for Data-Driven Power System Event Classifiers with Diffusion Models—0
A Comprehensive Review of Techniques, Algorithms, Advancements, Challenges, and Clinical Applications of Multi-modal Medical Image Fusion for Improved Diagnosis—0
Just Functioning as a Hook for Two-Stage Referring Multi-Object Tracking—0
Application of Distributed Arithmetic to Adaptive Filtering Algorithms: Trends, Challenges and Future—0
Appearance-based Gesture recognition in the compressed domain—0
Adversarial Imitation Learning via Random Search—0
Appa: Bending Weather Dynamics with Latent Diffusion Models for Global Data Assimilation—0
A posteriori Trading-inspired Model-free Time Series Segmentation—0
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
Coordination of OLTC and Smart Inverters for Optimal Voltage Regulation of Unbalanced Distribution Networks—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
A Path Integral Approach for Time-Dependent Hamiltonians with Applications to Derivatives Pricing—0
A partial likelihood approach to tree-based density modeling and its application in Bayesian inference—0
Advancing Machine Learning in Industry 4.0: Benchmark Framework for Rare-event Prediction in Chemical Processes—0
A Comparison of Deep Learning Architectures for Spacecraft Anomaly Detection—0
A parsimonious, computationally efficient machine learning method for spatial regression—0
Advancing Diffusion Models: Alias-Free Resampling and Enhanced Rotational Equivariance—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified