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 3376–3400 of 4891 papers

TitleStatusHype
Addressing Delayed Feedback in Conversion Rate Prediction via Influence Functions—0
Mitigating the Impact of Noisy Edges on Graph-Based Algorithms via Adversarial Robustness Evaluation—0
A Non-Parametric Bootstrap for Spectral Clustering—0
A deep convolutional neural network model for rapid prediction of fluvial flood inundation—0
A Deeper Look at 3D Shape Classifiers—0
A Deep-Genetic Algorithm (Deep-GA) Approach for High-Dimensional Nonlinear Parabolic Partial Differential Equations—0
A Deep Learning Algorithm for High-Dimensional Exploratory Item Factor Analysis—0
A Deep Learning Framework for Boundary-Aware Semantic Segmentation—0
A Deep Learning Model for Traffic Flow State Classification Based on Smart Phone Sensor Data—0
A DenseNet Based Approach for Multi-Frame In-Loop Filter in HEVC—0
A Derivative-free Method for Quantum Perceptron Training in Multi-layered Neural Networks—0
A Diagrammatic Approach to Improve Computational Efficiency in Group Equivariant Neural Networks—0
ADMM Algorithms for Residual Network Training: Convergence Analysis and Parallel Implementation—0
Advanced Deep Learning Techniques for Analyzing Earnings Call Transcripts: Methodologies and Applications—0
Advanced Natural-based interaction for the ITAlian language: LLaMAntino-3-ANITA—0
Advances and Open Challenges in Federated Foundation Models—0
Advances in LLMs with Focus on Reasoning, Adaptability, Efficiency and Ethics—0
Advances in the Simulation and Modeling of Complex Systems using Dynamical Graph Grammars—0
Advancing Brain-Computer Interface System Performance in Hand Trajectory Estimation with NeuroKinect—0
Advancing clinical trial outcomes using deep learning and predictive modelling: bridging precision medicine and patient-centered care—0
Advancing Decoding Strategies: Enhancements in Locally Typical Sampling for LLMs—0
Advancing Diffusion Models: Alias-Free Resampling and Enhanced Rotational Equivariance—0
Advancing Machine Learning in Industry 4.0: Benchmark Framework for Rare-event Prediction in Chemical Processes—0
Advancing Neuromorphic Computing: Mixed-Signal Design Techniques Leveraging Brain Code Units and Fundamental Code Units—0
Advancing Physics Data Analysis through Machine Learning and Physics-Informed Neural Networks—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified