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 3801–3850 of 4891 papers

TitleStatusHype
Deep neural networks with controlled variable selection for the identification of putative causal genetic variantsCode0
L-SR1 Adaptive Regularization by Cubics for Deep Learning—0
Efficient Token Mixing for Transformers via Adaptive Fourier Neural Operators—0
Confusion-based rank similarity filters for computationally-efficient machine learning on high dimensional dataCode0
An Improved Frequent Directions Algorithm for Low-Rank Approximation via Block Krylov Iteration—0
Fast Density Estimation for Density-based Clustering Methods—0
Formulations and Approximations of the Branch Flow Model for Mesh Power Networks—0
An Efficient and Scalable Collection of Fly-inspired Voting Units for Visual Place Recognition in Changing Environments—0
Improved optimization strategies for deep Multi-Task Networks—0
Stochastic MPC with Multi-modal Predictions for Traffic Intersections—0
LOF: Structure-Aware Line Tracking based on Optical Flow—0
Deep Learning based Model-free Robust Load Restoration to Enhance Bulk System Resilience with Wind Power Penetration—0
Gaussian Moments as Physically Inspired Molecular Descriptors for Accurate and Scalable Machine Learning Potentials—0
Towards a variational Jordan-Lee-Preskill quantum algorithm—0
Adaptive Few-Shot Learning PoC Ultrasound COVID-19 Diagnostic System—0
Robust Predictable Control—0
Near-Field Millimeter-Wave Imaging via Arrays in the Shape of Polyline—0
Efficient Action Recognition Using Confidence Distillation—0
Cross-token Modeling with Conditional Computation—0
GTG-Shapley: Efficient and Accurate Participant Contribution Evaluation in Federated LearningCode0
A review of Quantum Neural Networks: Methods, Models, Dilemma—0
Domain-Specific Japanese ELECTRA Model Using a Small Corpus—0
Backstepping Mean-Field Density Control for Large-Scale Heterogeneous Nonlinear Stochastic Systems—0
Feature Analysis for Machine Learning-based IoT Intrusion Detection—0
Stochastic Optimal Operation of the VSC-MTDC System with FACTS Devices to Integrate Wind Power—0
CMML: Contextual Modulation Meta Learning for Cold-Start RecommendationCode0
Flikcer -- A Chrome Extension to Resolve Online Epileptogenic Visual Content with Real-Time Luminance Frequency Analysis—0
Pixel Contrastive-Consistent Semi-Supervised Semantic Segmentation—0
Simple is better: Making Decision Trees faster using random sampling—0
Parallel Quasi-concave set optimization: A new frontier that scales without needing submodularity—0
M-ar-K-Fast Independent Component AnalysisCode0
Seismic wave propagation and inversion with Neural Operators—0
Maximizing Influence with Graph Neural Networks—0
Exact Pareto Optimal Search for Multi-Task Learning and Multi-Criteria Decision-Making—0
Value-Agnostic Conversational Semantic Parsing—0
Beyond Sentence-Level End-to-End Speech Translation: Context Helps—0
Dissecting FLOPs along input dimensions for GreenAI cost estimationsCode0
Training Energy-Efficient Deep Spiking Neural Networks with Single-Spike Hybrid Input Encoding—0
A Frequency-based Parent Selection for Reducing the Effect of Evaluation Time Bias in Asynchronous Parallel Multi-objective Evolutionary Algorithms—0
HYPER-SNN: Towards Energy-efficient Quantized Deep Spiking Neural Networks for Hyperspectral Image Classification—0
Data-based stochastic modeling reveals sources of activity bursts in single-cell TGF-β signalingCode0
Dispatch of Virtual Inertia and Damping: Numerical Method with SDP and ADMM—0
Logspace Reducibility From Secret Leakage Planted Clique—0
Optimal Operation of Power Systems with Energy Storage under Uncertainty: A Scenario-based Method with Strategic Sampling—0
Stein Variational Gradient Descent with Multiple Kernel—0
Precision-Weighted Federated Learning—0
Robust Topology Optimization Using Multi-Fidelity Variational Autoencoders—0
Solving Large-Scale Multi-Objective Optimization via Probabilistic Prediction Model—0
Continuous vs. Discrete Optimization of Deep Neural NetworksCode0
Accelerating Spherical k-Means—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified