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 3826–3850 of 4891 papers

TitleStatusHype
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