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 3851–3875 of 4891 papers

TitleStatusHype
On Optimistic versus Randomized Exploration in Reinforcement Learning—0
On Robust Wasserstein Barycenter: The Model and Algorithm—0
On scenario construction for stochastic shortest path problems in real road networks—0
On Selecting Distance Metrics in n-Dimensional Normed Vector Spaces of Cells: A Novel Criterion and Similarity Measure Towards Efficient and Accurate Omics Analysis—0
On Significance of Subword tokenization for Low Resource and Efficient Named Entity Recognition: A case study in Marathi—0
On sparse connectivity, adversarial robustness, and a novel model of the artificial neuron—0
On Sparse Gaussian Chain Graph Models—0
On Statistical Rates and Provably Efficient Criteria of Latent Diffusion Transformers (DiTs)—0
On Stochastic Variance Reduced Gradient Method for Semidefinite Optimization—0
On the Calculation of the Variance of Algebraic Variables in Power System Dynamic Models with Stochastic Processes—0
On the Complexity of Neural Computation in Superposition—0
On the Connection Between Diffusion Models and Molecular Dynamics—0
On the consistency theory of high dimensional variable screening—0
On the Effective Horizon of Inverse Reinforcement Learning—0
On the Efficiency of Convolutional Neural Networks—0
On-the-fly spectral unmixing based on Kalman filtering—0
On the Fundamental Limits of Exact Inference in Structured Prediction—0
On the impact of key design aspects in simulated Hybrid Quantum Neural Networks for Earth Observation—0
On the Importance of Feature Separability in Predicting Out-Of-Distribution Error—0
On the Iteration Complexity of Hypergradient Computations—0
On the Post-hoc Explainability of Deep Echo State Networks for Time Series Forecasting, Image and Video Classification—0
On the Power of Decision Trees in Auto-Regressive Language Modeling—0
On the Query Complexity of Verifier-Assisted Language Generation—0
On the Radiality Constraints for Distribution System Restoration and Reconfiguration Problems—0
On the Robustness of Graph Reduction Against GNN Backdoor—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified