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 3851–3900 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
Universality of Linear Recurrences Followed by Non-linear Projections: Finite-Width Guarantees and Benefits of Complex Eigenvalues—0
On the workflow, opportunities and challenges of developing foundation model in geophysics—0
On Vision Transformers for Classification Tasks in Side-Scan Sonar Imagery—0
OpenSlot: Mixed Open-Set Recognition with Object-Centric Learning—0
Open-Source Drift Detection Tools in Action: Insights from Two Use Cases—0
OpenSplat3D: Open-Vocabulary 3D Instance Segmentation using Gaussian Splatting—0
Operator Learning for Robust Stabilization of Linear Markov-Jumping Hyperbolic PDEs—0
opp/ai: Optimistic Privacy-Preserving AI on Blockchain—0
OptiGait-LGBM: An Efficient Approach of Gait-based Person Re-identification in Non-Overlapping Regions—0
Optimal Approximate Matrix Multiplication over Sliding Windows—0
Optimal Bounds for Private Minimum Spanning Trees via Input Perturbation—0
Optimal Control for Discrete-Time Systems under Bounded Disturbances—0
Optimal Coupled Sensor Placement and Path-Planning in Unknown Time-Varying Environments—0
Optimal Depth of Neural Networks—0
Optimal Design of Neural Network Structure for Power System Frequency Security Constraints—0
Optimal Distributed Subsampling for Maximum Quasi-Likelihood Estimators with Massive Data—0
Optimal Downsampling for Imbalanced Classification with Generalized Linear Models—0
Optimal Gradient Checkpointing for Sparse and Recurrent Architectures using Off-Chip Memory—0
Optimal Hardening Strategy for Electricity-Hydrogen Networks with Hydrogen Leakage Risk Control against Extreme Weather—0
Optimal Lattice Boltzmann Closures through Multi-Agent Reinforcement Learning—0
Non-iterative generation of an optimal mesh for a blade passage using deep reinforcement learning—0
Optimal Operation of Power Systems with Energy Storage under Uncertainty: A Scenario-based Method with Strategic Sampling—0
Optimal Clustering with Dependent Costs in Bayesian Networks—0
Optimal Sampling Designs for Multi-dimensional Streaming Time Series with Application to Power Grid Sensor Data—0
Optimal Scaling Laws for Efficiency Gains in a Theoretical Transformer-Augmented Sectional MoE Framework—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified