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 4101–4125 of 4891 papers

TitleStatusHype
Augmented Sliced Wasserstein DistancesCode1
Faster Wasserstein Distance Estimation with the Sinkhorn Divergence—0
Sparsity Turns Adversarial: Energy and Latency Attacks on Deep Neural Networks—0
GP3: A Sampling-based Analysis Framework for Gaussian Processes—0
Fast Maximum Likelihood Estimation and Supervised Classification for the Beta-Liouville Multinomial—0
AlgebraNetsCode0
Combining the band-limited parameterization and Semi-Lagrangian Runge--Kutta integration for efficient PDE-constrained LDDMM—0
Exploring Quality and Generalizability in Parameterized Neural Audio EffectsCode1
Neural Network Activation Quantization with Bitwise Information BottlenecksCode0
GAP++: Learning to generate target-conditioned adversarial examples—0
Smart Forgetting for Safe Online Learning with Gaussian Processes—0
Learning Mixtures of Random Utility Models with Features from Incomplete Preferences—0
Uncertainty-Aware CNNs for Depth Completion: Uncertainty from Beginning to EndCode1
Look Locally Infer Globally: A Generalizable Face Anti-Spoofing Approach—0
Anomaly Detection with Tensor Networks—0
Quantifying the Uncertainty in Model Parameters Using Gaussian Process-Based Markov Chain Monte Carlo: An Application to Cardiac Electrophysiological Models—0
On scenario construction for stochastic shortest path problems in real road networks—0
Learning to Generate 3D Training Data Through Hybrid Gradient—0
Overcoming Multi-Model Forgetting in One-Shot NAS With Diversity MaximizationCode1
Instability, Computational Efficiency and Statistical Accuracy—0
Beyond the Mean-Field: Structured Deep Gaussian Processes Improve the Predictive UncertaintiesCode0
Optimal Distributed Subsampling for Maximum Quasi-Likelihood Estimators with Massive Data—0
MOTS: Multiple Object Tracking for General Categories Based On Few-Shot Method—0
An Efficient Machine-Learning Approach for PDF Tabulation in Turbulent Combustion Closure—0
Improved Protein-ligand Binding Affinity Prediction with Structure-Based Deep Fusion InferenceCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified