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 3401–3425 of 4891 papers

TitleStatusHype
Digital Twin Data Modelling by Randomized Orthogonal Decomposition and Deep Learning—0
Reliability Analysis of Complex Multi-State System Based on Universal Generating Function and Bayesian Network—0
Estimating the Optimal Covariance with Imperfect Mean in Diffusion Probabilistic ModelsCode1
Implicit Regularization or Implicit Conditioning? Exact Risk Trajectories of SGD in High Dimensions—0
Fast and Reliable Evaluation of Adversarial Robustness with Minimum-Margin AttackCode0
SpecNet2: Orthogonalization-free spectral embedding by neural networksCode0
Scalable Exploration for Neural Online Learning to Rank with Perturbed Feedback—0
Stochastic Gradient Descent without Full Data ShuffleCode0
Geometric Policy Iteration for Markov Decision Processes—0
Discovery and density estimation of latent confounders in Bayesian networks with evidence lower bound—0
Reducing Capacity Gap in Knowledge Distillation with Review Mechanism for Crowd CountingCode0
How Much is Enough? A Study on Diffusion Times in Score-based Generative Models—0
Learning Ego 3D Representation as Ray TracingCode1
An efficient semi-supervised quality control system trained using physics-based MRI-artefact generators and adversarial training—0
8-bit Numerical Formats for Deep Neural Networks—0
BInGo: Bayesian Intrinsic Groupwise Registration via Explicit Hierarchical Disentanglement—0
Model-Informed Generative Adversarial Network (MI-GAN) for Learning Optimal Power Flow—0
A Survey on Computationally Efficient Neural Architecture Search—0
Dynamic Cardiac MRI Reconstruction Using Combined Tensor Nuclear Norm and Casorati Matrix Nuclear Norm RegularizationsCode1
Searching for COMETINHO: The Little Metric That Could—0
Hierarchically Constrained Adaptive Ad Exposure in Feeds—0
itKD: Interchange Transfer-based Knowledge Distillation for 3D Object DetectionCode1
Features extraction and reduction techniques with optimized SVM for Persian/Arabic handwritten digits recognitionCode0
GraMeR: Graph Meta Reinforcement Learning for Multi-Objective Influence Maximization—0
Exploring the Open World Using Incremental Extreme Value MachinesCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified