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 3576–3600 of 4891 papers

TitleStatusHype
Asynchronous Decentralized Bayesian Optimization for Large Scale Hyperparameter Optimization—0
On the Computational Efficiency of Adaptive and Dynamic Regret Minimization—0
LiteCON: An All-Photonic Neuromorphic Accelerator for Energy-efficient Deep Learning (Preprint)—0
On the Calculation of the Variance of Algebraic Variables in Power System Dynamic Models with Stochastic Processes—0
Two-Stage Neural Contextual Bandits for Personalised News RecommendationCode0
Combining Retrospective Approximation with Importance Sampling for Optimising Conditional Value at Risk—0
Temporal Attention Unit: Towards Efficient Spatiotemporal Predictive Learning—0
TreeDRNet:A Robust Deep Model for Long Term Time Series Forecasting—0
Superadditivity and Convex Optimization for Globally Optimal Cell Segmentation using Deformable Shape ModelsCode0
Efficient fine-grained road segmentation using superpixel-based CNN and CRF models—0
Square One Bias in NLP: Towards a Multi-Dimensional Exploration of the Research Manifold—0
Digital Twin Data Modelling by Randomized Orthogonal Decomposition and Deep Learning—0
Fast Simulation of Particulate Suspensions Enabled by Graph Neural Network—0
Fast and Reliable Evaluation of Adversarial Robustness with Minimum-Margin AttackCode0
Reliability Analysis of Complex Multi-State System Based on Universal Generating Function and Bayesian Network—0
Implicit Regularization or Implicit Conditioning? Exact Risk Trajectories of SGD in High Dimensions—0
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
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
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified