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 1751–1775 of 4891 papers

TitleStatusHype
Clifford Circuits can be Properly PAC Learned if and only if RP=NP—0
Explicifying Neural Implicit Fields for Efficient Dynamic Human Avatar Modeling via a Neural Explicit Surface—0
Exploiting Change Blindness for Video Coding: Perspectives from a Less Promising User Study—0
Adapting Physics-Informed Neural Networks for Bifurcation Detection in Ecological Migration Models—0
Dispatch of Virtual Inertia and Damping: Numerical Method with SDP and ADMM—0
Exploiting Frequency Correlation for Hyperspectral Image Reconstruction—0
Exploiting inter-agent coupling information for efficient reinforcement learning of cooperative LQR—0
AutoSVD++: An Efficient Hybrid Collaborative Filtering Model via Contractive Auto-encoders—0
Federated K-Means Clustering via Dual Decomposition-based Distributed Optimization—0
Federated Learning for Medical Image Classification: A Comprehensive Benchmark—0
Exploring Brain Network Organization in Alzheimer Disease and Frontotemporal Dementia: A Crossplot Transition Entropy Approach—0
A machine learning framework for data driven acceleration of computations of differential equations—0
Discriminative Transfer Learning for General Image Restoration—0
Accelerating Deep Neuroevolution on Distributed FPGAs for Reinforcement Learning Problems—0
Discriminative Shape From Shading in Uncalibrated Illumination—0
Exploring Patterns Behind Sports—0
Discriminative Optimization: Theory and Applications to Point Cloud Registration—0
Clustered Patch Embeddings for Permutation-Invariant Classification of Whole Slide Images—0
Autonomous Wheel Loader Trajectory Tracking Control Using LPV-MPC—0
Exploring the Limits of Differentially Private Deep Learning with Group-wise Clipping—0
Exploring Tokenization Methods for Multitrack Sheet Music Generation—0
Clustering with a Reject Option: Interactive Clustering as Bayesian Prior Elicitation—0
Exploring Winograd Convolution for Cost-effective Neural Network Fault Tolerance—0
Federated Fine-Tuning of LLMs on the Very Edge: The Good, the Bad, the Ugly—0
Discriminative Optimization: Theory and Applications to Computer Vision Problems—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified