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 1726–1750 of 4891 papers

TitleStatusHype
Detecting Sexism in German Online Newspaper Comments with Open-Source Text Embeddings (Team GDA, GermEval2024 Shared Task 1: GerMS-Detect, Subtasks 1 and 2, Closed Track)Code0
Provably Efficient Infinite-Horizon Average-Reward Reinforcement Learning with Linear Function Approximation—0
Evaluating the Efficacy of Instance Incremental vs. Batch Learning in Delayed Label Environments: An Empirical Study on Tabular Data Streaming for Fraud DetectionCode0
Neuromorphic Spintronics—0
OML-AD: Online Machine Learning for Anomaly Detection in Time Series DataCode0
Scaling Continuous Kernels with Sparse Fourier Domain Learning—0
Multiscale fusion enhanced spiking neural network for invasive BCI neural signal decoding—0
On the limits of agency in agent-based modelsCode4
Thermal Modelling of Battery Cells for Optimal Tab and Surface Cooling Control—0
Convex Reformulation of Information Constrained Linear State Estimation with Mixed-Binary Variables for Outlier Accommodation—0
An Efficient Privacy-aware Split Learning Framework for Satellite Communications—0
Adaptive Sampling for Continuous Group Equivariant Neural Networks—0
Biomimetic Frontend for Differentiable Audio ProcessingCode0
Apollo: Band-sequence Modeling for High-Quality Audio RestorationCode3
Integration of Mamba and Transformer -- MAT for Long-Short Range Time Series Forecasting with Application to Weather Dynamics—0
Integrating Neural Operators with Diffusion Models Improves Spectral Representation in Turbulence ModelingCode2
Rapid Parameter Estimation for Extreme Mass Ratio Inspirals Using Machine Learning—0
In-Situ Fine-Tuning of Wildlife Models in IoT-Enabled Camera Traps for Efficient Adaptation—0
GateAttentionPose: Enhancing Pose Estimation with Agent Attention and Improved Gated Convolutions—0
Mamba for Scalable and Efficient Personalized Recommendations—0
A Continual and Incremental Learning Approach for TinyML On-device Training Using Dataset Distillation and Model Size Adaption—0
Uncertainty Quantification in Seismic Inversion Through Integrated Importance Sampling and Ensemble Methods—0
The Weak Form Is Stronger Than You Think—0
Ferret: Federated Full-Parameter Tuning at Scale for Large Language ModelsCode1
CerviXpert: A Multi-Structural Convolutional Neural Network for Predicting Cervix Type and Cervical Cell Abnormalities—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified