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 1151–1175 of 4891 papers

TitleStatusHype
An Energy-efficient Aerial Backhaul System with Reconfigurable Intelligent Surface—0
ADC/DAC-Free Analog Acceleration of Deep Neural Networks with Frequency Transformation—0
Byzantine-Resilient Distributed P2P Energy Trading via Spatial-Temporal Anomaly Detection—0
An Empirical Study of Training State-of-the-Art LiDAR Segmentation Models—0
Bypassing the Simulator: Near-Optimal Adversarial Linear Contextual Bandits—0
An Empirical Study of Dimensional Reduction Techniques for Facial Action Units Detection—0
Sparse solutions of the kernel herding algorithm by improved gradient approximation—0
Diverse super-resolution with pretrained deep hiererarchical VAEs—0
Burst Image Super-Resolution with Mamba—0
Bunched LPCNet2: Efficient Neural Vocoders Covering Devices from Cloud to Edge—0
Broad Critic Deep Actor Reinforcement Learning for Continuous Control—0
An Empirical Analysis of Speech Self-Supervised Learning at Multiple Resolutions—0
A Dataset Fusion Algorithm for Generalised Anomaly Detection in Homogeneous Periodic Time Series Datasets—0
An Efficient Speech Separation Network Based on Recurrent Fusion Dilated Convolution and Channel Attention—0
Bridging Fairness Gaps: A (Conditional) Distance Covariance Perspective in Fairness Learning—0
An efficient semi-supervised quality control system trained using physics-based MRI-artefact generators and adversarial training—0
Bridging Distributional and Risk-sensitive Reinforcement Learning with Provable Regret Bounds—0
Bridging Autoencoders and Dynamic Mode Decomposition for Reduced-order Modeling and Control of PDEs—0
A Data-Driven Sparse Polynomial Chaos Expansion Method to Assess Probabilistic Total Transfer Capability for Power Systems with Renewables—0
BOTS-LM: Training Large Language Models for Setswana—0
Divide-and-Conquer Strategy for Large-Scale Dynamic Bayesian Network Structure Learning—0
DMRA: An Adaptive Line Spectrum Estimation Method through Dynamical Multi-Resolution of Atoms—0
Breaking the Bias: Recalibrating the Attention of Industrial Anomaly Detection—0
Breaking Boundaries: Balancing Performance and Robustness in Deep Wireless Traffic Forecasting—0
Brain Tumor Diagnosis Using Quantum Convolutional Neural Networks—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified