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 2226–2250 of 4891 papers

TitleStatusHype
An Empirical Study of Dimensional Reduction Techniques for Facial Action Units Detection—0
ADC/DAC-Free Analog Acceleration of Deep Neural Networks with Frequency Transformation—0
Effortless Cross-Platform Video Codec: A Codebook-Based Method—0
EffLoc: Lightweight Vision Transformer for Efficient 6-DOF Camera Relocalization—0
Burst Image Super-Resolution with Mamba—0
Efficient Vision-Language Models by Summarizing Visual Tokens into Compact Registers—0
Efficient Video Segmentation Using Parametric Graph Partitioning—0
Bunched LPCNet2: Efficient Neural Vocoders Covering Devices from Cloud to Edge—0
Efficient universal shuffle attack for visual object tracking—0
Efficient Uncertainty Propagation with Guarantees in Wasserstein Distance—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
Efficient Transformed Gaussian Process State-Space Models for Non-Stationary High-Dimensional Dynamical Systems—0
Efficient Transformations in Deep Learning Convolutional Neural Networks—0
Parallel Spiking Unit for Efficient Training of Spiking Neural Networks—0
Efficient Training of Very Deep Neural Networks for Supervised Hashing—0
Bridging Fairness Gaps: A (Conditional) Distance Covariance Perspective in Fairness Learning—0
An Efficient Speech Separation Network Based on Recurrent Fusion Dilated Convolution and Channel Attention—0
Efficient Training of Physics-Informed Neural Networks with Direct Grid Refinement Algorithm—0
Efficient training of physics-informed neural networks via importance sampling—0
Bridging Distributional and Risk-sensitive Reinforcement Learning with Provable Regret Bounds—0
Efficient Training of Neural Stochastic Differential Equations by Matching Finite Dimensional Distributions—0
Efficient Token Mixing for Transformers via Adaptive Fourier Neural Operators—0
Bridging Autoencoders and Dynamic Mode Decomposition for Reduced-order Modeling and Control of PDEs—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified