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

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Showing 2201–2250 of 4891 papers

TitleStatusHype
E-MD3C: Taming Masked Diffusion Transformers for Efficient Zero-Shot Object Customization—0
CAM-NET: An AI Model for Whole Atmosphere with Thermosphere and Ionosphere Extension—0
An Enhancement of CNN Algorithm for Rice Leaf Disease Image Classification in Mobile Applications—0
Addressing Delayed Feedback in Conversion Rate Prediction via Influence Functions—0
360-Degree Video Super Resolution and Quality Enhancement Challenge: Methods and Results—0
Embedding Recurrent Layers with Dual-Path Strategy in a Variant of Convolutional Network for Speaker-Independent Speech Separation—0
CAMEL: Curvature-Augmented Manifold Embedding and Learning—0
Embedded Federated Feature Selection with Dynamic Sparse Training: Balancing Accuracy-Cost Tradeoffs—0
ELSA: Efficient Label Shift Adaptation through the Lens of Semiparametric Models—0
An Enhanced Low-Resolution Image Recognition Method for Traffic Environments—0
ELF-VC: Efficient Learned Flexible-Rate Video Coding—0
Electronic excited states from physically-constrained machine learning—0
Electromyography-Based Gesture Recognition: Hierarchical Feature Extraction for Enhanced Spatial-Temporal Dynamics—0
CageViT: Convolutional Activation Guided Efficient Vision Transformer—0
An Energy-efficient Aerial Backhaul System with Reconfigurable Intelligent Surface—0
Electricity Market Forecasting via Low-Rank Multi-Kernel Learning—0
ELECTRA: A Cartesian Network for 3D Charge Density Prediction with Floating Orbitals—0
ELASTIC: Efficient Linear Attention for Sequential Interest Compression—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
Eigenspace Method for Spatiotemporal Hotspot Detection—0
EgoPoser: Robust Real-Time Egocentric Pose Estimation from Sparse and Intermittent Observations Everywhere—0
EGFormer: Towards Efficient and Generalizable Multimodal Semantic Segmentation—0
EFiGP: Eigen-Fourier Physics-Informed Gaussian Process for Inference of Dynamic Systems—0
Bypassing the Simulator: Near-Optimal Adversarial Linear Contextual Bandits—0
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