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 1701–1725 of 4891 papers

TitleStatusHype
Sparse L0-norm based Kernel-free Quadratic Surface Support Vector MachinesCode0
GCSAM: Gradient Centralized Sharpness Aware MinimizationCode0
Question-to-Question Retrieval for Hallucination-Free Knowledge Access: An Approach for Wikipedia and Wikidata Question Answering—0
Leveraging GANs For Active Appearance Models Optimized Model Fitting—0
Decomposing and Fusing Intra- and Inter-Sensor Spatio-Temporal Signal for Multi-Sensor Wearable Human Activity RecognitionCode0
Recovering Unobserved Network Links from Aggregated Relational Data: Discussions on Bayesian Latent Surface Modeling and Penalized Regression—0
Computational Discovery of Chiasmus in Ancient Religious TextCode0
Which price to pay? Auto-tuning building MPC controller for optimal economic cost—0
BOOST: Microgrid Sizing using Ordinal Optimization—0
Revisiting Ensemble Methods for Stock Trading and Crypto Trading Tasks at ACM ICAIF FinRL Contest 2023-2024—0
HOPS: High-order Polynomials with Self-supervised Dimension Reduction for Load Forecasting—0
Wasserstein Adaptive Value Estimation for Actor-Critic Reinforcement Learning—0
OMoE: Diversifying Mixture of Low-Rank Adaptation by Orthogonal Finetuning—0
Analytical Models of Frequency and Voltage in Large-Scale All-Inverter Power Systems—0
Adaptive Clustering for Efficient Phenotype Segmentation of UAV Hyperspectral Data—0
DPERC: Direct Parameter Estimation for Mixed Data—0
Deep Learning for Early Alzheimer Disease Detection with MRI ScansCode0
Graph Neural Networks for Travel Distance Estimation and Route Recommendation Under Probabilistic Hazards—0
Parallel multi-objective metaheuristics for smart communications in vehicular networks—0
Cancer-Net PCa-Seg: Benchmarking Deep Learning Models for Prostate Cancer Segmentation Using Synthetic Correlated Diffusion Imaging—0
Trusted Machine Learning Models Unlock Private Inference for Problems Currently Infeasible with Cryptography—0
Towards Lightweight and Stable Zero-shot TTS with Self-distilled Representation Disentanglement—0
Learning Joint Denoising, Demosaicing, and Compression from the Raw Natural Image Noise DatasetCode0
SPEQ: Stabilization Phases for Efficient Q-Learning in High Update-To-Data Ratio Reinforcement Learning—0
LeMo: Enabling LEss Token Involvement for MOre Context Fine-tuning—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified