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 2876–2900 of 4891 papers

TitleStatusHype
Input Convex Lipschitz RNN: A Fast and Robust Approach for Engineering TasksCode0
VeCAF: Vision-language Collaborative Active Finetuning with Training Objective Awareness—0
The Chronicles of RAG: The Retriever, the Chunk and the Generator—0
Polariton lattices as binarized neuromorphic networks—0
An ADRC-Incorporated Stochastic Gradient Descent Algorithm for Latent Factor Analysis—0
Image edge enhancement for effective image classification—0
Data-Efficient Interactive Multi-Objective Optimization Using ParEGO—0
A Physics-informed machine learning model for time-dependent wave runup prediction—0
Plug-in for visualizing 3D tool tracking from videos of Minimally Invasive Surgeries—0
Maximum-Entropy Adversarial Audio Augmentation for Keyword Spotting—0
A Lightweight Feature Fusion Architecture For Resource-Constrained Crowd Counting—0
Reliability Analysis of Complex Systems using Subset Simulations with Hamiltonian Neural Networks—0
Feature Network Methods in Machine Learning and Applications—0
DiffSHEG: A Diffusion-Based Approach for Real-Time Speech-driven Holistic 3D Expression and Gesture Generation—0
FlightLLM: Efficient Large Language Model Inference with a Complete Mapping Flow on FPGAs—0
SeTformer is What You Need for Vision and Language—0
Model-Agnostic Interpretation Framework in Machine Learning: A Comparative Study in NBA Sports—0
Predicting Traffic Flow with Federated Learning and Graph Neural with Asynchronous Computations Network—0
Migrating Birds Optimization-Based Feature Selection for Text Classification—0
Kernel-U-Net: Multivariate Time Series Forecasting using Custom Kernels—0
Efficient Parallel Audio Generation using Group Masked Language Modeling—0
SpecFormer: Guarding Vision Transformer Robustness via Maximum Singular Value Penalization—0
Structured Model Probing: Empowering Efficient Transfer Learning by Structured Regularization—0
Patch2Self2: Self-supervised Denoising on Coresets via Matrix Sketching—0
PromptCoT: Align Prompt Distribution via Adapted Chain-of-Thought—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified