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 2711–2720 of 4891 papers

TitleStatusHype
EViT: An Eagle Vision Transformer with Bi-Fovea Self-AttentionCode1
An Edge-Aware Graph Autoencoder Trained on Scale-Imbalanced Data for Traveling Salesman Problems—0
Focus on Local Regions for Query-based Object Detection—0
Realizing Stabilized Landing for Computation-Limited Reusable Rockets: A Quantum Reinforcement Learning Approach—0
A Comparison between Markov Chain and Koopman Operator Based Data-Driven Modeling of Dynamical Systems—0
On Time Domain Conformer Models for Monaural Speech Separation in Noisy Reverberant Acoustic EnvironmentsCode1
Robust-GBDT: GBDT with Nonconvex Loss for Tabular Classification in the Presence of Label Noise and Class ImbalanceCode1
QE-BEV: Query Evolution for Bird's Eye View Object Detection in Varied ContextsCode0
Task Aware Modulation using Representation Learning: An Approach for Few Shot Learning in Environmental Systems—0
Can pruning make Large Language Models more efficient?—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified