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 2701–2725 of 4891 papers

TitleStatusHype
Applications of ML-Based Surrogates in Bayesian Approaches to Inverse Problems—0
spateGAN: Spatio-Temporal Downscaling of Rainfall Fields Using a cGAN ApproachCode0
Effortless Cross-Platform Video Codec: A Codebook-Based Method—0
TpopT: Efficient Trainable Template Optimization on Low-Dimensional Manifolds—0
Efficient and Effective Deep Multi-view Subspace Clustering—0
Merging Experts into One: Improving Computational Efficiency of Mixture of ExpertsCode1
Re-initialization-free Level Set Method via Molecular Beam Epitaxy Equation Regularization for Image Segmentation—0
QUIK: Towards End-to-End 4-Bit Inference on Generative Large Language ModelsCode1
Fast Word Error Rate Estimation Using Self-Supervised Representations for Speech and Text—0
QArchSearch: A Scalable Quantum Architecture Search Package—0
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
Federated Fine-Tuning of LLMs on the Very Edge: The Good, the Bad, the Ugly—0
Privacy-preserving Multi-biometric Indexing based on Frequent Binary Patterns—0
Efficient Quantification and Representation of Aggregate Flexibility in Electric VehiclesCode0
xVal: A Continuous Numerical Tokenization for Scientific Language ModelsCode1
Learning to Reach Goals via DiffusionCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified