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 2901–2925 of 4891 papers

TitleStatusHype
Diffusion Models, Image Super-Resolution And Everything: A Survey—0
Viz: A QLoRA-based Copyright Marketplace for Legally Compliant Generative AI—0
Explainability-Driven Leaf Disease Classification Using Adversarial Training and Knowledge Distillation—0
Unified Task and Motion Planning using Object-centric Abstractions of Motion Constraints—0
Darwin3: A large-scale neuromorphic chip with a Novel ISA and On-Chip Learning—0
RefineNet: Enhancing Text-to-Image Conversion with High-Resolution and Detail Accuracy through Hierarchical Transformers and Progressive Refinement—0
PanGu-Draw: Advancing Resource-Efficient Text-to-Image Synthesis with Time-Decoupled Training and Reusable Coop-Diffusion—0
Comparative Analysis of Radiomic Features and Gene Expression Profiles in Histopathology Data Using Graph Neural Networks—0
Simultaneous Optimal System and Controller Design for Multibody Systems with Joint Friction using Direct Sensitivities—0
On Robust Wasserstein Barycenter: The Model and Algorithm—0
Finite-Time Frequentist Regret Bounds of Multi-Agent Thompson Sampling on Sparse HypergraphsCode0
Semantic Draw Engineering for Text-to-Image Creation—0
Kernel Heterogeneity Improves Sparseness of Natural Images RepresentationsCode0
Balancing Privacy, Robustness, and Efficiency in Machine Learning—0
Digital twin-assisted three-dimensional electrical capacitance tomography for multiphase flow imaging—0
A Novel ML-driven Test Case Selection Approach for Enhancing the Performance of Grammatical Evolution—0
Machine learning and domain decomposition methods -- a survey—0
ZeroShape: Regression-based Zero-shot Shape ReconstructionCode0
Enhancing Trade-offs in Privacy, Utility, and Computational Efficiency through MUltistage Sampling Technique (MUST)Code0
CRD: Collaborative Representation Distance for Practical Anomaly Detection—0
Optimizing Distributed Training on Frontier for Large Language Models—0
EPNet: An Efficient Pyramid Network for Enhanced Single-Image Super-Resolution with Reduced Computational Requirements—0
Aggregating Multiple Bio-Inspired Image Region Classifiers For Effective And Lightweight Visual Place Recognition—0
Modeling non-linear Effects with Neural Networks in Relational Event ModelsCode0
YOCO: A Hybrid In-Memory Computing Architecture with 8-bit Sub-PetaOps/W In-Situ Multiply Arithmetic for Large-Scale AI—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified