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

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Showing 1091–1100 of 4891 papers

TitleStatusHype
Poseidon: A ViT-based Architecture for Multi-Frame Pose Estimation with Adaptive Frame Weighting and Multi-Scale Feature FusionCode1
Optimizing Language Models for Grammatical Acceptability: A Comparative Study of Fine-Tuning Techniques—0
Black-box Optimization with Simultaneous Statistical Inference for Optimal Performance—0
A Critical Synthesis of Uncertainty Quantification and Foundation Models in Monocular Depth Estimation—0
Robust Hyperspectral Image Panshapring via Sparse Spatial-Spectral Representation—0
Transforming Indoor Localization: Advanced Transformer Architecture for NLOS Dominated Wireless Environments with Distributed Sensors—0
An Enhanced Zeroth-Order Stochastic Frank-Wolfe Framework for Constrained Finite-Sum OptimizationCode0
AlphaNet: Scaling Up Local-frame-based Atomistic Interatomic PotentialCode2
UNetVL: Enhancing 3D Medical Image Segmentation with Chebyshev KAN Powered Vision-LSTMCode0
QuantuneV2: Compiler-Based Local Metric-Driven Mixed Precision Quantization for Practical Embedded AI Applications—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified