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 1281–1290 of 4891 papers

TitleStatusHype
Test-time Adaptation for Foundation Medical Segmentation Model without Parametric Updates—0
Revisiting Funnel Transformers for Modern LLM Architectures with Comprehensive Ablations in Training and Inference Configurations—0
Overcoming Vocabulary Constraints with Pixel-level Fallback—0
LLMPi: Optimizing LLMs for High-Throughput on Raspberry Pi—0
Robust Channel Estimation for Optical Wireless Communications Using Neural NetworkCode0
Is Temporal Prompting All We Need For Limited Labeled Action Recognition?—0
High Dimensional Bayesian Optimization using Lasso Variable SelectionCode0
Dynamic Graph Structure Estimation for Learning Multivariate Point Process using Spiking Neural Networks—0
DynMoLE: Boosting Mixture of LoRA Experts Fine-Tuning with a Hybrid Routing MechanismCode0
Benchmarking Federated Machine Unlearning methods for Tabular Data—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified