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 1661–1670 of 4891 papers

TitleStatusHype
Solving High-Dimensional Partial Integral Differential Equations: The Finite Expression Method—0
From Natural Language to SQL: Review of LLM-based Text-to-SQL Systems—0
Graph-Based Representation Learning of Neuronal Dynamics and BehaviorCode0
Denoising VAE as an Explainable Feature Reduction and Diagnostic Pipeline for Autism Based on Resting state fMRI—0
EEG Emotion Copilot: Optimizing Lightweight LLMs for Emotional EEG Interpretation with Assisted Medical Record GenerationCode0
Optimism in the Face of Ambiguity Principle for Multi-Armed Bandits—0
Federated Instruction Tuning of LLMs with Domain Coverage Augmentation—0
Upper and Lower Bounds for Distributionally Robust Off-Dynamics Reinforcement Learning—0
POMONAG: Pareto-Optimal Many-Objective Neural Architecture Generator—0
2D-TPE: Two-Dimensional Positional Encoding Enhances Table Understanding for Large Language ModelsCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified