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 171–180 of 4891 papers

TitleStatusHype
Context is Gold to find the Gold Passage: Evaluating and Training Contextual Document EmbeddingsCode1
Hyperbolic Dataset Distillation—0
HLSAD: Hodge Laplacian-based Simplicial Anomaly Detection—0
LLaMA-XR: A Novel Framework for Radiology Report Generation using LLaMA and QLoRA Fine Tuning—0
Evaluating the Efficacy of LLM-Based Reasoning for Multiobjective HPC Job Scheduling—0
A New Deep-learning-Based Approach For mRNA Optimization: High Fidelity, Computation Efficiency, and Multiple Optimization FactorsCode0
Learning to Search for Vehicle Routing with Multiple Time Windows—0
DeepRTE: Pre-trained Attention-based Neural Network for Radiative TranferCode0
Learning Interpretable Differentiable Logic Networks for Tabular Regression—0
CURVE: CLIP-Utilized Reinforcement Learning for Visual Image Enhancement via Simple Image Processing—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified