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 2871–2880 of 4891 papers

TitleStatusHype
Identifying and Analyzing Task-Encoding Tokens in Large Language Models—0
I-SplitEE: Image classification in Split Computing DNNs with Early ExitsCode0
Curriculum Recommendations Using Transformer Base Model with InfoNCE Loss And Language Switching Method—0
Inverse analysis of granular flows using differentiable graph neural network simulator—0
Functional Autoencoder for Smoothing and Representation LearningCode0
Input Convex Lipschitz RNN: A Fast and Robust Approach for Engineering TasksCode0
VeCAF: Vision-language Collaborative Active Finetuning with Training Objective Awareness—0
The Chronicles of RAG: The Retriever, the Chunk and the Generator—0
Polariton lattices as binarized neuromorphic networks—0
An ADRC-Incorporated Stochastic Gradient Descent Algorithm for Latent Factor Analysis—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified