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 2491–2500 of 4891 papers

TitleStatusHype
A Fast Effective Greedy Approach for MU-MIMO Beam Selection in mm-Wave and THz Communications—0
immrax: A Parallelizable and Differentiable Toolbox for Interval Analysis and Mixed Monotone Reachability in JAXCode1
Identifying and Analyzing Task-Encoding Tokens in Large Language Models—0
Efficient Data Shapley for Weighted Nearest Neighbor Algorithms—0
I-SplitEE: Image classification in Split Computing DNNs with Early ExitsCode0
VMamba: Visual State Space ModelCode7
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
RWKV-TS: Beyond Traditional Recurrent Neural Network for Time Series TasksCode2
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified