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 776–800 of 4891 papers

TitleStatusHype
stream-learn -- open-source Python library for difficult data stream batch analysisCode1
AMR Similarity Metrics from PrinciplesCode1
Fast Sequence-Based Embedding with Diffusion GraphsCode1
The gap between theory and practice in function approximation with deep neural networksCode1
Deep Generalization of Structured Low-Rank Algorithms (Deep-SLR)Code1
Multi-Objective Evolutionary Design of Deep Convolutional Neural Networks for Image ClassificationCode1
Kimera: an Open-Source Library for Real-Time Metric-Semantic Localization and MappingCode1
Improved Techniques for Training Adaptive Deep NetworksCode1
Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional NetworksCode1
Metric-Learning based Deep Hashing Network for Content Based Retrieval of Remote Sensing ImagesCode1
How Can We Be So Dense? The Benefits of Using Highly Sparse RepresentationsCode1
MaCow: Masked Convolutional Generative FlowCode1
Efficient Neural Network Robustness Certification with General Activation FunctionsCode1
Attention U-Net: Learning Where to Look for the PancreasCode1
Fast Sequence Based Embedding with Diffusion GraphsCode1
Simple random search provides a competitive approach to reinforcement learningCode1
Rethinking the Smaller-Norm-Less-Informative Assumption in Channel Pruning of Convolution LayersCode1
featsel: A framework for benchmarking of feature selection algorithms and cost functionsCode1
Fast and Accurate Entity Recognition with Iterated Dilated ConvolutionsCode1
Rethinking the Inception Architecture for Computer VisionCode1
Towards Good Practices for Very Deep Two-Stream ConvNetsCode1
Sparse Projection Oblique Randomer ForestsCode1
Bayesian inference for logistic models using Polya-Gamma latent variablesCode1
Fremer: Lightweight and Effective Frequency Transformer for Workload Forecasting in Cloud Services—0
Computational-Statistical Tradeoffs from NP-hardness—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified