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 2551–2575 of 4891 papers

TitleStatusHype
Residual Bootstrap Exploration for Bandit Algorithms—0
Residual Bootstrap Exploration for Stochastic Linear Bandit—0
Residual Connection-Enhanced ConvLSTM for Lithium Dendrite Growth Prediction—0
Convolutional Neural Network for emotion recognition to assist psychiatrists and psychologists during the COVID-19 pandemic: experts opinion—0
Resolution-Based Distillation for Efficient Histology Image Classification—0
Resona: Improving Context Copying in Linear Recurrence Models with Retrieval—0
Resource Allocation via Model-Free Deep Learning in Free Space Optical Communications—0
ReStoCNet: Residual Stochastic Binary Convolutional Spiking Neural Network for Memory-Efficient Neuromorphic Computing—0
Retentive Decision Transformer with Adaptive Masking for Reinforcement Learning based Recommendation Systems—0
RetGK: Graph Kernels based on Return Probabilities of Random Walks—0
Rethinking and Reweighting the Univariate Losses for Multi-Label Ranking: Consistency and Generalization—0
Rethinking Convolution: Towards an Optimal Efficiency—0
Rethinking Graph Transformer Architecture Design for Node Classification—0
Rethinking Numerical Representations for Deep Neural Networks—0
Rethinking Range-View LiDAR Segmentation in Adverse Weather—0
P-SpikeSSM: Harnessing Probabilistic Spiking State Space Models for Long-Range Dependency Tasks—0
Rethinking Uncertainty Estimation in Natural Language Generation—0
Retrieval Augmentation via User Interest Clustering—0
Retrieval Instead of Fine-tuning: A Retrieval-based Parameter Ensemble for Zero-shot Learning—0
RE-tune: Incremental Fine Tuning of Biomedical Vision-Language Models for Multi-label Chest X-ray Classification—0
ReverBERT: A State Space Model for Efficient Text-Driven Speech Style Transfer—0
Revisiting 2D Convolutional Neural Networks for Graph-based Applications—0
Revisiting CHAMPAGNE: Sparse Bayesian Learning as Reweighted Sparse Coding—0
Revisiting Ensemble Methods for Stock Trading and Crypto Trading Tasks at ACM ICAIF FinRL Contest 2023-2024—0
Revisiting Frank-Wolfe for Structured Nonconvex Optimization—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified