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 4061–4070 of 4891 papers

TitleStatusHype
Reward-Biased Maximum Likelihood Estimation for Linear Stochastic Bandits—0
SWIFT: Scalable Wasserstein Factorization for Sparse Nonnegative Tensors—0
Model-Free Non-Stationary RL: Near-Optimal Regret and Applications in Multi-Agent RL and Inventory Control—0
Quantifying Statistical Significance of Neural Network-based Image Segmentation by Selective InferenceCode0
Episodic Memory for Learning Subjective-Timescale Models—0
A computationally efficient reconstruction algorithm for circular cone-beam computed tomography using shallow neural networks—0
A Survey on Deep Learning Methods for Semantic Image Segmentation in Real-Time—0
Towards a Systematic Computational Framework for Modeling Multi-Agent Decision-Making at Micro Level for Smart Vehicles in a Smart World—0
Event-Driven Receding Horizon Control for Distributed Estimation in Network Systems—0
Bandit Change-Point Detection for Real-Time Monitoring High-Dimensional Data Under Sampling Control—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified