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 3251–3275 of 4891 papers

TitleStatusHype
Improving Satellite Imagery Masking using Multi-task and Transfer Learning—0
Improving Significant Wave Height Prediction Using Chronos Models—0
Improving Spiking Sparse Recovery via Non-Convex Penalties—0
Improving STDP-based Visual Feature Learning with Whitening—0
Improving the affordability of robustness training for DNNs—0
Improving the Efficiency of Gradient Descent Algorithms Applied to Optimization Problems with Dynamical Constraints—0
Improving trajectory continuity in drone-based crowd monitoring using a set of minimal-cost techniques and deep discriminative correlation filters—0
Improving Vision-based Self-positioning in Intelligent Transportation Systems via Integrated Lane and Vehicle Detection—0
IMTS-Mixer: Mixer-Networks for Irregular Multivariate Time Series Forecasting—0
Incorporating Word Correlation Knowledge into Topic Modeling—0
Incremental Truncated LSTD—0
Indirect Query Bayesian Optimization with Integrated Feedback—0
Inference in Graphical Models via Semidefinite Programming Hierarchies—0
Inference of epidemiological parameters from household stratified data—0
Inference of Haemoglobin Concentration From Stereo RGB—0
InfLocNet: Enhanced Lung Infection Localization and Disease Detection from Chest X-Ray Images Using Lightweight Deep Learning—0
Information Geometry and Beta Link for Optimizing Sparse Variational Student-t Processes—0
InfraLib: Enabling Reinforcement Learning and Decision-Making for Large-Scale Infrastructure Management—0
InftyThink: Breaking the Length Limits of Long-Context Reasoning in Large Language Models—0
Input Switched Affine Networks: An RNN Architecture Designed for Interpretability—0
Insights into DeepSeek-V3: Scaling Challenges and Reflections on Hardware for AI Architectures—0
In-Situ Fine-Tuning of Wildlife Models in IoT-Enabled Camera Traps for Efficient Adaptation—0
Instability, Computational Efficiency and Statistical Accuracy—0
InstaSHAP: Interpretable Additive Models Explain Shapley Values Instantly—0
Integer Echo State Networks: Efficient Reservoir Computing for Digital Hardware—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified