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 1401–1425 of 4891 papers

TitleStatusHype
Fixed-Mean Gaussian Processes for Post-hoc Bayesian Deep LearningCode0
Deep autoregressive neural networks for high-dimensional inverse problems in groundwater contaminant source identificationCode0
A Hybrid Framework for Reinsurance Optimization: Integrating Generative Models and Reinforcement LearningCode0
A Survey on Prompt TuningCode0
First Exit Time Analysis of Stochastic Gradient Descent Under Heavy-Tailed Gradient NoiseCode0
GAMMA: A General Agent Motion Model for Autonomous DrivingCode0
Graph Construction with Flexible Nodes for Traffic Demand PredictionCode0
A Survey on Large-scale Machine LearningCode0
Decomposing the Time Series Forecasting Pipeline: A Modular Approach for Time Series Representation, Information Extraction, and ProjectionCode0
A Cascaded Dilated Convolution Approach for Mpox Lesion ClassificationCode0
Finding Influential Training Samples for Gradient Boosted Decision TreesCode0
QE-BEV: Query Evolution for Bird's Eye View Object Detection in Varied ContextsCode0
Dynamic Bi-Elman Attention Networks: A Dual-Directional Context-Aware Test-Time Learning for Text ClassificationCode0
Decomposing and Fusing Intra- and Inter-Sensor Spatio-Temporal Signal for Multi-Sensor Wearable Human Activity RecognitionCode0
DecomCAM: Advancing Beyond Saliency Maps through Decomposition and IntegrationCode0
Fighting Randomness with Randomness: Mitigating Optimisation Instability of Fine-Tuning using Delayed Ensemble and Noisy InterpolationCode0
Filtered Markovian Projection: Dimensionality Reduction in Filtering for Stochastic Reaction NetworksCode0
Decentralized and Lifelong-Adaptive Multi-Agent Collaborative LearningCode0
Efficient Training of Probabilistic Neural Networks for Survival AnalysisCode0
MEMC-Net: Motion Estimation and Motion Compensation Driven Neural Network for Video Frame Interpolation and EnhancementCode0
Debiasing Evidence Approximations: On Importance-weighted Autoencoders and Jackknife Variational InferenceCode0
Feedback Gradient Descent: Efficient and Stable Optimization with Orthogonality for DNNsCode0
Feed-Forward Optimization With Delayed Feedback for Neural NetworksCode0
Few-Shot Image-to-Semantics Translation for Policy Transfer in Reinforcement LearningCode0
DeBaCl: A Python Package for Interactive DEnsity-BAsed CLusteringCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified