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 4426–4450 of 4891 papers

TitleStatusHype
Surrogate-assisted Bayesian inversion for landscape and basin evolution modelsCode0
Learning Item-Interaction Embeddings for User Recommendations—0
Non-Intrusive Load Monitoring with Fully Convolutional Networks—0
Demystifying excessively volatile human learning: A Bayesian persistent prior and a neural approximation—0
Regret Bounds for Online Portfolio Selection with a Cardinality Constraint—0
Efficient Stochastic Gradient Hard Thresholding—0
PAC-Bayes Tree: weighted subtrees with guarantees—0
Deep Signal Recovery with One-Bit Quantization—0
Iterative Projection and Matching: Finding Structure-preserving Representatives and Its Application to Computer VisionCode0
CCNet: Criss-Cross Attention for Semantic SegmentationCode0
Compact and Efficient Encodings for Planning in Factored State and Action Spaces with Learned Binarized Neural Network Transition Models—0
Machine learning enables long time scale molecular photodynamics simulations—0
Private Selection from Private Candidates—0
Explain to Fix: A Framework to Interpret and Correct DNN Object Detector PredictionsCode0
Skeleton-based Gesture Recognition Using Several Fully Connected Layers with Path Signature Features and Temporal Transformer ModuleCode0
Efficient and Scalable Multi-task Regression on Massive Number of Tasks—0
Deep-learning the Latent Space of Light TransportCode0
Meta-Learning for Multi-objective Reinforcement Learning—0
A simple yet effective baseline for non-attributed graph classificationCode0
Training Domain Specific Models for Energy-Efficient Object DetectionCode0
Learning to Defend by Learning to Attack—0
Efficient Neural Network Robustness Certification with General Activation FunctionsCode1
Efficient Online Hyperparameter Optimization for Kernel Ridge Regression with Applications to Traffic Time Series Prediction—0
Don't forget, there is more than forgetting: new metrics for Continual Learning—0
Batch Normalization Sampling—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified