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 761–770 of 4891 papers

TitleStatusHype
Fast and Accurate Entity Recognition with Iterated Dilated ConvolutionsCode1
Fast and Interpretable 2D Homography Decomposition: Similarity-Kernel-Similarity and Affine-Core-Affine TransformationsCode1
ARNN: Attentive Recurrent Neural Network for Multi-channel EEG Signals to Identify Epileptic SeizuresCode1
Calibrating LLMs with Information-Theoretic Evidential Deep LearningCode1
Cached Multi-Lora Composition for Multi-Concept Image GenerationCode1
BUFFER: Balancing Accuracy, Efficiency, and Generalizability in Point Cloud RegistrationCode1
CAMP: Collaborative Attention Model with Profiles for Vehicle Routing ProblemsCode1
Fast Fishing: Approximating BAIT for Efficient and Scalable Deep Active Image ClassificationCode1
Efficient Learning of Mesh-Based Physical Simulation with BSMS-GNNCode1
Improving Computational Efficiency in Visual Reinforcement Learning via Stored EmbeddingsCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified