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 376–400 of 4891 papers

TitleStatusHype
Fast TreeSHAP: Accelerating SHAP Value Computation for TreesCode1
Anchor-based Large Language ModelsCode1
A new computationally efficient algorithm to solve Feature Selection for Functional Data Classification in high-dimensional spacesCode1
Complete Dictionary Learning via _p-norm MaximizationCode1
Comparative Analysis of Demonstration Selection Algorithms for LLM In-Context LearningCode1
Complex Neural Network based Joint AoA and AoD Estimation for Bistatic ISACCode1
GLiNER-BioMed: A Suite of Efficient Models for Open Biomedical Named Entity RecognitionCode1
Fast Point TransformerCode1
FastMap: Fast Queries Initialization Based Vectorized HD Map Reconstruction FrameworkCode1
Contrast-Phys+: Unsupervised and Weakly-supervised Video-based Remote Physiological Measurement via Spatiotemporal ContrastCode1
CondenseNet V2: Sparse Feature Reactivation for Deep NetworksCode1
Automated Lane Merging via Game Theory and Branch Model Predictive ControlCode1
Fast Sequence Based Embedding with Diffusion GraphsCode1
GraphMamba: An Efficient Graph Structure Learning Vision Mamba for Hyperspectral Image ClassificationCode1
Fast Kernel Scene FlowCode1
Cached Multi-Lora Composition for Multi-Concept Image GenerationCode1
An Efficient Memory-Augmented Transformer for Knowledge-Intensive NLP TasksCode1
Adaptive wavelet distillation from neural networks through interpretationsCode1
Consistent Accelerated Inference via Confident Adaptive TransformersCode1
FastKV: KV Cache Compression for Fast Long-Context Processing with Token-Selective PropagationCode1
Context is Gold to find the Gold Passage: Evaluating and Training Contextual Document EmbeddingsCode1
Fast Sequence-Based Embedding with Diffusion GraphsCode1
Prompt Tuned Embedding Classification for Multi-Label Industry Sector AllocationCode1
Federated Bayesian Optimization via Thompson SamplingCode1
Five A^+ Network: You Only Need 9K Parameters for Underwater Image EnhancementCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified