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 1021–1030 of 4891 papers

TitleStatusHype
BUZZ: Beehive-structured Sparse KV Cache with Segmented Heavy Hitters for Efficient LLM InferenceCode0
GraphQA: Protein Model Quality Assessment using Graph Convolutional NetworkCode0
GTG-Shapley: Efficient and Accurate Participant Contribution Evaluation in Federated LearningCode0
Graph Neural Networks for modelling breast biomechanical compressionCode0
Bridging Sensor Gaps via Attention Gated Tuning for Hyperspectral Image ClassificationCode0
Graph Degree Linkage: Agglomerative Clustering on a Directed GraphCode0
GraphGAN: Graph Representation Learning with Generative Adversarial NetsCode0
Graph Construction with Flexible Nodes for Traffic Demand PredictionCode0
Graph Convolutional Neural Networks with Diverse Negative Samples via Decomposed Determinant Point ProcessesCode0
Graph Learning from Data under Structural and Laplacian ConstraintsCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified