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 4676–4700 of 4891 papers

TitleStatusHype
GMFlow: Global Motion-Guided Recurrent Flow for 6D Object Pose Estimation—0
GmNet: Revisiting Gating Mechanisms From A Frequency View—0
GMSA: Enhancing Context Compression via Group Merging and Layer Semantic Alignment—0
GNN-Assisted Phase Space Integration with Application to Atomistics—0
GNN-Based Joint Channel and Power Allocation in Heterogeneous Wireless Networks—0
GNN-enabled Precoding for Massive MIMO LEO Satellite Communications—0
Gnothi Seauton: Empowering Faithful Self-Interpretability in Black-Box Transformers—0
GoRA: Gradient-driven Adaptive Low Rank Adaptation—0
Go Wide, Then Narrow: Efficient Training of Deep Thin Networks—0
GP3: A Sampling-based Analysis Framework for Gaussian Processes—0
GP-Aligner: Unsupervised Non-rigid Groupwise Point Set Registration Based On Optimized Group Latent Descriptor—0
GPAvatar: High-fidelity Head Avatars by Learning Efficient Gaussian Projections—0
Gradient-based Sample Selection for Faster Bayesian Optimization—0
Gradient Boosted Binary Histogram Ensemble for Large-scale Regression—0
Gradient-Descent Based Optimization of Constant Envelope OFDM Waveforms—0
Gradient-Descent Based Optimization of Multi-Tone Sinusoidal Frequency Modulated Waveforms—0
Gradient-EM Bayesian Meta-learning—0
Gradient-Free Textual Inversion—0
Gradient-guided Attention Map Editing: Towards Efficient Contextual Hallucination Mitigation—0
Gradient-Mask Tuning Elevates the Upper Limits of LLM Performance—0
Saliency strikes back: How filtering out high frequencies improves white-box explanations—0
Gram-CTC: Automatic Unit Selection and Target Decomposition for Sequence Labelling—0
GraMeR: Graph Meta Reinforcement Learning for Multi-Objective Influence Maximization—0
Granular Ball Twin Support Vector Machine with Universum Data—0
Graph Coloring: Comparing Cluster Graphs to Factor Graphs—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified