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 1576–1600 of 4891 papers

TitleStatusHype
EPS-MoE: Expert Pipeline Scheduler for Cost-Efficient MoE Inference—0
The Best of Both Worlds: Bridging Quality and Diversity in Data Selection with Bipartite Graph—0
Consistency Calibration: Improving Uncertainty Calibration via Consistency among Perturbed Neighbors—0
Expected Sliced Transport Plans—0
Dual Prototype Evolving for Test-Time Generalization of Vision-Language ModelsCode1
Physical Informed-Inspired Deep Reinforcement Learning Based Bi-Level Programming for Microgrid Scheduling—0
The Moral Case for Using Language Model Agents for Recommendation—0
Beyond Sequence: Impact of Geometric Context for RNA Property Prediction—0
OMCAT: Omni Context Aware Transformer—0
Quadratic Gating Functions in Mixture of Experts: A Statistical Insight—0
Adaptive Data Optimization: Dynamic Sample Selection with Scaling LawsCode1
Rethinking Graph Transformer Architecture Design for Node Classification—0
Holistic Physics Solver: Learning PDEs in a Unified Spectral-Physical Space—0
Improving Bias in Facial Attribute Classification: A Combined Impact of KL Divergence induced Loss Function and Dual Attention—0
SGLP: A Similarity Guided Fast Layer Partition Pruning for Compressing Large Deep ModelsCode0
A few-shot Label Unlearning in Vertical Federated Learning—0
fastHDMI: Fast Mutual Information Estimation for High-Dimensional Data—0
Ada-K Routing: Boosting the Efficiency of MoE-based LLMs—0
Large Language Model Evaluation via Matrix Nuclear-NormCode0
Free Video-LLM: Prompt-guided Visual Perception for Efficient Training-free Video LLMsCode2
α-DPO: Adaptive Reward Margin is What Direct Preference Optimization NeedsCode1
Echo State Networks for Spatio-Temporal Area-Level Data—0
Large-scale Multi-objective Feature Selection: A Multi-phase Search Space Shrinking Approach—0
Real-time Monitoring of Lower Limb Movement Resistance Based on Deep Learning—0
Large Scale Longitudinal Experiments: Estimation and InferenceCode2
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified