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 2581–2590 of 4891 papers

TitleStatusHype
Cross-Context Backdoor Attacks against Graph Prompt LearningCode0
Use of Boosting Algorithms in Household-Level Poverty Measurement: A Machine Learning Approach to Predict and Classify Household Wealth Quintiles in the Philippines—0
An Innovative Networks in Federated Learning—0
Combining Off-White and Sparse Black Models in Multi-step Physics-based Systems Identification -- EXTENDED VERSION—0
Performance evaluation of Reddit Comments using Machine Learning and Natural Language Processing methods in Sentiment Analysis—0
Efficient Ensembles Improve Training Data Attribution—0
CLAQ: Pushing the Limits of Low-Bit Post-Training Quantization for LLMsCode0
On Fairness of Low-Rank Adaptation of Large ModelsCode0
Provably Efficient Reinforcement Learning with Multinomial Logit Function Approximation—0
Probabilistic Graph Rewiring via Virtual NodesCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified