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 2151–2175 of 4891 papers

TitleStatusHype
OMCAT: Omni Context Aware Transformer—0
Rethinking Graph Transformer Architecture Design for Node Classification—0
A few-shot Label Unlearning in Vertical Federated Learning—0
fastHDMI: Fast Mutual Information Estimation for High-Dimensional Data—0
Echo State Networks for Spatio-Temporal Area-Level Data—0
SGLP: A Similarity Guided Fast Layer Partition Pruning for Compressing Large Deep ModelsCode0
Large Language Model Evaluation via Matrix Nuclear-NormCode0
Ada-K Routing: Boosting the Efficiency of MoE-based LLMs—0
Retrieval Instead of Fine-tuning: A Retrieval-based Parameter Ensemble for Zero-shot Learning—0
Large-scale Multi-objective Feature Selection: A Multi-phase Search Space Shrinking Approach—0
Self-Data Distillation for Recovering Quality in Pruned Large Language Models—0
Dualformer: Controllable Fast and Slow Thinking by Learning with Randomized Reasoning Traces—0
Real-time Monitoring of Lower Limb Movement Resistance Based on Deep Learning—0
WGFormer: An SE(3)-Transformer Driven by Wasserstein Gradient Flows for Molecular Ground-State Conformation Prediction—0
Generalized Group Data Attribution—0
POPoS: Improving Efficient and Robust Facial Landmark Detection with Parallel Optimal Position SearchCode0
COrAL: Order-Agnostic Language Modeling for Efficient Iterative RefinementCode0
On the impact of key design aspects in simulated Hybrid Quantum Neural Networks for Earth Observation—0
Optimal Downsampling for Imbalanced Classification with Generalized Linear Models—0
Balancing Innovation and Privacy: Data Security Strategies in Natural Language Processing Applications—0
pLDDT-Predictor: High-speed Protein Screening Using Transformer and ESM2Code0
Think Beyond Size: Adaptive Prompting for More Effective Reasoning—0
Privately Learning from Graphs with Applications in Fine-tuning Large Language ModelsCode0
Gridded Transformer Neural Processes for Large Unstructured Spatio-Temporal Data—0
Learning Content-Aware Multi-Modal Joint Input Pruning via Bird's-Eye-View Representation—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified