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 3426–3450 of 4891 papers

TitleStatusHype
Learning to Think: Information-Theoretic Reinforcement Fine-Tuning for LLMs—0
Learning to Upsample and Upmix Audio in the Latent Domain—0
Learning Transferable Friction Models and LuGre Identification via Physics Informed Neural Networks—0
Learning Wake-Sleep Recurrent Attention Models—0
Learning Weather Models from Data with WSINDy—0
Learn to Communicate with Neural Calibration: Scalability and Generalization—0
LEMDA: A Novel Feature Engineering Method for Intrusion Detection in IoT Systems—0
LeMo: Enabling LEss Token Involvement for MOre Context Fine-tuning—0
LeMoLE: LLM-Enhanced Mixture of Linear Experts for Time Series Forecasting—0
LENS-XAI: Redefining Lightweight and Explainable Network Security through Knowledge Distillation and Variational Autoencoders for Scalable Intrusion Detection in Cybersecurity—0
Less Can Be More: Exploring Population Rating Dispositions with Partitioned Models in Recommender Systems—0
Less Discriminatory Alternative and Interpretable XGBoost Framework for Binary Classification—0
Less is More: Efficient Image Vectorization with Adaptive Parameterization—0
Less is More: Efficient Weight Farcasting with 1-Layer Neural Network—0
Less is More: Extreme Gradient Boost Rank-1 Adaption for Efficient Finetuning of LLMs—0
Less is More: Rethinking Few-Shot Learning and Recurrent Neural Nets—0
Less is More: Towards Green Code Large Language Models via Unified Structural Pruning—0
Levels of Integration between Low-Level Reasoning and Task Planning—0
Leveraging Advantages of Interactive and Non-Interactive Models for Vector-Based Cross-Lingual Information Retrieval—0
Leveraging Diffusion Models for Parameterized Quantum Circuit Generation—0
Leveraging GANs For Active Appearance Models Optimized Model Fitting—0
Leveraging Knowledge Distillation for Lightweight Skin Cancer Classification: Balancing Accuracy and Computational Efficiency—0
Leveraging Large Language Models for Medical Information Extraction and Query Generation—0
Leveraging object detection for the identification of lung cancer—0
Leveraging point annotations in segmentation learning with boundary loss—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified