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 3401–3450 of 4891 papers

TitleStatusHype
Learning Pore-scale Multi-phase Flow from Experimental Data with Graph Neural Network—0
Learning Positive Functions with Pseudo Mirror Descent—0
Learning Real-World Action-Video Dynamics with Heterogeneous Masked Autoregression—0
Learning Representation for Multitask learning through Self Supervised Auxiliary learning—0
Learning Routines for Effective Off-Policy Reinforcement Learning—0
Learning Stochastic Parametric Differentiable Predictive Control Policies—0
Learning Summary Statistic for Approximate Bayesian Computation via Deep Neural Network—0
Learning Symbolic Persistent Macro-Actions for POMDP Solving Over Time—0
Learning the Update Operator for 2D/3D Image Registration—0
Learning to Control the Smoothness of Graph Convolutional Network Features—0
Learning to Defend by Learning to Attack—0
Learning-to-Defer for Extractive Question Answering—0
Learning to Exploit Invariances in Clinical Time-Series Data using Sequence Transformer Networks—0
Learning to Generate 3D Training Data Through Hybrid Gradient—0
Learning to Generate Content-Aware Dynamic Detectors—0
Learning to generate physical ocean states: Towards hybrid climate modeling—0
Learning to Generate Synthetic 3D Training Data through Hybrid Gradient—0
Maximizing Influence with Graph Neural Networks—0
Learning to Minimize Cost-to-Serve for Multi-Node Multi-Product Order Fulfilment in Electronic Commerce—0
Learning to Optimize in Model Predictive Control—0
Learning to Optimize Permutation Flow Shop Scheduling via Graph-based Imitation Learning—0
Learning to sample in Cartesian MRI—0
Learning to Search Efficiently in High Dimensions—0
Learning to Search for Vehicle Routing with Multiple Time Windows—0
Learning to Speed Up Query Planning in Graph Databases—0
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