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 1801–1825 of 4891 papers

TitleStatusHype
American option pricing using generalised stochastic hybrid systems—0
Tiny-Toxic-Detector: A compact transformer-based model for toxic content detection—0
A Minibatch-SGD-Based Learning Meta-Policy for Inventory Systems with Myopic Optimal Policy—0
Gradient-free variational learning with conditional mixture networksCode1
LLaVA-MoD: Making LLaVA Tiny via MoE Knowledge DistillationCode3
The Role of Fibration Symmetries in Geometric Deep Learning—0
A Simple Baseline with Single-encoder for Referring Image Segmentation—0
Learning-Based Adaptive Dynamic Routing with Stability Guarantee for a Single-Origin-Single-Destination Network—0
MSFMamba: Multi-Scale Feature Fusion State Space Model for Multi-Source Remote Sensing Image ClassificationCode1
Research Advances and New Paradigms for Biology-inspired Spiking Neural Networks—0
Step-by-Step Unmasking for Parameter-Efficient Fine-tuning of Large Language ModelsCode0
Quantum-Powered Personalized Learning—0
LowCLIP: Adapting the CLIP Model Architecture for Low-Resource Languages in Multimodal Image Retrieval TaskCode0
3D-RCNet: Learning from Transformer to Build a 3D Relational ConvNet for Hyperspectral Image ClassificationCode2
Batch-FPM: Random batch-update multi-parameter physical Fourier ptychography neural network—0
TraIL-Det: Transformation-Invariant Local Feature Networks for 3D LiDAR Object Detection with Unsupervised Pre-Training—0
FreqINR: Frequency Consistency for Implicit Neural Representation with Adaptive DCT Frequency Loss—0
Prompt-Matcher: Leveraging Large Models to Reduce Uncertainty in Schema Matching Results—0
The Ultimate Guide to Fine-Tuning LLMs from Basics to Breakthroughs: An Exhaustive Review of Technologies, Research, Best Practices, Applied Research Challenges and Opportunities—0
Interpretable breast cancer classification using CNNs on mammographic imagesCode0
Mastering the Digital Art of War: Developing Intelligent Combat Simulation Agents for Wargaming Using Hierarchical Reinforcement Learning—0
Data-Driven Parametrization of Molecular Mechanics Force Fields for Expansive Chemical Space CoverageCode2
GSFusion: Online RGB-D Mapping Where Gaussian Splatting Meets TSDF FusionCode3
Extrinsic Fluctuations in the p53 Cycle—0
Enhancing Transferability of Adversarial Attacks with GE-AdvGAN+: A Comprehensive Framework for Gradient Editing—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified