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Computational Efficiency
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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.
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Large Language Model Guided Self-Debugging Code Generation
Mamba Hawkes Process for Event Sequence Modeling
Fremer: Lightweight and Effective Frequency Transformer for Workload Forecasting in Cloud Services
From Roots to Rewards: Dynamic Tree Reasoning with RL
Computational-Statistical Tradeoffs from NP-hardness
A Framework for Waterfall Pricing Using Simulation-Based Uncertainty Modeling
FourCastNet 3: A geometric approach to probabilistic machine-learning weather forecasting at scale
Heat Kernel Goes Topological
FORTRESS: Function-composition Optimized Real-Time Resilient Structural Segmentation via Kolmogorov-Arnold Enhanced Spatial Attention Networks
DCR: Quantifying Data Contamination in LLMs Evaluation
U-RWKV: Lightweight medical image segmentation with direction-adaptive RWKV
Recursive Bound-Constrained AdaGrad with Applications to Multilevel and Domain Decomposition Minimization
Canonical Bayesian Linear System Identification
Lightweight Model for Poultry Disease Detection from Fecal Images Using Multi-Color Space Feature Optimization and Machine Learning
$I^{2}$-World: Intra-Inter Tokenization for Efficient Dynamic 4D Scene Forecasting
Geo-ORBIT: A Federated Digital Twin Framework for Scene-Adaptive Lane Geometry Detection
Comparative Analysis of Vision Transformers and Traditional Deep Learning Approaches for Automated Pneumonia Detection in Chest X-Rays
Recurrent U-Net-Based Graph Neural Network (RUGNN) for Accurate Deformation Predictions in Sheet Material Forming
Fast Gaussian Processes under Monotonicity Constraints
Hyperspectral Anomaly Detection Methods: A Survey and Comparative Study
Decomposing the Time Series Forecasting Pipeline: A Modular Approach for Time Series Representation, Information Extraction, and Projection
QS4D: Quantization-aware training for efficient hardware deployment of structured state-space sequential models
A Survey on Prompt Tuning
Critical Nodes Identification in Complex Networks: A Survey
Comparative Analysis of CNN and Transformer Architectures with Heart Cycle Normalization for Automated Phonocardiogram Classification
DC-AR: Efficient Masked Autoregressive Image Generation with Deep Compression Hybrid Tokenizer
High Order Collaboration-Oriented Federated Graph Neural Network for Accurate QoS Prediction
MVNet: Hyperspectral Remote Sensing Image Classification Based on Hybrid Mamba-Transformer Vision Backbone Architecture
Generalized Adaptive Transfer Network: Enhancing Transfer Learning in Reinforcement Learning Across Domains
Instant Particle Size Distribution Measurement Using CNNs Trained on Synthetic Data
Improve Underwater Object Detection through YOLOv12 Architecture and Physics-informed Augmentation
FADRM: Fast and Accurate Data Residual Matching for Dataset Distillation
VolumetricSMPL: A Neural Volumetric Body Model for Efficient Interactions, Contacts, and Collisions
LLM-guided Chemical Process Optimization with a Multi-Agent Approach
Antibody Design and Optimization with Multi-scale Equivariant Graph Diffusion Models for Accurate Complex Antigen Binding
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