Latest 15 Papers - May 08, 2025
Latest 15 Papers - May 08, 2025
Differentiable Architecture Search
Differentiable architecture search (DARTS) is a popular approach in neural architecture search (NAS) that uses gradient-based optimization to search for the best neural network architecture. In recent years, DARTS has been widely adopted in various applications, including computer vision, natural language processing, and reinforcement learning. Here are some of the latest papers on DARTS:
Neural Architecture Search
Neural architecture search (NAS) is a subfield of machine learning that focuses on automatically designing and searching for the best neural network architecture. Here are some of the latest papers on NAS:
DARTS
DARTS (Differentiable Architecture Search) is a popular approach in neural architecture search (NAS) that uses gradient-based optimization to search for the best neural network architecture. Here are some of the latest papers on DARTS:
| Title | Date | Comment |
|---|---|---|
| On the structure of (dart, odd hole)-free graphs | 2025-04-29 | |
| Design, Contact Modeling, and Collision-inclusive Planning of a Dual-stiffness Aerial RoboT (DART) | 2025-04-26 | Accep...Accepted for publication at IEEE ICRA 2025 |
| DART: Disease-aware Image-Text Alignment and Self-correcting Re-alignment for Trustworthy Radiology Report Generation | 2025-04-16 | The I...The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2025 |
| dARt Vinci: Egocentric Data Collection for Surgical Robot Learning at Scale | 2025-03-07 |
Q&A: Latest 15 Papers - May 08, 2025
Q: What is Differentiable Architecture Search (DARTS)?
A: Differentiable architecture search (DARTS) is a popular approach in neural architecture search (NAS) that uses gradient-based optimization to search for the best neural network architecture.
Q: What are some of the latest papers on DARTS?
A: Some of the latest papers on DARTS include:
- DNAD: Differentiable Neural Architecture Distillation: This paper proposes a new method for distilling neural network architectures using differentiable neural architecture search.
- FX-DARTS: Designing Topology-unconstrained Architectures with Differentiable Architecture Search and Entropy-based Super-network Shrinking: This paper proposes a new method for designing topology-unconstrained architectures using differentiable architecture search and entropy-based super-network shrinking.
- Regularizing Differentiable Architecture Search with Smooth Activation: This paper proposes a new method for regularizing differentiable architecture search using smooth activation.
Q: What is Neural Architecture Search (NAS)?
A: Neural architecture search (NAS) is a subfield of machine learning that focuses on automatically designing and searching for the best neural network architecture.
Q: What are some of the latest papers on NAS?
A: Some of the latest papers on NAS include:
- ABG-NAS: Adaptive Bayesian Genetic Neural Architecture Search for Graph Representation Learning: This paper proposes a new method for adaptive Bayesian genetic neural architecture search for graph representation learning.
- Llama-Nemotron: Efficient Reasoning Models: This paper proposes a new method for efficient reasoning models using Llama-Nemotron.
- Edge-Cloud Collaborative Computing on Distributed Intelligence and Model Optimization: A Survey: This paper provides a survey on edge-cloud collaborative computing on distributed intelligence and model optimization.
Q: What is DARTS?
A: DARTS (Differentiable Architecture Search) is a popular approach in neural architecture search (NAS) that uses gradient-based optimization to search for the best neural network architecture.
Q: What are some of the latest papers on DARTS?
A: Some of the latest papers on DARTS include:
- On the structure of (dart, odd hole)-free graphs: This paper proposes a new method for studying the structure of (dart, odd hole)-free graphs.
- Design, Contact Modeling, and Collision-inclusive Planning of a Dual-stiffness Aerial RoboT (DART): This paper proposes a new method for designing, contact modeling, and collision-inclusive planning of a dual-stiffness aerial robot (DART).
- DART: Disease-aware Image-Text Alignment and Self-correcting Re-alignment for Trustworthy Radiology Report Generation: This paper proposes a new method for disease-aware image-text alignment and self-correcting re-alignment for trustworthy radiology report generation.
Q: What is the difference between DARTS and NAS?
A: DARTS (Differentiable Architecture Search) is a popular approach in neural architecture search (NAS) that uses gradient-based optimization to search for the best neural network architecture. NAS is a broader field that includes various approaches for searching for the best neural network architecture, including DARTS.
Q: What are some of the applications of DARTS and NAS?
A: DARTS and NAS have various applications in computer vision, natural language processing, and reinforcement learning. Some of the applications include:
- Image classification: DARTS and NAS can be used to search for the best neural network architecture for image classification tasks.
- Object detection: DARTS and NAS can be used to search for the best neural network architecture for object detection tasks.
- Natural language processing: DARTS and NAS can be used to search for the best neural network architecture for natural language processing tasks.
- Reinforcement learning: DARTS and NAS can be used to search for the best neural network architecture for reinforcement learning tasks.
Q: What are some of the challenges in DARTS and NAS?
A: Some of the challenges in DARTS and NAS include:
- Computational cost: DARTS and NAS can be computationally expensive, especially for large-scale neural networks.
- Overfitting: DARTS and NAS can suffer from overfitting, especially when the search space is large.
- Evaluation metrics: DARTS and NAS require careful evaluation metrics to ensure that the searched architecture is optimal.
Q: What are some of the future directions in DARTS and NAS?
A: Some of the future directions in DARTS and NAS include:
- Efficient search methods: Developing efficient search methods that can reduce the computational cost of DARTS and NAS.
- Improved evaluation metrics: Developing improved evaluation metrics that can better capture the performance of DARTS and NAS.
- Applications in other domains: Applying DARTS and NAS to other domains, such as robotics and healthcare.