Search Results for author:
Found 17 papers, 6 papers with code
PromptCharm: Text-to-Image Generation through Multi-modal Prompting and Refinement
However, prompting remains challenging for novice users due to the complexity of the stable diffusion model and the non-trivial efforts required for iteratively editing and refining the text prompts.
Look Before You Leap: An Exploratory Study of Uncertainty Measurement for Large Language Models
In particular, we experiment with twelve uncertainty estimation methods and four LLMs on four prominent natural language processing (NLP) tasks to investigate to what extent uncertainty estimation techniques could help characterize the prediction risks of LLMs.
Benchmarking Robustness of AI-Enabled Multi-sensor Fusion Systems: Challenges and Opportunities
Multi-Sensor Fusion (MSF) based perception systems have been the foundation in supporting many industrial applications and domains, such as self-driving cars, robotic arms, and unmanned aerial vehicles.
Is Model Attention Aligned with Human Attention? An Empirical Study on Large Language Models for Code Generation
Through a quantitative experiment and a user study, we confirmed that, among twelve different attention computation methods, attention computed by the perturbation-based method is most aligned with human attention and is constantly favored by human programmers.
Towards Efficient Deep Hashing Retrieval: Condensing Your Data via Feature-Embedding Matching
The expenses involved in training state-of-the-art deep hashing retrieval models have witnessed an increase due to the adoption of more sophisticated models and large-scale datasets.
DeepSeer: Interactive RNN Explanation and Debugging via State Abstraction
The core of DeepSeer is a state abstraction method that bundles semantically similar hidden states in an RNN model and abstracts the model as a finite state machine.
DeepLens: Interactive Out-of-distribution Data Detection in NLP Models
In this work, we propose DeepLens, an interactive system that helps users detect and explore OOD issues in massive text corpora.
An Exploratory Study of AI System Risk Assessment from the Lens of Data Distribution and Uncertainty
More importantly, it also lacks systematic investigation on how to perform the risk assessment for AI systems from unit level to system level.
AI-driven Mobile Apps: an Explorative Study
In this paper, we conduct the most extensive empirical study on 56, 682 published AI apps from three perspectives: dataset characteristics, development issues, and user feedback and privacy.
Common Corruption Robustness of Point Cloud Detectors: Benchmark and Enhancement
Then, for the first attempt, we construct a benchmark based on the physical-aware common corruptions for point cloud detectors, which contains a total of 1, 122, 150 examples covering 7, 481 scenes, 25 common corruption types, and 6 severities.
Rethinking Unsupervised Domain Adaptation for Semantic Segmentation
Due to its high annotation cost, researchers have developed many UDA methods for semantic segmentation, which assume no labeled sample is available in the target domain.
ArchRepair: Block-Level Architecture-Oriented Repairing for Deep Neural Networks
In this work, as the first attempt, we initiate to repair DNNs by jointly optimizing the architecture and weights at a higher (i. e., block) level.
Cross-Region Domain Adaptation for Class-level Alignment
To cope with this, we propose a method that applies adversarial training to align two feature distributions in the target domain.
Ranked #1 on
Domain Adaptation
on Synscapes-to-Cityscapes
Improved Few-shot Segmentation by Redefinition of the Roles of Multi-level CNN Features
This study is concerned with few-shot segmentation, i. e., segmenting the region of an unseen object class in a query image, given support image(s) of its instances.
CarveNet: Carving Point-Block for Complex 3D Shape Completion
3D point cloud completion is very challenging because it heavily relies on the accurate understanding of the complex 3D shapes (e. g., high-curvature, concave/convex, and hollowed-out 3D shapes) and the unknown & diverse patterns of the partially available point clouds.
Deep Learning Models to Predict Pediatric Asthma Emergency Department Visits
Pediatric asthma is the most prevalent chronic childhood illness, afflicting about 6. 2 million children in the United States.
Supervised Descriptor Learning for Multi-Output Regression
In this paper, we propose a novel supervised descriptor learning (SDL) algorithm to establish a discriminative and compact feature representation for multi-output regression.
