Search Results for author:
Found 21 papers, 4 papers with code
FreeCam3D: Snapshot Structured Light 3D with Freely-Moving Cameras
The projected pattern can be observed in part or full by any camera, to reconstruct both the 3D map of the scene and the camera pose in the projector coordinates.
Enhancing Fairness through Reweighting: A Path to Attain the Sufficiency Rule
We introduce an innovative approach to enhancing the empirical risk minimization (ERM) process in model training through a refined reweighting scheme of the training data to enhance fairness.
"I Like Sunnie More Than I Expected!": Exploring User Expectation and Perception of an Anthropomorphic LLM-based Conversational Agent for Well-Being Support
The human-computer interaction (HCI) research community has a longstanding interest in exploring the mismatch between users' actual experiences and expectation toward new technologies, for instance, large language models (LLMs).
Discovering Galaxy Features via Dataset Distillation
In many applications, Neural Nets (NNs) have classification performance on par or even exceeding human capacity.
Adversarial Reweighting Guided by Wasserstein Distance for Bias Mitigation
To balance the data distribution between the majority and the minority groups, our approach deemphasizes samples from the majority group.
Causal Fairness-Guided Dataset Reweighting using Neural Networks
The two neural networks can approximate the causal model of the data, and the causal model of interventions.
Counterfactual Explanation for Regression via Disentanglement in Latent Space
In this paper, we introduce a novel method to generate CEs for a pre-trained regressor by first disentangling the label-relevant from the label-irrelevant dimensions in the latent space.
Counterfactual Explanation via Search in Gaussian Mixture Distributed Latent Space
In this paper, we introduce a new method to generate CEs for a pre-trained binary classifier by first shaping the latent space of an autoencoder to be a mixture of Gaussian distributions.
High-Fidelity Image Synthesis from Pulmonary Nodule Lesion Maps using Semantic Diffusion Model
We utilize annotation information from the LUNA16 dataset to create paired CT images and masks, and assess the quality of the generated images using the Frechet Inception Distance (FID), as well as on two common clinical downstream tasks: nodule detection and nodule localization.
Invariant Content Synergistic Learning for Domain Generalization of Medical Image Segmentation
In this paper, we propose a method, named Invariant Content Synergistic Learning (ICSL), to improve the generalization ability of DCNNs on unseen datasets by controlling the inductive bias.
Homogeneous Low-Resolution Face Recognition Method based Correlation Features
Face recognition technology has been widely adopted in many mission-critical scenarios like means of human identification, controlled admission, and mobile device access, etc.
Reinforcement Learning Agent Training with Goals for Real World Tasks
Reinforcement Learning (RL) is a promising approach for solving various control, optimization, and sequential decision making tasks.
NTIRE 2020 Challenge on NonHomogeneous Dehazing
We focus on the proposed solutions and their results evaluated on NH-Haze, a novel dataset consisting of 55 pairs of real haze free and nonhomogeneous hazy images recorded outdoor.
NTIRE 2020 Challenge on Image Demoireing: Methods and Results
Track 1 targeted the single image demoireing problem, which seeks to remove moire patterns from a single image.
PROPS: Probabilistic personalization of black-box sequence models
In particular, we construct a baseline language model by training a LSTM on the entire Wikipedia corpus of 2. 5 million articles (around 6. 6 billion words), and then use PROPS to provide lightweight customization into a personalized language model of President Donald J. Trump's tweeting.
Projecting "better than randomly": How to reduce the dimensionality of very large datasets in a way that outperforms random projections
However, the recent development of randomized principal component analysis (RPCA) has opened up the possibility of obtaining approximate principal components on very large datasets.
Robust Shape Estimation for 3D Deformable Object Manipulation
Existing shape estimation methods for deformable object manipulation suffer from the drawbacks of being off-line, model dependent, noise-sensitive or occlusion-sensitive, and thus are not appropriate for manipulation tasks requiring high precision.
Robotics
On the Information Theoretic Distance Measures and Bidirectional Helmholtz Machines
By establishing a connection between bi-directional Helmholtz machines and information theory, we propose a generalized Helmholtz machine.
Lazy stochastic principal component analysis
Stochastic principal component analysis (SPCA) has become a popular dimensionality reduction strategy for large, high-dimensional datasets.
Generalized Convolutional Neural Networks for Point Cloud Data
The introduction of cheap RGB-D cameras, stereo cameras, and LIDAR devices has given the computer vision community 3D information that conventional RGB cameras cannot provide.
"Influence Sketching": Finding Influential Samples In Large-Scale Regressions
There is an especially strong need in modern large-scale data analysis to prioritize samples for manual inspection.
