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Found 12 papers, 3 papers with code
Opportunities and challenges in the application of large artificial intelligence models in radiology
Influenced by ChatGPT, artificial intelligence (AI) large models have witnessed a global upsurge in large model research and development.
Integrating Active Learning in Causal Inference with Interference: A Novel Approach in Online Experiments
In the domain of causal inference research, the prevalent potential outcomes framework, notably the Rubin Causal Model (RCM), often overlooks individual interference and assumes independent treatment effects.
HiDiffusion: Unlocking High-Resolution Creativity and Efficiency in Low-Resolution Trained Diffusion Models
We introduce HiDiffusion, a tuning-free framework comprised of Resolution-Aware U-Net (RAU-Net) and Modified Shifted Window Multi-head Self-Attention (MSW-MSA) to enable pretrained large text-to-image diffusion models to efficiently generate high-resolution images (e. g. 1024$\times$1024) that surpass the training image resolution.
Measurement of In-Circuit Common-Mode Impedance at the AC Input of a Motor Drive System
The in-circuit common-mode (CM) impedance at the AC input of a motor drive system (MDS) provides valuable inputs for evaluating and estimating the CM electromagnetic interference (EMI) noise generated by the switching of power semiconductor devices in the MDS.
Impact of Motor Stator Winding Faults on Common-Mode Current
This paper investigates the influence of different motor stator failures on the common-mode (CM) current.
In-Circuit Differential-Mode Impedance Extraction at the AC Input of a Motor Drive System
The in-circuit differential-mode (DM) impedance at the AC input of a motor drive system (MDS) serves as a key parameter to evaluate and estimate the DM electromagnetic interference (EMI) noise caused by the switching of power semiconductor devices in the MDS.
Robust Machine Reading Comprehension by Learning Soft labels
Neural models have achieved great success on the task of machine reading comprehension (MRC), which are typically trained on hard labels.
Feature Selection Methods for Uplift Modeling and Heterogeneous Treatment Effect
Uplift modeling is a causal learning technique that estimates subgroup-level treatment effects.
CausalML: Python Package for Causal Machine Learning
CausalML is a Python implementation of algorithms related to causal inference and machine learning.
Maximum Relevance and Minimum Redundancy Feature Selection Methods for a Marketing Machine Learning Platform
This paper describes the approach to extend, evaluate, and implement the mRMR feature selection methods for classification problem in a marketing machine learning platform at Uber that automates creation and deployment of targeting and personalization models at scale.
Uplift Modeling for Multiple Treatments with Cost Optimization
An important but so far neglected use case for uplift modeling is an experiment with multiple treatment groups that have different costs, such as for example when different communication channels and promotion types are tested simultaneously.
Relaxed Majorization-Minimization for Non-smooth and Non-convex Optimization
We propose a new majorization-minimization (MM) method for non-smooth and non-convex programs, which is general enough to include the existing MM methods.
