Robot learning in the era of foundation models: A survey
The proliferation of Large Language Models (LLMs) has s fueled a shift in robot learning
from automation towards general embodied Artificial Intelligence (AI). Adopting foundation …
from automation towards general embodied Artificial Intelligence (AI). Adopting foundation …
Causal inference meets deep learning: A comprehensive survey
Deep learning relies on learning from extensive data to generate prediction results. This
approach may inadvertently capture spurious correlations within the data, leading to models …
approach may inadvertently capture spurious correlations within the data, leading to models …
Towards causal foundation model: on duality between causal inference and attention
Foundation models have brought changes to the landscape of machine learning,
demonstrating sparks of human-level intelligence across a diverse array of tasks. However …
demonstrating sparks of human-level intelligence across a diverse array of tasks. However …
Zero-Shot Automatic Annotation and Instance Segmentation using LLM-Generated Datasets: Eliminating Field Imaging and Manual Annotation for Deep Learning …
Currently, deep learning-based instance segmentation for various applications (eg,
Agriculture) is predominantly performed using a labor-intensive process involving extensive …
Agriculture) is predominantly performed using a labor-intensive process involving extensive …
[PDF][PDF] Dynamic inter-treatment information sharing for heterogeneous treatment effects estimation
Existing heterogeneous treatment effects learners, also known as conditional average
treatment effects (CATE) learners, lack a general mechanism for end-to-end inter-treatment …
treatment effects (CATE) learners, lack a general mechanism for end-to-end inter-treatment …
[HTML][HTML] Review of Machine Learning solutions for Eating Disorders
S Ghosh, P Burger, M Simeunovic, J Maas… - International Journal of …, 2024 - Elsevier
Abstract Background Eating Disorders (EDs) are one of the most complex psychiatric
disorders, with significant impairment of psychological and physical health, and …
disorders, with significant impairment of psychological and physical health, and …
Dynamic inter-treatment information sharing for individualized treatment effects estimation
Estimation of individualized treatment effects (ITE) from observational studies is a
fundamental problem in causal inference and holds significant importance across domains …
fundamental problem in causal inference and holds significant importance across domains …
I See, Therefore I Do: Estimating Causal Effects for Image Treatments
A Thorat, R Kolla, N Pedanekar - arXiv preprint arXiv:2412.06810, 2024 - arxiv.org
Causal effect estimation under observational studies is challenging due to the lack of ground
truth data and treatment assignment bias. Though various methods exist in literature for …
truth data and treatment assignment bias. Though various methods exist in literature for …
The Most Disruptive Near-Term Use of AI in Cancer Care: Patient Empowerment Through Software Agents
F Nothaft, B Power - AI in Precision Oncology, 2024 - liebertpub.com
Cancer care often involves making complex medical decisions within a challenging
environment: a balkanized medical system of many specialists, information overload and …
environment: a balkanized medical system of many specialists, information overload and …
Towards Causal Foundation Model: on Duality between Optimal Balancing and Attention
Foundation models have brought changes to the landscape of machine learning,
demonstrating sparks of human-level intelligence across a diverse array of tasks. However …
demonstrating sparks of human-level intelligence across a diverse array of tasks. However …