Latest AI and machine learning research in adhd/add for healthcare professionals.
This technical report introduces our top-ranked solution that employs two approaches, \ie suffix injection and projected gradient descent (PGD) , to address the TiFA workshop MLLM attack challenge. Specifically, we first append the text from an incorrectly labeled option (pseudo-labeled) to the original query as a suffix. Using this modified query, our second approach applies the PGD method to a...
Neurodevelopmental disorders (NDDs) cover a variety of conditions, including autism spectrum disorder, attention-deficit/hyperactivity disorder, and epilepsy, which impair the central and peripheral nervous systems. Their high comorbidity and complex etiologies present significant challenges for accurate diagnosis and effective treatments. Conventional clinical and experimental studies are time-...
Novel-view synthesis is an important problem in computer vision with applications in 3D reconstruction, mixed reality, and robotics. Recent methods ...
Robustness and generalizability in medical image segmentation are often hindered by scarcity and limited diversity of training data, which stands in...
Deep learning models for point clouds have shown to be vulnerable to adversarial attacks, which have received increasing attention in various safety...
View transformation robustness (VTR) is critical for deep-learning-based multi-view 3D object reconstruction models, which indicates the methods' st...
Visual generation has witnessed remarkable progress in single-image tasks, yet extending these capabilities to temporal sequences remains challengin...
Abstract Purpose: High-quality 4D MRI requires an impractically long scanning time for dense k-space signal acquisition covering all respiratory pha...
Imitation learning with a privileged teacher has proven effective for learning complex control behaviors from high-dimensional inputs, such as image...
Human readers can accurately count how many letters are in a word (e.g., 7 in ``buffalo''), remove a letter from a given position (e.g., ``bufflo'')...
Vision-language models have made significant strides recently, demonstrating superior performance across a range of tasks, e.g. optical character re...
We consider the problem of model compression for Large Language Models (LLMs) at post-training time, where the task is to compress a well-trained mo...
We propose a pipeline for gaining insights into complex diseases by training LLMs on challenging social media text data classification tasks, obtain...
Large Language Models are known to capture real-world knowledge, allowing them to excel in many downstream tasks. Despite recent advances, these mod...
With the rapid advancements in Large Language Models (LLMs), LLM-based agents have introduced convenient and user-friendly methods for leveraging to...
Quantization is a critical step to enable efficient LLM serving under limited resource. However, previous research observes that certain weights in ...
We present HumanEdit, a high-quality, human-rewarded dataset specifically designed for instruction-guided image editing, enabling precise and divers...
Recognizing gestures in artworks can add a valuable dimension to art understanding and help to acknowledge the role of the sense of smell in cultura...
Internal features from large-scale pre-trained diffusion models have recently been established as powerful semantic descriptors for a wide range of ...
This paper introduces an innovative approach to Attention-deficit/hyperactivity disorder (ADHD) diagnosis by employing deep learning (DL) techniques...