A5028001282- Advanced Neural Network Applications
- Advanced Image and Video Retrieval Techniques
- Domain Adaptation and Few-Shot Learning
- Multimodal Machine Learning Applications
- Video Surveillance and Tracking Methods
- Visual Attention and Saliency Detection
- Sexuality, Behavior, and Technology
- Topic Modeling
- Gaze Tracking and Assistive Technology
- Machine Learning in Healthcare
- Advanced Computing and Algorithms
- Suicide and Self-Harm Studies
- Traffic Prediction and Management Techniques
- Data Quality and Management
- Artificial Intelligence in Healthcare and Education
- Child and Adolescent Psychosocial and Emotional Development
- Sexual Differentiation and Disorders
- Eating Disorders and Behaviors
- Explainable Artificial Intelligence (XAI)
- Obsessive-Compulsive Spectrum Disorders
- Recommender Systems and Techniques
- IoT and GPS-based Vehicle Safety Systems
- Traffic and Road Safety
- Human Pose and Action Recognition
- Adversarial Robustness in Machine Learning
Google (United States)
2019-2025
University of Southern Mississippi
2010-2022
The University of Texas at Tyler
2021-2022
Fulbright Canada
2022
University of Alberta
2022
Western University
2022
University of Saskatchewan
2022
Women and Children’s Health Research Institute
2022
NOSM University
2022
Creative Commons
2022
In this paper, we present a new approach to time series forecasting. Time data are prevalent in many scientific and engineering disciplines. forecasting is crucial task modeling data, an important area of machine learning. work developed novel method that employs Transformer-based learning models forecast data. This works by leveraging self-attention mechanisms learn complex patterns dynamics from Moreover, it generic framework can be applied univariate multivariate as well embeddings. Using...
Recent artificial intelligence (AI) systems have reached milestones in "grand challenges" ranging from Go to protein-folding. The capability retrieve medical knowledge, reason over it, and answer questions comparably physicians has long been viewed as one such grand challenge. Large language models (LLMs) catalyzed significant progress question answering; Med-PaLM was the first model exceed a "passing" score US Medical Licensing Examination (USMLE) style with of 67.2% on MedQA dataset....
Collecting labeled data for the task of semantic segmentation is expensive and time-consuming, as it requires dense pixel-level annotations. While recent Convolutional Neural Network (CNN) based approaches have achieved impressive results by using large amounts training data, their performance drops significantly amount decreases. This happens because deep CNNs trained with de facto cross-entropy loss can easily overfit to small data. To address this issue, we propose a simple effective...
BackgroundMedicine is inherently multimodal, requiring the simultaneous interpretation and integration of insights between many data modalities spanning text, imaging, genomics, more. Generalist biomedical artificial intelligence systems that flexibly encode, integrate, interpret these might better enable impactful applications ranging from scientific discovery to care delivery.MethodsTo catalyze development models, we curated MultiMedBench, a new multimodal benchmark. MultiMedBench...
Excellence in a wide variety of medical applications poses considerable challenges for AI, requiring advanced reasoning, access to up-to-date knowledge and understanding complex multimodal data. Gemini models, with strong general capabilities long-context offer exciting possibilities medicine. Building on these core strengths Gemini, we introduce Med-Gemini, family highly capable models that are specialized medicine the ability seamlessly use web search, can be efficiently tailored novel...
Large language models (LLMs) have shown promise in medical question answering, with Med-PaLM being the first to exceed a 'passing' score United States Medical Licensing Examination style questions. However, challenges remain long-form answering and handling real-world workflows. Here, we present 2, which bridges these gaps combination of base LLM improvements, domain fine-tuning new strategies for improving reasoning grounding through ensemble refinement chain retrieval. 2 scores up 86.5% on...
In 1988, the Sexual Addiction Screening Test (SAST) was described as an early assessment tool for clinicians to use with patients who manifested sexually compulsive behavior. Early research acknowledged that while SAST very useful heterosexual males, it did not do well women or homosexual men. There were subsequent efforts create instruments "SAST- like" architecture and Called W-SAST G-SAST, these had little support their clinical utility. This article describes development of a new version...
Standard Knowledge Distillation (KD) approaches distill the knowledge of a cumbersome teacher model into parameters student with pre-defined architecture. However, neural network, which is represented by network's output distribution conditioned on its input, depends not only but also Hence, more generalized approach for KD to teacher's both and architecture student. To achieve this, we present new \textit{Architecture-aware (AKD)} that finds models (pearls teacher) are best distilling given...
This paper studies the problem of novel category discovery on single- and multi-modal data with labels from different but relevant categories. We present a generic, end-to-end framework to jointly learn reliable representation assign clusters unlabelled data. To avoid over-fitting learnt embedding labelled data, we take inspiration self-supervised learning by noise-contrastive estimation extend it handle In particular, propose using discrimination cross-modal augment instance used in...
The task of assigning semantic classes and track identities to every pixel in a video is called panoptic segmentation. Our work the first that targets this real-world setting requiring dense interpretation both spatial temporal domains. As ground-truth for difficult expensive obtain, existing datasets are either constructed synthetically or only sparsely annotated within short clips. To overcome this, we introduce new benchmark encompassing two datasets, KITTI-STEP, MOTChallenge-STEP....
Medicine is inherently multimodal, with rich data modalities spanning text, imaging, genomics, and more. Generalist biomedical artificial intelligence (AI) systems that flexibly encode, integrate, interpret this at scale can potentially enable impactful applications ranging from scientific discovery to care delivery. To the development of these models, we first curate MultiMedBench, a new multimodal benchmark. MultiMedBench encompasses 14 diverse tasks such as medical question answering,...
Video Panoptic Segmentation (VPS) aims to achieve comprehensive pixel-level scene understanding by segmenting all pixels and associating objects in a video. Current solutions can be categorized into online near-online approaches. Evolving over the time, each category has its own specialized designs, making it nontrivial adapt models between different categories. To alleviate discrepancy, this work, we propose unified approach for VPS. The meta architecture of proposed Video-kMaX consists two...
This paper is concerned with ranking many pre-trained deep neural networks (DNNs), called checkpoints, for the transfer learning to a downstream task. Thanks broad use of DNNs, we may easily collect hundreds checkpoints from various sources. Which them transfers best our task interest? Striving answer this question thoroughly, establish checkpoint benchmark (NeuCRaB) and study some intuitive measures. These measures are generic, applying different output types without knowing how on which...
Large language models (LLMs) have revolutionized natural processing. However, effectively incorporating complex and potentially noisy user interaction data remains a challenge. To address this, we propose User-LLM, novel framework that leverages embeddings to contextualize LLMs. These embeddings, distilled from diverse interactions using self-supervised pretraining, capture latent preferences their evolution over time. We integrate these with LLMs through cross-attention soft-prompting,...
Convolution exploits locality for efficiency at a cost of missing long range context. Self-attention has been adopted to augment CNNs with non-local interactions. Recent works prove it possible stack self-attention layers obtain fully attentional network by restricting the attention local region. In this paper, we attempt remove constraint factorizing 2D into two 1D self-attentions. This reduces computation complexity and allows performing within larger or even global companion, also propose...
We study the relative effects of data augmentations, pretraining algorithms, and model architectures in Self-Supervised Learning (SSL). While recent literature this space leaves impression that algorithm is critical importance to performance, understanding its effect complicated by difficulty making objective direct comparisons between methods. propose a new framework which unifies many seemingly disparate SSL methods into single shared template. Using framework, we identify aspects differ...
In this paper we introduce extensions and modifications of the classical degree sequence graphic realization problem studied by Erd\H{o}s-Gallai Havel-Hakimi, as well corresponding connected version. We define joint-degree matrix (resp. graphic) problem, where in addition to sequence, exact number desired edges between vertices different classes is also specified. give necessary sufficient conditions, polynomial time decision construction algorithms for problems. These problems arise...
Abstract Objective Given that the majority of those who die by suicide are male, masculine traits have been examined as a potential link to development capability for suicide. However, research has not if such influence suicidal desire (i.e., thwarted belongingness, perceived burdensomeness). This study stereotypically stoicism, sensation seeking, physical aggression, verbal and self‐reliance on all three components Interpersonal Theory Suicide within sample male female service members....
Mutual gaze detection, i.e., predicting whether or not two people are looking at each other, plays an important role in understanding human interactions. In this work, we focus on the task of image-based mutual and propose a simple effective approach to boost performance by using auxiliary 3D estimation during training phase. We achieve without additional labeling cost branch pseudo labels deduced from labels. By sharing head image encoder between detection branches, better features than...
Abstract Timely interventions and early preparedness of healthcare resources are crucial measures to tackle the COVID-19 disease. To aid these efforts, we developed Mobility-Augmented SEIR model (MA-SEIR) that leverages Google’s aggregate anonymized mobility data augment classic compartmental models. We show in a retrospective analysis how this method can be applied at an stage epidemic forecast its subsequent spread onset different geographic regions, with minimal parameterization model....
Insecure attachment has been associated with short-term mating strategies and higher numbers of extra-dyadic affairs. Research found that insecure is also related to sexual addiction. A sample people seeking treatment for addiction (N = 4,492) was used explore the prevalence styles in such a population, degree which predicts relationship preoccupation, preoccupation isolated pursuits, both measured by SDI-4.0 Preoccupation scales. Preoccupied fearful avoidant were most prevalent this sample....