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Found 16 papers, 4 papers with code
Multimodal ChatGPT for Medical Applications: an Experimental Study of GPT-4V
In this paper, we critically evaluate the capabilities of the state-of-the-art multimodal large language model, i. e., GPT-4 with Vision (GPT-4V), on Visual Question Answering (VQA) task.
BiomedGPT: A Unified and Generalist Biomedical Generative Pre-trained Transformer for Vision, Language, and Multimodal Tasks
Conventional task- and modality-specific artificial intelligence (AI) models are inflexible in real-world deployment and maintenance for biomedicine.
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Normative Modeling via Conditional Variational Autoencoder and Adversarial Learning to Identify Brain Dysfunction in Alzheimer's Disease
Normative modeling is an emerging and promising approach to effectively study disorder heterogeneity in individual participants.
Learning Apparent Diffusion Coefficient Maps from Accelerated Radial k-Space Diffusion-Weighted MRI in Mice using a Deep CNN-Transformer Model
Purpose: To accelerate radially sampled diffusion weighted spin-echo (Rad-DW-SE) acquisition method for generating high quality apparent diffusion coefficient (ADC) maps.
AutoADR: Automatic Model Design for Ad Relevance
We add the model designed by AutoADR as a sub-model into the production Ad Relevance model.
Learning Role-based Graph Embeddings
Random walks are at the heart of many existing network embedding methods.
Similarity-based Multi-label Learning
Multi-label classification is an important learning problem with many applications.
Inductive Representation Learning in Large Attributed Graphs
To make these methods more generally applicable, we propose a framework for inductive network representation learning based on the notion of attributed random walk that is not tied to node identity and is instead based on learning a function $\Phi : \mathrm{\rm \bf x} \rightarrow w$ that maps a node attribute vector $\mathrm{\rm \bf x}$ to a type $w$.
A Framework for Generalizing Graph-based Representation Learning Methods
Random walks are at the heart of many existing deep learning algorithms for graph data.
Deep Feature Learning for Graphs
This paper presents a general graph representation learning framework called DeepGL for learning deep node and edge representations from large (attributed) graphs.
Estimation of Graphlet Statistics
In this work, we propose an unbiased graphlet estimation framework that is (a) fast with significant speedups compared to the state-of-the-art, (b) parallel with nearly linear-speedups, (c) accurate with <1% relative error, (d) scalable and space-efficient for massive networks with billions of edges, and (e) flexible for a variety of real-world settings, as well as estimating macro and micro-level graphlet statistics (e. g., counts) of both connected and disconnected graphlets.
Revisiting Role Discovery in Networks: From Node to Edge Roles
The experimental results demonstrate the utility of edge roles for network analysis tasks on a variety of graphs from various problem domains.
Hybrid CPU-GPU Framework for Network Motifs
In this paper, we propose a key class of hybrid parallel graphlet algorithms that leverages multiple CPUs and GPUs simultaneously for computing k-vertex induced subgraph statistics (called graphlets).
Relational Similarity Machines
Despite the importance of relational learning, most existing methods are hard to adapt to different settings, due to issues with efficiency, scalability, accuracy, and flexibility for handling a wide variety of classification problems, data, constraints, and tasks.
On-line Planning and Scheduling: An Application to Controlling Modular Printers
To our knowledge, this work represents the first successful industrial application of embedded domain-independent temporal planning.
Best-First Heuristic Search for Multicore Machines
To harness modern multicore processors, it is imperative to develop parallel versions of fundamental algorithms.
