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Found 50 papers, 15 papers with code
From Tokens to Materials: Leveraging Language Models for Scientific Discovery
Exploring the predictive capabilities of language models in material science is an ongoing interest.
A Benchmark on Directed Graph Representation Learning in Hardware Designs
To keep pace with the rapid advancements in design complexity within modern computing systems, directed graph representation learning (DGRL) has become crucial, particularly for encoding circuit netlists, computational graphs, and developing surrogate models for hardware performance prediction.
DSparsE: Dynamic Sparse Embedding for Knowledge Graph Completion
An ablation study is performed to examine the effects of the dynamic layer and relation-aware layer, where the combined model achieves the best performance.
Predicting Lung Cancer Patient Prognosis with Large Language Models
The experimental results demonstrate that LLMs can achieve competitive, and in some tasks superior, performance in lung cancer prognosis prediction compared to data-driven logistic regression models despite not using additional patient data.
The Power of Combining Data and Knowledge: GPT-4o is an Effective Interpreter of Machine Learning Models in Predicting Lymph Node Metastasis of Lung Cancer
We then designed a prompt template to integrate the patient data with the predicted probability from the machine learning model.
Boundary Detection Algorithm Inspired by Locally Linear Embedding
A practical model for this structure is an embedded compact manifold with boundary.
Zero-shot information extraction from radiological reports using ChatGPT
Information extraction is the strategy to transform the sequence of characters into structured data, which can be employed for secondary analysis.
Probabilistic Neural Transfer Function Estimation with Bayesian System Identification
While deep neural network models have demonstrated excellent power on neural prediction, they usually do not provide the uncertainty of the resulting neural representations and derived statistics, such as the stimuli driving neurons optimally, from in silico experiments.
Air-Ground Integrated Sensing and Communications: Opportunities and Challenges
The air-ground integrated sensing and communications (AG-ISAC) network, which consists of unmanned aerial vehicles (UAVs) and ground terrestrial networks, offers unique capabilities and demands special design techniques.
Improving Zero-Shot Action Recognition using Human Instruction with Text Description
Furthermore, the proposed model can also be combined with other models to improve its accuracy.
Privacy-Preserving Record Linkage for Cardinality Counting
Several applications require counting the number of distinct items in the data, which is known as the cardinality counting problem.
GReS: Graphical Cross-domain Recommendation for Supply Chain Platform
Supply Chain Platforms (SCPs) provide downstream industries with numerous raw materials.
Heterogeneous Graph Tree Networks
Heterogeneous graph neural networks (HGNNs) have attracted increasing research interest in recent three years.
Prediction of protein allosteric signalling pathways and functional residues through paths of optimised propensity
Key residues in both orthosteric and allosteric sites were identified and showed agreement with experimental results, and pivotal signalling residues along the pathway were also revealed, thus providing alternative targets for drug design.
Unsupervised Learning for Combinatorial Optimization with Principled Objective Relaxation
Using machine learning to solve combinatorial optimization (CO) problems is challenging, especially when the data is unlabeled.
Dog nose print matching with dual global descriptor based on Contrastive Learning
These methods generally extract the global features as descriptor to represent the original image.
Snake net and balloon force with a neural network for detecting multiple phases
In order to obtain a physical phase diagram, the snake model with an artificial neural network is applied in an unsupervised learning way by the authors of [Phys. Rev. Lett.
GTNet: A Tree-Based Deep Graph Learning Architecture
We formulate a general propagation rule following the nature of message passing in the tree to update a node's feature by aggregating its initial feature and its neighbor nodes' updated features.
Ranked #50 on
Node Property Prediction
on ogbn-arxiv
Characterizing and overcoming the greedy nature of learning in multi-modal deep neural networks
We propose an algorithm to balance the conditional learning speeds between modalities during training and demonstrate that it indeed addresses the issue of greedy learning.
LOSTIN: Logic Optimization via Spatio-Temporal Information with Hybrid Graph Models
Despite the stride made by machine learning (ML) based performance modeling, two major concerns that may impede production-ready ML applications in EDA are stringent accuracy requirements and generalization capability.
High-Level Synthesis Performance Prediction using GNNs: Benchmarking, Modeling, and Advancing
The contribution of this work is three-fold.
DeepMix: Mobility-aware, Lightweight, and Hybrid 3D Object Detection for Headsets
In this paper, we propose DeepMix, a mobility-aware, lightweight, and hybrid 3D object detection framework for improving the user experience of AR/MR on mobile headsets.
SLAM: A Unified Encoder for Speech and Language Modeling via Speech-Text Joint Pre-Training
We build a single encoder with the BERT objective on unlabeled text together with the w2v-BERT objective on unlabeled speech.
Inferring Manifolds From Noisy Data Using Gaussian Processes
In analyzing complex datasets, it is often of interest to infer lower dimensional structure underlying the higher dimensional observations.
Recognizing and overcoming the greedy nature of learning in multi-modal deep neural networks
We refer to this gain as the conditional utilization rate of the modality.
Program-to-Circuit: Exploiting GNNs for Program Representation and Circuit Translation
Pioneering in this direction, we expect more GNN endeavors to revolutionize this high-demand Program-to-Circuit problem and to enrich the expressiveness of GNNs on programs.
Weakly-supervised High-resolution Segmentation of Mammography Images for Breast Cancer Diagnosis
In cancer diagnosis, interpretability can be achieved by localizing the region of the input image responsible for the output, i. e. the location of a lesion.
A Survey of Machine Learning for Computer Architecture and Systems
Then, we summarize the common problems in computer architecture/system design that can be solved by ML techniques, and the typical ML techniques employed to resolve each of them.
IronMan: GNN-assisted Design Space Exploration in High-Level Synthesis via Reinforcement Learning
Despite the great success of High-Level Synthesis (HLS) tools, we observe several unresolved challenges: 1) the high-level abstraction of programming styles in HLS sometimes conceals optimization opportunities; 2) existing HLS tools do not provide flexible trade-off (Pareto) solutions among different objectives and constraints; 3) the actual quality of the resulting RTL designs is hard to predict.
Eigen-convergence of Gaussian kernelized graph Laplacian by manifold heat interpolation
The result holds for un-normalized and random-walk graph Laplacians when data are uniformly sampled on the manifold, as well as the density-corrected graph Laplacian (where the affinity matrix is normalized by the degree matrix from both sides) with non-uniformly sampled data.
Phoebe: Reuse-Aware Online Caching with Reinforcement Learning for Emerging Storage Models
With data durability, high access speed, low power efficiency and byte addressability, NVMe and SSD, which are acknowledged representatives of emerging storage technologies, have been applied broadly in many areas.
Graph Based Gaussian Processes on Restricted Domains
The GL is constructed from a kernel which depends only on the Euclidean coordinates of the inputs.
Reducing false-positive biopsies with deep neural networks that utilize local and global information in screening mammograms
Breast cancer is the most common cancer in women, and hundreds of thousands of unnecessary biopsies are done around the world at a tremendous cost.
An artificial intelligence system for predicting the deterioration of COVID-19 patients in the emergency department
In order to verify performance in a real clinical setting, we silently deployed a preliminary version of the deep neural network at New York University Langone Health during the first wave of the pandemic, which produced accurate predictions in real-time.
Strong Uniform Consistency with Rates for Kernel Density Estimators with General Kernels on Manifolds
When analyzing modern machine learning algorithms, we may need to handle kernel density estimation (KDE) with intricate kernels that are not designed by the user and might even be irregular and asymmetric.
Data-driven Efficient Solvers for Langevin Dynamics on Manifold in High Dimensions
To construct an efficient and stable approximation for the Langevin dynamics on $\mathcal{N}$, we leverage the corresponding Fokker-Planck equation on the manifold $\mathcal{N}$ in terms of the reaction coordinates $\mathsf{y}$.
The Cost of Privacy in Asynchronous Differentially-Private Machine Learning
The experiments illustrate that collaboration among more than 10 data owners with at least 10, 000 records with privacy budgets greater than or equal to 1 results in a superior machine-learning model in comparison to a model trained in isolation on only one of the datasets, illustrating the value of collaboration and the cost of the privacy.
An interpretable classifier for high-resolution breast cancer screening images utilizing weakly supervised localization
In this work, we extend the globally-aware multiple instance classifier, a framework we proposed to address these unique properties of medical images.
Improving the Ability of Deep Neural Networks to Use Information From Multiple Views in Breast Cancer Screening
In breast cancer screening, radiologists make the diagnosis based on images that are taken from two angles.
Memristor Hardware-Friendly Reinforcement Learning
Namely, neuromorphic architectures that leverage memristors, the programmable and nonvolatile two-terminal devices, as synaptic weights in hardware neural networks, are candidates of choice to realize such highly energy-efficient and complex nervous systems.
Improving localization-based approaches for breast cancer screening exam classification
We trained and evaluated a localization-based deep CNN for breast cancer screening exam classification on over 200, 000 exams (over 1, 000, 000 images).
Screening Mammogram Classification with Prior Exams
Radiologists typically compare a patient's most recent breast cancer screening exam to their previous ones in making informed diagnoses.
The Value of Collaboration in Convex Machine Learning with Differential Privacy
In this paper, we apply machine learning to distributed private data owned by multiple data owners, entities with access to non-overlapping training datasets.
Globally-Aware Multiple Instance Classifier for Breast Cancer Screening
Moreover, both the global structure and local details play important roles in medical image analysis tasks.
Deep Neural Networks Improve Radiologists' Performance in Breast Cancer Screening
We present a deep convolutional neural network for breast cancer screening exam classification, trained and evaluated on over 200, 000 exams (over 1, 000, 000 images).
When Locally Linear Embedding Hits Boundary
The impact of the hyperbolic part of the operator is discussed and we propose a clipped LLE algorithm which is a potential approach to recover the Dirichlet Laplace-Beltrami operator.
Statistics Theory Statistics Theory 62-07
Network Modeling and Pathway Inference from Incomplete Data ("PathInf")
In this work, we developed a network inference method from incomplete data ("PathInf") , as massive and non-uniformly distributed missing values is a common challenge in practical problems.
Breast density classification with deep convolutional neural networks
Breast density classification is an essential part of breast cancer screening.
High-Resolution Breast Cancer Screening with Multi-View Deep Convolutional Neural Networks
In our work, we propose to use a multi-view deep convolutional neural network that handles a set of high-resolution medical images.
