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Found 28 papers, 6 papers with code
System Description on Automatic Simultaneous Translation Workshop
This paper describes our system submitted on the third automatic simultaneous translation workshop at NAACL2022.
Incorporating Domain Differential Equations into Graph Convolutional Networks to Lower Generalization Discrepancy
We theoretically derive conditions where GCNs incorporating such domain differential equations are robust to mismatched training and testing data compared to baseline domain agnostic models.
Robust Conformal Prediction under Distribution Shift via Physics-Informed Structural Causal Model
Uncertainty is critical to reliable decision-making with machine learning.
FourCastNeXt: Optimizing FourCastNet Training for Limited Compute
FourCastNeXt is an optimization of FourCastNet - a global machine learning weather forecasting model - that performs with a comparable level of accuracy and can be trained using around 5% of the original FourCastNet computational requirements.
Distance Guided Generative Adversarial Network for Explainable Binary Classifications
Experimental results show that DisGAN consistently outperforms the GAN-based augmentation methods with explainable binary classification.
A Parameterized Generative Adversarial Network Using Cyclic Projection for Explainable Medical Image Classification
Although current data augmentation methods are successful to alleviate the data insufficiency, conventional augmentation are primarily intra-domain while advanced generative adversarial networks (GANs) generate images remaining uncertain, particularly in small-scale datasets.
Local Convolution Enhanced Global Fourier Neural Operator For Multiscale Dynamic Spaces Prediction
One of the most notable methods is the Fourier Neural Operator (FNO), which is inspired by Green's function method and approximate operator kernel directly in the frequency domain.
Neurogenesis Dynamics-inspired Spiking Neural Network Training Acceleration
Experimental results show that NDSNN achieves up to 20. 52\% improvement in accuracy on Tiny-ImageNet using ResNet-19 (with a sparsity of 99\%) as compared to other SOTA methods (e. g., Lottery Ticket Hypothesis (LTH), SET-SNN, RigL-SNN).
Quantum Deep Hedging
Quantum machine learning has the potential for a transformative impact across industry sectors and in particular in finance.
Around the world in 60 words: A generative vocabulary test for online research
Six behavioral experiments (N=236) in six countries and eight languages show that (a) our test can distinguish between native speakers of closely related languages, (b) the test is reliable ($r=0. 82$), and (c) performance strongly correlates with existing tests (LexTale) and self-reports.
Dynamic Sparse Training via Balancing the Exploration-Exploitation Trade-off
We further design an acquisition function and provide the theoretical guarantees for the proposed method and clarify its convergence property.
3D Reconstruction of Multiple Objects by mmWave Radar on UAV
The radar data is sent to a deep neural network model, which outputs the point cloud reconstruction of the multiple objects in the space.
R2P: A Deep Learning Model from mmWave Radar to Point Cloud
Recent research has shown the effectiveness of mmWave radar sensing for object detection in low visibility environments, which makes it an ideal technique in autonomous navigation systems.
System Identification via Nuclear Norm Regularization
Hankel regularization encourages the low-rankness of the Hankel matrix, which maps to the low-orderness of the system.
Towards Sample-efficient Overparameterized Meta-learning
Specifically, for (1), we first show that learning the optimal representation coincides with the problem of designing a task-aware regularization to promote inductive bias.
DeepPoint: A Deep Learning Model for 3D Reconstruction in Point Clouds via mmWave Radar
Built on our recent proposed 3DRIMR (3D Reconstruction and Imaging via mmWave Radar), we introduce in this paper DeepPoint, a deep learning model that generates 3D objects in point cloud format that significantly outperforms the original 3DRIMR design.
Quantum Machine Learning for Finance
In fact, finance is estimated to be the first industry sector to benefit from Quantum Computing not only in the medium and long terms, but even in the short term.
3DRIMR: 3D Reconstruction and Imaging via mmWave Radar based on Deep Learning
In this paper we propose 3D Reconstruction and Imaging via mmWave Radar (3DRIMR), a deep learning based architecture that reconstructs 3D shape of an object in dense detailed point cloud format, based on sparse raw mmWave radar intensity data.
Sample Efficient Subspace-based Representations for Nonlinear Meta-Learning
We prove that subspace-based representations can be learned in a sample-efficient manner and provably benefit future tasks in terms of sample complexity.
Electronic transport properties and hydrostatic pressure effect of FeSe$_{0.67}$Te$_{0.33}$ single crystals free of phase separation
FeSe$_{1-x}$Te$_{x}$ superconductors manifest some intriguing electronic properties depending on the value of $x$.
Superconductivity Materials Science
A Hierarchical User Intention-Habit Extract Network for Credit Loan Overdue Risk Detection
In this paper, we propose a model named HUIHEN (Hierarchical User Intention-Habit Extract Network) that leverages the users' behavior information in mobile banking APP.
Multi-Site Infant Brain Segmentation Algorithms: The iSeg-2019 Challenge
Deep learning-based methods have achieved state-of-the-art performance; however, one of major limitations is that the learning-based methods may suffer from the multi-site issue, that is, the models trained on a dataset from one site may not be applicable to the datasets acquired from other sites with different imaging protocols/scanners.
Finite Sample System Identification: Improved Rates and the Role of Regularization
This paper studies low-order linear system identification via regularized regression.
ResNeSt: Split-Attention Networks
It is well known that featuremap attention and multi-path representation are important for visual recognition.
Ranked #8 on
Instance Segmentation
on COCO test-dev
(APM metric)
Escaping from saddle points on Riemannian manifolds
We consider minimizing a nonconvex, smooth function $f$ on a Riemannian manifold $\mathcal{M}$.
Transform-Based Multilinear Dynamical System for Tensor Time Series Analysis
We propose a novel multilinear dynamical system (MLDS) in a transform domain, named $\mathcal{L}$-MLDS, to model tensor time series.
Optimize transfer learning for lung diseases in bronchoscopy using a new concept: sequential fine-tuning
To help doctors to be more selective on biopsies and provide a second opinion on diagnosis, in this work, we propose a computer-aided diagnosis (CAD) system for lung diseases including cancers and tuberculosis (TB).
Denoising of 3D magnetic resonance images with multi-channel residual learning of convolutional neural network
Without noise level parameter, our general noise-applicable model is also better than the other compared methods in two datasets.
