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Found 22 papers, 12 papers with code
HyperCLOVA X Technical Report
We introduce HyperCLOVA X, a family of large language models (LLMs) tailored to the Korean language and culture, along with competitive capabilities in English, math, and coding.
Efficient Diffusion-Driven Corruption Editor for Test-Time Adaptation
To address this, we propose a novel TTA method by leveraging a latent diffusion model (LDM) based image editing model and fine-tuning it with our newly introduced corruption modeling scheme.
SF(DA)$^2$: Source-free Domain Adaptation Through the Lens of Data Augmentation
We construct an augmentation graph in the feature space of the pretrained model using the neighbor relationships between target features and propose spectral neighborhood clustering to identify partitions in the prediction space.
Entropy is not Enough for Test-Time Adaptation: From the Perspective of Disentangled Factors
To mitigate it, TTA methods have utilized the model output's entropy as a confidence metric that aims to determine which samples have a lower likelihood of causing error.
One-Shot Structure-Aware Stylized Image Synthesis
While GAN-based models have been successful in image stylization tasks, they often struggle with structure preservation while stylizing a wide range of input images.
Gradient Alignment with Prototype Feature for Fully Test-time Adaptation
In context of Test-time Adaptation(TTA), we propose a regularizer, dubbed Gradient Alignment with Prototype feature (GAP), which alleviates the inappropriate guidance from entropy minimization loss from misclassified pseudo label.
Noise Map Guidance: Inversion with Spatial Context for Real Image Editing
Text-guided diffusion models have become a popular tool in image synthesis, known for producing high-quality and diverse images.
Compose and Conquer: Diffusion-Based 3D Depth Aware Composable Image Synthesis
Addressing the limitations of text as a source of accurate layout representation in text-conditional diffusion models, many works incorporate additional signals to condition certain attributes within a generated image.
Subsurface Characterization using Ensemble-based Approaches with Deep Generative Models
WGAN-GP is trained to generate high-dimensional K fields from a low-dimensional latent space and ES-MDA then updates the latent variables by assimilating available measurements.
Confidence Score for Source-Free Unsupervised Domain Adaptation
Unlike existing confidence scores that use only one of the source or target domain knowledge, the JMDS score uses both knowledge.
Deep learning-based fast solver of the shallow water equations
Furthermore, we augment the bathymetry posterior distribution to a more general class of distributions before providing them as inputs to ML algorithm in the second stage.
Variational encoder geostatistical analysis (VEGAS) with an application to large scale riverine bathymetry
Here, we propose a reduced-order model (ROM) based approach that utilizes a variational autoencoder (VAE), a type of deep neural network with a narrow layer in the middle, to compress bathymetry and flow velocity information and accelerate bathymetry inverse problems from flow velocity measurements.
Confidence Score Weighting Adaptation for Source-Free Unsupervised Domain Adaptation
Unsupervised domain adaptation (UDA) aims to achieve high performance within the unlabeled target domain by leveraging the labeled source domain.
6MapNet: Representing soccer players from tracking data by a triplet network
Although the values of individual soccer players have become astronomical, subjective judgments still play a big part in the player analysis.
A framework for data-driven solution and parameter estimation of PDEs using conditional generative adversarial networks
This work is the first to employ and adapt the image-to-image translation concept based on conditional generative adversarial networks (cGAN) towards learning a forward and an inverse solution operator of partial differential equations (PDEs).
Removing Undesirable Feature Contributions Using Out-of-Distribution Data
Herein, we propose a data augmentation method to improve generalization in both adversarial and standard learning by using out-of-distribution (OOD) data that are devoid of the abovementioned issues.
Application of deep learning to large scale riverine flow velocity estimation
First, using the principal component geostatistical approach (PCGA) we estimate the probability density function of the bathymetry from flow velocity measurements, and then we use multiple machine learning algorithms to obtain a fast solver of the SWEs, given augmented realizations from the posterior bathymetry distribution and the prescribed range of BCs.
Fast and Scalable Earth Texture Synthesis using Spatially Assembled Generative Adversarial Neural Networks
The earth texture with complex morphological geometry and compositions such as shale and carbonate rocks, is typically characterized with sparse field samples because of an expensive and time-consuming characterization process.
iCaps: An Interpretable Classifier via Disentangled Capsule Networks
We propose an interpretable Capsule Network, iCaps, for image classification.
Joint Contrastive Learning for Unsupervised Domain Adaptation
Enhancing feature transferability by matching marginal distributions has led to improvements in domain adaptation, although this is at the expense of feature discrimination.
Connectivity-informed Drainage Network Generation using Deep Convolution Generative Adversarial Networks
Stochastic network modeling is often limited by high computational costs to generate a large number of networks enough for meaningful statistical evaluation.
PBBFMM3D: a parallel black-box algorithm for kernel matrix-vector multiplication
We introduce a parallel method that provably requires $O(N)$ operations to reduce the computation cost.
Mathematical Software
