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Found 23 papers, 4 papers with code
Modeling the Space of Point Landmark Constrained Diffeomorphisms
Based on Teichm\""uller theory, this mapping space is generated by the Beltrami coefficients, which are infinitesimally Teichm\""uller equivalent to $0$.
Voucher Abuse Detection with Prompt-based Fine-tuning on Graph Neural Networks
We design a novel graph prompting function to reformulate the downstream task into a similar template as the pretext task in pre-training, thereby narrowing the objective gap.
Two Heads are Better than One: Towards Better Adversarial Robustness by Combining Transduction and Rejection
Nevertheless, under recent strong adversarial attacks (GMSA, which has been shown to be much more effective than AutoAttack against transduction), Goldwasser et al.'s work was shown to have low performance in a practical deep-learning setting.
Automated Surface Texture Analysis via Discrete Cosine Transform and Discrete Wavelet Transform
Comparing our results with the heuristic threshold selection approach shows good agreement with mean accuracies as high as 95\%.
Towards Evaluating the Robustness of Neural Networks Learned by Transduction
There has been emerging interest in using transductive learning for adversarial robustness (Goldwasser et al., NeurIPS 2020; Wu et al., ICML 2020; Wang et al., ArXiv 2021).
Adversarial Attacks on ML Defense Models Competition
Due to the vulnerability of deep neural networks (DNNs) to adversarial examples, a large number of defense techniques have been proposed to alleviate this problem in recent years.
A New Bidirectional Unsupervised Domain Adaptation Segmentation Framework
Domain shift happens in cross-domain scenarios commonly because of the wide gaps between different domains: when applying a deep learning model well-trained in one domain to another target domain, the model usually performs poorly.
Multi-Stage Graph Peeling Algorithm for Probabilistic Core Decomposition
To make the previous PA focus more on dense subgraphs, we propose a multi-stage graph peeling algorithm (M-PA) that has a two-stage data screening procedure added before the previous PA. After removing vertices from the graph based on the user-defined thresholds, we can reduce the graph complexity largely and without affecting the vertices in subgraphs that we are interested in.
Towards Adversarial Robustness via Transductive Learning
Compared to traditional "test-time" defenses, these defense mechanisms "dynamically retrain" the model based on test time input via transductive learning; and theoretically, attacking these defenses boils down to bilevel optimization, which seems to raise the difficulty for adaptive attacks.
dFDA-VeD: A Dynamic Future Demand Aware Vehicle Dispatching System
As the process should be socially and economically profitable, the task of vehicle dispatching is highly challenging, specially due to the time-varying travel demands and traffic conditions.
Tensor Random Projection for Low Memory Dimension Reduction
The TRP map is formed as the Khatri-Rao product of several smaller random projections, and is compatible with any base random projection including sparse maps, which enable dimension reduction with very low query cost and no floating point operations.
COSINE: A Web Server for Clonal and Subclonal Structure Inference and Evolution in Cancer Genomics
In the past decade, a variety of methods have been developed for subclonal reconstruction using bulk tumor sequencing data.
Cortical Surface Shape Analysis Based on Alexandrov Polyhedra
Shape analysis has been playing an important role in early diagnosis and prognosis of neurodegenerative diseases such as Alzheimer's diseases (AD).
Test-Time Adaptation and Adversarial Robustness
On the positive side, we show that, if one is allowed to access the training data, then Domain Adversarial Neural Networks (${\sf DANN}$), an algorithm designed for unsupervised domain adaptation, can provide nontrivial robustness in the test-time maximin threat model against strong transfer attacks and adaptive fixed point attacks.
A Macro-Micro Weakly-supervised Framework for AS-OCT Tissue Segmentation
Primary angle closure glaucoma (PACG) is the leading cause of irreversible blindness among Asian people.
AE-OT: A NEW GENERATIVE MODEL BASED ON EXTENDED SEMI-DISCRETE OPTIMAL TRANSPORT
In order to tackle the both problems, we explicitly separate the manifold embedding and the optimal transportation; the first part is carried out using an autoencoder to map the images onto the latent space; the second part is accomplished using a GPU-based convex optimization to find the discontinuous transportation maps.
Representation Bayesian Risk Decompositions and Multi-Source Domain Adaptation
Recent studies provide hints and failure examples for domain invariant representation learning, a common approach for this problem, but the explanations provided are somewhat different and do not provide a unified picture.
AE-OT-GAN: Training GANs from data specific latent distribution
Though generative adversarial networks (GANs) areprominent models to generate realistic and crisp images, they often encounter the mode collapse problems and arehard to train, which comes from approximating the intrinsicdiscontinuous distribution transform map with continuousDNNs.
Bayesian Recurrent Framework for Missing Data Imputation and Prediction with Clinical Time Series
Real-world clinical time series data sets exhibit a high prevalence of missing values.
Low-Rank Tucker Approximation of a Tensor From Streaming Data
This paper describes a new algorithm for computing a low-Tucker-rank approximation of a tensor.
Mode Collapse and Regularity of Optimal Transportation Maps
This work builds the connection between the regularity theory of optimal transportation map, Monge-Amp\`{e}re equation and GANs, which gives a theoretic understanding of the major drawbacks of GANs: convergence difficulty and mode collapse.
Latent Space Optimal Transport for Generative Models
Experimental results on an eight-Gaussian dataset show that the proposed OT can handle multi-cluster distributions.
