MixUp explained as a special adversarial training technique.
problem Improving model robustness against adversarial attacks.
method Introducing directional adversarial training (DAT) and proving MixUp's equivalence to a specific subclass of DAT.
result The family of Untied MixUp schemes is equivalent to the entire class of DAT schemes and can improve upon MixUp.
A scalable method to learn causal graphs from large data.
problem Learning causal graphs from large scale data is challenging.
method Differentiable Adjacency Test (DAT) to evaluate adjacency in causal graphs.
result DAT-Graph can learn graphs of 1000 variables with state-of-the-art accuracy.
DAT-CGAN improves time series generation for better decision support.
problem Generating accurate time series data for decision support.
method DAT-CGAN uses multi-Wasserstein loss and overlapped block-sampling for improved sample efficiency.
result DAT-CGAN outperforms GAN-based baselines in generating data relevant to decision processes.
Survival strategy for crypto firms in bear markets using BTC-to-sats payments rail.
problem Downside risk in crypto reserves during bear markets.
method Conservative treasury policy, operating line monetizing holdings, BTC-to-sats payments rail.
result Sustained mNAV premium through cycles with disclosed KPIs.
Paper proposes MDAT to stabilize domain alignment in label-scarce settings.
problem Stable and comprehensive domain alignment in label-scarce settings.
method Max-margin Domain-Adversarial Training (MDAT) with Adversarial Reconstruction Network (ARN).
result MDAT stabilizes gradient reversing and achieves strong robustness to hyper-parameters.
Model predicts missed doses and targets TB patients more effectively.
problem Improving adherence to TB treatment using digital data.
method Deep learning model trained on 17k TB patients' adherence data.
result Model predicts 21% more patients at risk of missing doses.
Deep neural network classifies DaTscan SPECT images for Parkinson's Disease.
problem Early diagnosis of Parkinson's Disease through objective analysis of SPECT images.
method InceptionV3 architecture with custom binary classifier, 10-fold cross validation.
result Deep neural network achieves high accuracy in classifying DaTscan SPECT images.
We consider the Nordic electricity spot market from mid 1992 to the end of year 2000. This market is found to be well approximated by an anti-persistent self-affine (mean-reverting) walk. It is characterized by a Hurst exponent of H≃0.41 over three orders of magnitude in time ranging from days to years. We argu…
New method predicts Parkinson's using deep neural network latent info.
problem Medical diagnosis of Parkinson's disease.
method Transfer learning, k-means clustering, k-Nearest Neighbour classification of DNN representations.
result Improved prediction of Parkinson's disease based on MRI and DaT Scan data.
Method addresses label shift in adversarial domain adaptation.
problem Label shift in behavioral studies.
method DATS (Domain Adversarial nets for Target Shift) framework.
result DATS framework performs well under large label shift.
Many people are suffering from voice disorders, which can adversely affect the quality of their lives. In response, some researchers have proposed algorithms for automatic assessment of these disorders, based on voice signals. However, these signals can be sensitive to the recording devices. Indeed, the channel effect …
New algorithm improves online decision making by adaptively inferring arm rewards.
problem Adaptive selection of arms in MAB leads to non-iid data, complicating accurate inference.
method Proposes a doubly adaptive TS algorithm that leverages causal inference for adaptive reweighting.
result Demonstrates superior empirical performance in identifying the best arm compared to UCB and TS.