Adversarial robustness of deep autoencoders (AEs) has received less attention than that of discriminative models, although their compressed latent representations induce ill-conditioned mappings that can amplify small input perturbations and destabil...
Decentralized deep learning in PyTorch. Built to train models on thousands of volunteers across the world. (⭐ 2392)
We present the final public data release of the VLT/ISAAC near-infrared imaging survey in the GOODS-South field. The survey covers an area of 172.5, 159.6 and 173.1 arcmin^2 in the J, H, and Ks bands, respectively. For point sources total limiting...
In his second mock of the 2026 NFL Draft, Daniel Jeremiah forecasts the Giants to kick off a receiver run that goes six deep in the first 32 picks. Check out the full Round 1 projection.
Traditional deep learning (DL) models are powerful classifiers, but many approaches do not provide uncertainties for their estimates. Uncertainty quantification (UQ) methods for DL models have received increased attention in the literature due to the...
Survival analysis is a critical tool for modeling time-to-event data. Recent deep learning-based models have reduced various modeling assumptions including proportional hazard and linearity. However, a persistent challenge remains in incorporating lo...
Gregg Rosenthal's ranking of the top 101 free agents of 2026 has arrived! Who are the best available players in this year's class? Which positions appear deepest on the veteran market?
Deep Learning models tend to learn correlations of patterns on huge datasets. The bigger these systems are, the more complex are the phenomena they can detect, and the more data they need for this. The use of Artificial Intelligence (AI) is becoming...
Snyder is named for merchant and buffalo hunter William Henry (Pete) Snyder, who built a trading post on Deep Creek in 1878. It soon drew fellow hunters, and a small settlement grew up around the post.
Judgment prediction for legal cases has attracted much research efforts for its practice use, of which the ultimate goal is prison term prediction. While existing work merely predicts the total prison term, in reality a defendant is often charged wit...
Then you might have missed out on an even deeper look into Aventurine's mindset and perspective during those events. In total, we have 4 main quest parts from his POV: [Double Indemnity](https://honka...
Using machine learning algorithms, including deep learning, we studied the prediction of personal attributes from the text of tweets, such as gender, occupation, and age groups. We applied word2vec to construct word vectors, which were then used to v...
Recent MIDI-to-audio synthesis methods using deep neural networks have successfully generated high-quality, expressive instrumental tracks. However, these methods require MIDI annotations for supervised training, limiting the diversity of instrument...
Content generation and manipulation approaches based on deep learning methods have seen significant advancements, leading to an increased need for techniques to detect whether an image has been generated or edited. Another area of research focuses on...
So what's cooking? What kind of behavioral science are they using to keep people hooked?...
Variational Learning (VL) has recently gained popularity for training deep neural networks. Part of its empirical success can be explained by theories such as PAC-Bayes bounds, minimum description length and marginal likelihood, but little has been d...
Although gradient descent with Polyak's momentum is widely used in modern machine and deep learning, a concrete understanding of its effects on the training trajectory remains elusive. In this work, we empirically show that for linear diagonal networ...
Clair3 - Symphonizing pileup and full-alignment for deep learning-based long-read variant calling (⭐ 342)
Exploration using Seismic Investigations, Geodesy and Heat Transport (InSight) mission was a robotic lander designed to study the deep interior of the
Deep neural networks often produce overconfident predictions, undermining their reliability in safety-critical applications. This miscalibration is further exacerbated under distribution shift, where test data deviates from the training distribution...