27 citations · 58 across the 7 of their papers we have counts for
9 papers
V-CAPE: Visual Consistency Assessment for Product Image Editing
Iaroslav Melekhov, Roberto Alcover Couso, Francesco Taioli +4
Recent advances in generative image editing enable complex transformations such as background replacement, object manipulation, and pose adjustment from natural language instructio…
DiTailed: Ensuring Visual Object Consistency in Text-Image-to-Image Flow Matching Models
Francesco Taioli, Daniel Coelho, Iaroslav Melekhov +4
Despite remarkable progress in text-guided image editing, generative models frequently fail to preserve visual object consistency, defined as the preservation of a subject's key at…
Clustering Context in Off-Policy Evaluation
Daniel Guzman-Olivares, Philipp Schmidt, Jacek Golebiowski +1
Off-policy evaluation can leverage logged data to estimate the effectiveness of new policies in e-commerce, search engines, media streaming services, or automatic diagnostic tools…
Learning Action Embeddings for Off-Policy Evaluation
Matej Cief, Jacek Golebiowski, Philipp Schmidt +2
Off-policy evaluation (OPE) methods allow us to compute the expected reward of a policy by using the logged data collected by a different policy. OPE is a viable alternative to run…
Variational Boosted Soft Trees
Tristan Cinquin, Tammo Rukat, Philipp Schmidt +2
Gradient boosting machines (GBMs) based on decision trees consistently demonstrate state-of-the-art results on regression and classification tasks with tabular data, often outperfo…
Ordering Dimensions with Nested Dropout Normalizing Flows
Artur Bekasov, Iain Murray
The latent space of normalizing flows must be of the same dimensionality as their output space. This constraint presents a problem if we want to learn low-dimensional, semantically…
Cubic-Spline Flows
Conor Durkan, Artur Bekasov, Iain Murray +1
A normalizing flow models a complex probability density as an invertible transformation of a simple density. The invertibility means that we can evaluate densities and generate sam…
Neural Spline Flows
Conor Durkan, Artur Bekasov, Iain Murray +1
A normalizing flow models a complex probability density as an invertible transformation of a simple base density. Flows based on either coupling or autoregressive transforms both o…
Bayesian Adversarial Spheres: Bayesian Inference and Adversarial Examples in a Noiseless Setting
Artur Bekasov, Iain Murray
Modern deep neural network models suffer from adversarial examples, i.e. confidently misclassified points in the input space. It has been shown that Bayesian neural networks are a…