75 citations · 372 across the 57 of their papers we have counts for
8 papers · 1 filter
LQF: Linear Quadratic Fine-Tuning
Alessandro Achille, Aditya Golatkar, Avinash Ravichandran +2
Classifiers that are linear in their parameters, and trained by optimizing a convex loss function, have predictable behavior with respect to changes in the training data, initial c…
Mixed-Privacy Forgetting in Deep Networks
Aditya Golatkar, Alessandro Achille, Avinash Ravichandran +2
We show that the influence of a subset of the training samples can be removed -- or "forgotten" -- from the weights of a network trained on large-scale image classification tasks,…
Usable Information and Evolution of Optimal Representations During Training
Michael Kleinman, Alessandro Achille, Daksh Idnani +1
We introduce a notion of usable information contained in the representation learned by a deep network, and use it to study how optimal representations for the task emerge during tr…
Predicting Training Time Without Training
Luca Zancato, Alessandro Achille, Avinash Ravichandran +2
We tackle the problem of predicting the number of optimization steps that a pre-trained deep network needs to converge to a given value of the loss function. To do so, we leverage…
Adversarial Training Reduces Information and Improves Transferability
Matteo Terzi, Alessandro Achille, Marco Maggipinto +1
Recent results show that features of adversarially trained networks for classification, in addition to being robust, enable desirable properties such as invertibility. The latter p…
LayoutTransformer: Layout Generation and Completion with Self-attention
Kamal Gupta, Justin Lazarow, Alessandro Achille +3
We address the problem of scene layout generation for diverse domains such as images, mobile applications, documents, and 3D objects. Most complex scenes, natural or human-designed…