Technology
Aleatoric and Epistemic Discrimination: Fundamental Limits of Fairness Interventions
We then quantify epistemic discrimination as the gap between a model's accuracy when fairness constraints are
Aleatoric and Epistemic Discrimination: Fundamental Limits of Fairness Interventions
We then quantify epistemic discrimination as the gap between a model's accuracy when fairness constraints are
First-order methods almost always avoid saddle points: The case of vanishing step-sizes
Ioannis Panageas, Georgios Piliouras, Xiao Wang
Neural Information Processing Systems http://nips.cc/
Shapingembodiedagentbehaviorwith activity-contextpriorsfromegocentricvideo
Despite its importance, current approaches do not explicitly model the activity-context.
Learning Action and Reasoning-Centric Image Editing from Videos and Simulations
Object, attribute or stylistic changes can be learned from visually static datasets. On the other hand, high-quality data for action and reasoning-centric edits is scarce and has to come from entirely different sources that cover e.g.
OntheEffectivenessofLipschitz-DrivenRehearsal inContinualLearning-SupplementaryMaterial
If α > β, we are overemphasizing the contribution of the first term of Eq. 9 (which brings each layer'sλk1 andck close toeach other) overthesecond one(which induces small Lipschitz targets).