Figure 10: We are optimizing VSML RNNs to implement neural forwardcomputation suchthat for different inputs and weights a tanh-activated multiplicative interaction is produced (left), with different lines for differentw. Next, we use a deep network and provide intermediate errors by a ground truth network. Finally, we remove intermediate errors and use the RNN's intermediate predictions that are now close to the ground truth. All 6meta test tasks are unseen. Thebottom plot shows the same dataset processed by SGD with Adam which learns significantly slower by followingthegradient. those enabled.

Clockwise from top left: Rachel Johnson, Polly Toynbee, Jeanette Winterson, Cathy Newman, Ann Widdecombe, Fiona Marr. Clockwise from top left: Rachel Johnson, Polly Toynbee, Jeanette Winterson, Cathy Newman, Ann Widdecombe, Fiona Marr. I asked AI to name my wife. Authors, a newsreader, a lawyer and an esteemed colleague: they're all great - but I'm not married to any of them. Can we really depend on this technology?

Therefore,techniquestoreconstruct articulated 3D objects from 2D images are crucial and highly useful. In this work, we propose a practical problem setting to estimate 3D pose and shape of animals given only a few (10-30) in-the-wild images of a particular animal species (say,horse). Contrary toexisting worksthatrelyonpre-defined template shapes, we do not assume any form of 2D or 3D ground-truth annotations, nor do we leverage any multi-view or temporal information. Moreover, each input image ensemble can contain animal instances with varying poses, backgrounds, illuminations, and textures. Our key insight is that 3D parts have much simpler shape compared totheoverall animal and that theyarerobustw.r.t.

We further design an effective mechanism to inject PH information into GP at both feature and topology levels, with a novel topology-preserving loss function.

Much of this work is based on upper confidence bound (UCB) principles and prescribes some kind of exploration bonus to prioritize exploration of rarely visited regions.