Inspired by real immigrant stories, H1B.Life captures the uncertainty, trade-offs, and pure luck that shape the lives of people trying to build a future in the US. When Allison Yang moved to the US from China two years ago, she noticed that immigrants often talked about their visa status like they were playing cards. The former Chinese journalist and founder of the game studio Reality Reload was at an event in New York when she heard fellow Chinese immigrants talking in confusing terminologies, like playing a Queen, Knight, or Ace. Everyone introduced themselves by words like H-1B, OPT, L-1, O-1, NIW--names of legal immigration categories in the US. With their cards on the table, they could start talking in greater depth about each person's immigration journey.

Semi-supervised learning methods based on generative adversarial networks (GANs) obtained strong empirical results, but it is not clear 1) how the discriminator benefits from joint training with a generator, and 2) why good semi-supervised classification performance and a good generator cannot be obtained at the same time. Theoretically we show that given the discriminator objective, good semi-supervised learning indeed requires a bad generator, and propose the definition of a preferred generator. Empirically, we derive a novel formulation based on our analysis that substantially improves over feature matching GANs, obtaining state-of-the-art results on multiple benchmark datasets.

We study the problem of learning probabilistic first-order logical rules for knowledge base reasoning. This learning problem is difficult because it requires learning the parameters in a continuous space as well as the structure in a discrete space. We propose a framework, Neural Logic Programming, that combines the parameter and structure learning of first-order logical rules in an end-to-end differentiable model. This approach is inspired by a recently-developed differentiable logic called TensorLog [5], where inference tasks can be compiled into sequences of differentiable operations. We design a neural controller system that learns to compose these operations. Empirically, our method outperforms prior work on multiple knowledge base benchmark datasets, including Freebase and WikiMovies.

This task has received a considerable amount of attention, particularly for classical ways of modeling distributions like structured prediction.

The technology may not be ready to replace workers, but that isn't stopping execs from pushing forward anyway. Late last month, at an event in Washington, D.C., Andrew Yang delivered a bleak message. "I have bad news, America," he told the crowd. The Fuckening is the name that Yang, a former presidential candidate, has given to AI's disembowelment of the workforce. As he sees it, millions of knowledge workers will soon lose their job, personal-bankruptcy rates will spike, and entire downtowns will turn vacant as offices hollow out.

Fomi watches you work, then scolds you when your attention wanders. It's helpful, but there are privacy issues to consider. I've tested a lot of software tools over the years designed to block distractions and keep you focused. None of them work perfectly, mostly because of context. Reddit, for example, is something I should generally avoid during the workday, so I tend to block it--this is a good decision for me overall.

However, constrained by the inherent geometric constraints of sequences, it still falls short in modeling long-range dependencies.

Many classic methods have shown non-local self-similarity in natural images to be an effective prior for image restoration. However, it remains unclear and challenging to make use of this intrinsic property via deep networks.

Inthispaper,weproposeareciprocative learning algorithm to exploit visual attention for training deep classifiers.