Animal pose estimation and tracking (APT) is a fundamental task for detecting and tracking animal keypoints from a sequence of video frames.

Informally, a'spurious correlation' is the dependence of a model on some aspect of the input data that an analyst thinks shouldn't matter. In machine learning, these have a know-it-when-you-see-it character; e.g., changing the gender of a sentence's subject changes a sentiment predictor's output. To check for spurious correlations, we can'stress test' models by perturbing irrelevant parts of input data and seeing if model predictions change. In this paper, we study stress testing using the tools of causal inference. We introduce counterfactual invariance as a formalization of the requirement that changing irrelevant parts of the input shouldn't change model predictions.

Since the appearance of Transformers, many works have investigated ways to encode positional information.

HierSpeech-U can adapt to a novel speaker by utilizing self-supervised speech representations without text transcripts.

Mark Wahlberg straightens his tie and beams at the audience as he takes his seat on daytime talk show The View, ahead of his hotly anticipated interview. Immediately, he's unsettled by the host, Joy Behar. Her eyes seem shifty, suspicious, even predatory. There's a sense, almost, of the uncanny valley--her presence feels oddly inhuman. His instincts are right, of course, and he's soon forced to defend himself against a barrage of cruel insults playing on his deepest vulnerabilities.

Welcome to Slate's weekly news quiz. It's Friday, which means it's time to test your knowledge of the week's news events. Your host, Ray Hamel, has concocted questions on news topics ranging from politics to business, from culture to sports to science. At the end of the quiz, you'll be able to compare your score with that of the average contestant, as well as that of a Slatester who has agreed to take the quiz on the record. This week's contestant is senior editor Rebecca Onion.

In this experiment we train both ProtoNet and proposed MProtoNet+KML on a meta-dataset constructed by combining Omniglot and FC100 few-shot tasks. We samples 300 meta-train Omniglot tasks, and a meta-test FC100 task as target task to perform transference analysis.