We provide hyper-parameters of our models in Table A.1. Table A.1: Hyper-parameters used for training our VisualCSE and AudioCSE. Vision, we use Dropout augmentation (the same strategy in SimCSE) for AudioCSE. We compare unsup-SimCSE and unsup-VisualCSE on a small scale retrieval test. As shown in Table C.1, VisualCSE generally retrieves qualitatively different sentences than SimCSE.

We expand Eq. 10 as: q There are three KL divergence terms in our training objective ELBO (Eq. Medium and Y elp Large datasets, we follow (Guu et al., 2018) to use a three-layer attentional LSTM Skip connections are also used between adjacent LSTM layers. We apply annealing and free-bits techniques following (Li et al., 2019) to the KL term on prototype variable, As in Section 4.3, here we show more generated examples through interpolation on MSCOCO dataset. Table 6: Qualitative examples from the MSCOCO dataset on interpolated sentence generation given the prototype.

We expand Eq. 10 as: q There are three KL divergence terms in our training objective ELBO (Eq. Medium and Y elp Large datasets, we follow (Guu et al., 2018) to use a three-layer attentional LSTM Skip connections are also used between adjacent LSTM layers. We apply annealing and free-bits techniques following (Li et al., 2019) to the KL term on prototype variable, As in Section 4.3, here we show more generated examples through interpolation on MSCOCO dataset. Table 6: Qualitative examples from the MSCOCO dataset on interpolated sentence generation given the prototype.

A week before Halloween last year, city of Austin employee Rachel Castignoli sent a polite but firm email to a government relations staffer at self-driving vehicle developer Cruise. "We would like you to not operate between 5 pm and 9 pm on Halloween," she wrote in bold text highlighted in yellow, documents obtained by WIRED through a public records request show. More children are killed by vehicles on Halloween than on any other night of the year, she wrote, and the city wanted to limit traffic--regardless of whether software or a human was behind the wheel. "Please acknowledge receipt of this email," Castignoli concluded, also in bold, adding "Thanks!" Castignoli's email is an example of the strange position of officials in some US cities chosen by Cruise and rivals such as Alphabet's Waymo as testing grounds for self-driving taxi services. Castignoli works for Austin's Transportation and Public Works Department, which like local agencies around the country, is responsible for what happens on city streets, setting speed limits and traffic restrictions.

As Waymo robotaxis plucked up passengers for free this week in Santa Monica and Venice, worry grew among Los Angeles officials about the safety of driverless cars on city streets. Mayor Bass asked regulators Wednesday to increase their scrutiny of automated taxis and said the city should have a say in how they are regulated. The move comes after a Cruise robotaxi dragged a person down a San Francisco street last month and the company allegedly failed to disclose the footage to the state Department of Motor Vehicles. The DMV suspended the General Motors-owned company's permits and Cruise has since announced it will suspend U.S. operations. The incident in San Francisco -- where the two driverless fleets were doing business -- was among several that raised red flags among Los Angeles officials, who have begun to see more and more robotaxis being tested on city streets.

A group of San Francisco organizers are encouraging people to put traffic cones on the hoods of driverless vehicles as a form of protest against the cars' expansion on city streets. A video of the group's actions with step-by-step instructions on how to disable a robo-taxi with a cone has gone viral on Twitter and sparked intense debates about the pros and cons of autonomous vehicles and the value of protesting in this way. Safe Street Rebel, a group of organizers that advocate for pedestrian safety and reducing the number of cars on roads, are behind this stunt. They hope that it will raise the public's awareness of the potential dangers driverless taxis pose before a pivotal vote by the California public utilities commission set to take place on 13 July. The vote would allow Cruise, a company controlled by the automaker General Motors, and Waymo, a Google spinoff, to charge people for rides as a part of the state's driverless autonomous vehicles passenger service deployment program, according to the meeting agenda.

Tesla's AI Day, a yearly event for the tech-obsessed eager to see new ways the company is pushing the envelope, is scheduled for Friday, Sept. 30 in Palo Alto. It's expected to be live-streamed on the Tesla website and YouTube channel around 5 p.m. PT and promises lots of Big Musk Energy. AI Day is basically Tesla's version of an Apple event, but rather than product launches, the event will have a forward-looking focus. It's less about new Teslas, and more about emerging technologies the company is exploring. As Musk noted on Twitter, "this event is meant for recruiting AI & robotics engineers, so will be highly technical."

Accuracy and Diversity are two essential metrizable manifestations in generating natural and semantically correct captions. Many efforts have been made to enhance one of them with another decayed due to the trade-off gap. In this work, we will show that the inferior standard of accuracy draws from human annotations (leave-one-out) are not appropriate for machine-generated captions. To improve diversity with a solid accuracy performance, we exploited a novel Variational Transformer framework. By introducing the "Invisible Information Prior" and the "Auto-selectable GMM", we instruct the encoder to learn the precise language information and object relation in different scenes for accuracy assurance. By introducing the "Range-Median Reward" baseline, we retain more diverse candidates with higher rewards during the RL-based training process for diversity assurance. Experiments show that our method achieves the simultaneous promotion of accuracy (CIDEr) and diversity (self-CIDEr), up to 1.1 and 4.8 percent. Also, our method got the most similar performance of the semantic retrieval compared to human annotations, with 50.3 (50.6 of human) for R@1(i2t).