Recently, the accuracy of image-text matching has been greatly improved by multimodal pretrained models, all of which are trained on millions or billions of paired images and texts. Different from them, this paper studies a new scenario as unpaired image-text matching, in which paired images and texts are assumed to be unavailable during model training. To deal with this, we propose a simple yet effective method namely Multimodal Aligned Conceptual Knowledge (MACK), which is inspired by the knowledge use in human brain. It can be directly used as general knowledge to correlate images and texts even without model training, or further fine-tuned based on unpaired images and texts to better generalize to certain datasets. In addition, we extend it as a re-ranking method, which can be easily combined with existing image-text matching models to substantially improve their performance.

The use of machine learning methods in high energy physics typically relies on large volumes of precise simulation for training. As machine learning models become more complex they can become increasingly sensitive to differences between this simulation and the real data collected by experiments. We present a generic methodology based on contrastive learning which is able to greatly mitigate this negative effect. Crucially, the method does not require prior knowledge of the specifics of the mismodeling. While we demonstrate the efficacy of this technique using the task of jet-tagging at the Large Hadron Collider, it is applicable to a wide array of different tasks both in and out of the field of high energy physics.

Recently, the accuracy of image-text matching has been greatly improved by multimodal pretrained models, all of which are trained on millions or billions of paired images and texts. Different from them, this paper studies a new scenario as unpaired image-text matching, in which paired images and texts are assumed to be unavailable during model training. To deal with this, we propose a simple yet effective method namely Multimodal Aligned Conceptual Knowledge (MACK), which is inspired by the knowledge use in human brain. It can be directly used as general knowledge to correlate images and texts even without model training, or further fine-tuned based on unpaired images and texts to better generalize to certain datasets. In addition, we extend it as a re-ranking method, which can be easily combined with existing image-text matching models to substantially improve their performance.

This paper proposes several alternative extensions of GAIL to multi-agent imitation learning settings. The paper includes strong, positive results on a wide range of environments against a suitable selection of baselines. However, insufficient details of the environments is provided to reproduce or fully appreciate the complexity of these environments. If accepted, I would request the authors add these to the appendix and would appreciate details (space permitting) to be discussed in the rebuttal - particularly the state representation. The more pressing point I would like to raise for discussion in the rebuttal is with regard to the MACK algorithm proposed for the generator. The authors make a justified argument for the novelty of the algorithm, but do not thoroughly justify why they used this algorithm instead of an established MARL algorithm (e.g.

The legal profession has a long history of keeping pace with technology as it advances. With the development and spread of artificial intelligence (AI), various professions have embraced its ability to automate tasks that people once did. This shift has caused anxiety among lawyers, who worry about losing their jobs to machines. But it is becoming clear that, as AI evolves, lawyers will find new and innovative ways to use it in their practices. AI is already used in some law firms to automate such tasks as contract review and discovery.

When most people think about the longevity of their professions, they tend to think that, one day, robots will probably replace them. Blockchain, AI, and numerous other disruptive technologies are on the rise, so the amount of human labor required for a task is going to continually diminish. That being said, in order to properly consider one's future, one should look at both the threats and opportunities. In fact, the latter may even provide contingency plans to the former. Let's start by dealing with fear using our robot example.

Arcades occupy a unique place in video game history. In the late 1970s and 1980s, a string of hits like Space Invaders, Pac-Man, and Donkey Kong ushered in new gameplay mechanics and bright, crispy pixel graphics. The 1990s featured the fighting game boom with Street Fighter II, Mortal Kombat, and Virtua Fighter demonstrating cutting-edge graphics and gameplay. It was the place to be, a time when the cutting edge in video games, from texture-mapped polygonal graphics to peripheral control inputs (including steering wheels, light guns, and dance-mats), could only be found crammed into immaculately designed cabinets, complete with their showy bezels and marquees. Arcades dodged hardware limitations largely due to their ability to optimize the hardware specifically to play one single game.

Locals affected by Hurricane Harvey who searched for help or a way to give it may not realize their search was influenced by artificial intelligence. Thousands of searches landed on one of Gallery Furniture's web pages, guided there by a software named Albert. Running algorithms that constantly assessed and tweaked the words, syntax, offers and even colors on social media and the web, the software crafted landing pages, social media posts and email campaigns designed to elicit the greatest response. Then he read a case study about a Harley Davidson dealership in New York City that posted some pretty jaw-dropping sales increases after implementing AI: Weekend sales nearly doubled, and leads shot from one a day to 40 a day in just a month. The numbers might have generated skepticism, except the article was in the Harvard Business Review.