Extensiveexperiments on NYUv2 dataset (object detection with scene classification, depth prediction, and surface normal estimation as auxiliary tasks) validate the relevance of the approach and its superiority to flat MTL approaches.

I do not completely agree with this characterization. TL is a broader term for the phenomenon of learnings from one task benefitting another task. Fine-tuning is a sequential way of doing it. Standard MTL is a parallel means to the same end.

Two weeks ago, Netflix confirmed it would livestream the Love Is Blind season four reunion. But when the time came, first it was delayed, then, after an hour, Netflix pulled the plug. The streaming service soon tweeted an apology and aired the reunion yesterday. This was only Netflix's second attempt at livestreaming. Its first, Chris Rock's comedy special Selective Outrage, aired live in March.

Apparently, the project's domain relies on the most popular liquor in the world -- Whiskey. A dark spirit coming from a great variety of grains, distilled throughout the world and arriving at quite a number of styles (Irish, Scotch, Bourbon etc) [1]. Scotland, Ireland, Canada & Japan are among the famous exporters and on an international scale, the global production almost reaches the level of $95m revenue [2]. The main scope, hereof, is to introduce in a… 'companionable' way, how helpful can the Clustering Algorithms prove to be, anytime we need to find patterns in a (large) dataset. Actually, it might be considered as a powerful expansion of the standard Exploratory Data Analysis (EDA), which is often very beneficial to try, before using Supervised Machine Learning (ML) models.

The Find-Life-on-Mars event of the 1997 American Association for Artificial Intelligence Mobile Robot Competition and Exhibition featured robots trying to find and collect stationary and moving colored objects in an arena littered with real rocks. The 2-day event had 11 entries participating in both single-robot and multirobot categories, both with and without manipulators. During the event, many of the robots successfully demonstrated object recognition, obstacle avoidance, exploration, and the collection and depositing of objects. The general concept was to have the robots locate, collect, and deliver a variety of "life forms," including both stationary and moving objects. Technically, the event was designed to highlight mobile manipulation, object recognition, exploration, and obstacle avoidance in a relatively unstructured environment.

CLARK's arm was ripped off midway NOMAD 150 robots (shown in the photograph). From left to right: Tucker Balch, Juan Carlos Santamaria, and Tom Collins. Noise helps move the robot out of any local minima it encounters. In this manner, behaviors were developed for each stage of the task, for example, wander, acquire_red, acquire_blue, predock_red, predock_blue, deliver_red, and deliver_blue. The control systems sequence from one behavior to another based on perceptual cues provided by the sensors.

This, coupled with limited bandwidth and latencies, motivates on-board autonomy that ensures the quality of the science data return. Increasing quality of the data requires better sample selection, data validation, and data reduction. Robotic studies in Mars-like desert terrain have advanced autonomy for long-distance exploration and seeded technologies for planetary rover missions. In these field experiments the remote science team uses a novel control strategy that intersperses preplanned activities with autonomous decision making. The robot performs automatic data collection, interpretation, and response at multiple spatial scales.

The primary revolution in automated planning in the last decade has been the very impressive scaleup in planner performance. A large part of the credit for this can be attributed squarely to the invention and deployment of powerful reachability heuristics. Most, if not all, modern reachability heuristics are based on a remarkably extensible data structure called the planning graph, which made its debut as a bit player in the success of GraphPlan, but quickly grew in prominence to occupy the center stage. Planning graphs are a cheap means to obtain informative look-ahead heuristics for search and have become ubiquitous in state-of-the-art heuristic search planners. We present the foundations of planning graph heuristics in classical planning and explain how their flexibility lets them adapt to more expressive scenarios that consider action costs, goal utility, numeric resources, time, and uncertainty. Considerable work has been done in the last 40 years on modeling a wide variety of ...