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We use Personal Data and Other Data for our legitimate business interests, including the following: Provide the Services you request. Repeat Steps 4—5 for all the beers on your flight paddle, and then head back to Step 1. Have fun! No one under the age of 21 will be allowed to enter in the event!
This includes designated drivers, volunteers, and vendors. No children, toddlers, infants or strollers are permitted. No one will be admitted without a valid photo I. In order to rate each beer, it is necessary to download our custom Unlabeled app. You can download , print, and rate beers during the event using paper scoresheets on the BJCP website.
No, this is a brand new event series presented by The Growler Magazine with the main goal of education. Each Unlabeled event will feature Minnesota-made beers from a single beer style and guests will blind sample and rate each beer according to the BJCP style guidelines. In order to keep the tasting percent blind, brewery reps will not be pouring the beer at the event. All of the beers and the winner of Best of Show will be revealed near the end of the event in the Unlabeled app and publicly over the PA.
A list of ingredients for each beer has been provided by each breweries. Beers with lactose, tree nuts, or peanuts will be marked as such at the Flight Station and in the app. Unless otherwise denoted, all beers available for sampling contain gluten. Online follow links above and in person during regular office hours at The Growler offices, Toronto St. Paul, MN Cash Only.
Only if there are still tickets available. Be sure to check there before going to the venue without a ticket to ensure that one will be available to purchase. No refund due to weather. Please prepare for the elements. Check back closer to event time for a map of the event grounds. Entry to the event, a souvenir tasting flight paddle, and dozens of samples of Minnesota-made Oktoberfest.
Designated drivers are welcome to bring sealed or empty bottles of water into the event and can enjoy the non-alcoholic refreshments available at a designated booth, marked on the map. Yes, several food trucks will be on-site during the event. Food does not come with the price of admission. Yes, the venue is wheelchair accessible, although please note that this is an outdoor event and many aspects of the event are on the grassy area of the park. Only service dogs are allowed.
In that case, the individual must maintain control of the animal through voice, signal, or other effective controls. All other ticket related questions can be directed to:. For assistance with any issues concerning tickets you have purchased, please call Find a Copy About Contact. Past Events. Get Involved. Taste, Then Rate.
We demonstrate how MMCC can be applied, without fine-tuning, to a variety of challenging tasks, and qualitatively examine its predictions. In the example below, MMCC predicts the future d from present frame a , rather than less relevant potential futures b or c. Viewing Videos as Graphs The foundation of our method is to represent narrated videos as graphs. We view videos as a collection of nodes, where nodes are either video frames sampled at 1 frame per second or segments of narrated text extracted with automatic speech recognition systems , encoded by neural networks.
During training, MMCC constructs a graph from the nodes, using cross-modal edges to connect video frames and text segments that refer to the same state, and temporal edges to connect the present e.
The temporal edges operate on both modalities equally — they can start from either a video frame, some text, or both, and can connect to a future or past state in either modality.
MMCC achieves this by learning a latent representation shared by frames and text and then making predictions in this representation space. Multi-modal Cycle Consistency To learn the cross-modal and temporal edge functions without supervision, we apply the idea of cycle consistency. Here, cycle consistency refers to the construction of cycle graphs , in which the model constructs a series of edges from an initial node to other nodes and back again: Given a start node e.
To do this, at the start of training, the model assumes that frames and text with the same timestamps are counterparts, but then relaxes this assumption later. The model then predicts a future state, and the node most similar to this prediction is selected. Finally, the model attempts to invert the above steps by predicting the present state backward from the future node, and thus connecting the future node back with the start node.
Intuitively, this training objective requires the predicted future to contain enough information about its past to be invertible, leading to predictions that correspond to meaningful changes to the same entities e.
Moreover, the inclusion of cross-modal edges ensures future predictions are meaningful in either modality. Importantly, this cyclic graph constraint makes few assumptions for the kind of temporal edges the model should learn, as long as they end up forming a consistent cycle.
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