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Charles Ramirez
Charles Ramirez

MODELS



Generative models are one of the most promising approaches towards this goal. To train a generative model we first collect a large amount of data in some domain (e.g., think millions of images, sentences, or sounds, etc.) and then train a model to generate data like it. The intuition behind this approach follows a famous quote from Richard Feynman:




MODELS


Download: https://www.google.com/url?q=https%3A%2F%2Fmiimms.com%2F2uedfB&sa=D&sntz=1&usg=AOvVaw2G9i0VYFW_0mIMIbtNkxGL



The trick is that the neural networks we use as generative models have a number of parameters significantly smaller than the amount of data we train them on, so the models are forced to discover and efficiently internalize the essence of the data in order to generate it.


Generative models have many short-term applications. But in the long run, they hold the potential to automatically learn the natural features of a dataset, whether categories or dimensions or something else entirely.


Improving GANs (code). First, as mentioned above GANs are a very promising family of generative models because, unlike other methods, they produce very clean and sharp images and learn codes that contain valuable information about these textures. However, GANs are formulated as a game between two networks and it is important (and tricky!) to keep them in balance: for example, they can oscillate between solutions, or the generator has a tendency to collapse. In this work, Tim Salimans, Ian Goodfellow, Wojciech Zaremba and colleagues have introduced a few new techniques for making GAN training more stable. These techniques allow us to scale up GANs and obtain nice 128x128 ImageNet samples:


We show some example 32x32 image samples from the model in the image below, on the right. On the left are earlier samples from the DRAW model for comparison (vanilla VAE samples would look even worse and more blurry). The DRAW model was published only one year ago, highlighting again the rapid progress being made in training generative models.


Curiosity-driven Exploration in Deep Reinforcement Learning via Bayesian Neural Networks (code). Efficient exploration in high-dimensional and continuous spaces is presently an unsolved challenge in reinforcement learning. Without effective exploration methods our agents thrash around until they randomly stumble into rewarding situations. This is sufficient in many simple toy tasks but inadequate if we wish to apply these algorithms to complex settings with high-dimensional action spaces, as is common in robotics. In this paper, Rein Houthooft and colleagues propose VIME, a practical approach to exploration using uncertainty on generative models. VIME makes the agent self-motivated; it actively seeks out surprising state-actions. We show that VIME can improve a range of policy search methods and makes significant progress on more realistic tasks with sparse rewards (e.g. scenarios in which the agent has to learn locomotion primitives without any guidance).


Models work in a variety of conditions, from comfortable indoor studios and runway fashion shows to outdoors in all weather conditions. Most models work part time and have unpredictable work schedules. Many also experience periods of unemployment.


No formal educational credential is required and training is limited. Specific requirements depend on the client. However, most models must be within certain ranges for height, weight, and clothing size to meet the needs of fashion designers, photographers, and advertisers.


About 500 openings for models are projected each year, on average, over the decade. Many of those openings are expected to result from the need to replace workers who transfer to different occupations or exit the labor force, such as to retire.


Models must research an agency before signing, in order to ensure that the agency has a good reputation in the modeling industry. For information on agencies, models should contact a local consumer affairs organization, such as the Better Business Bureau.


Some freelance models do not sign with agencies. Instead, they market themselves to potential clients and apply for modeling jobs directly. However, because most clients prefer to work with agents, it is difficult for new models to pursue a freelance career.


Models appear in different types of media to promote a product or service. Models advertise products and merchandise in magazine or newspaper advertisements, department store catalogs, or television commercials. Increasingly, models are appearing in online ads or on retail websites. Models also pose for sketch artists, painters, and sculptors.


No formal education credential is required to become a model. Specific requirements depend on the client, with different jobs requiring different physical characteristics. However, most models must be within certain ranges for height, weight, and clothing size.


Specific requirements depend on the client, but most models must be within certain ranges for height, weight, and clothing size. Requirements may change slightly over time as perceptions of physical beauty change.


The median hourly wage for models was $15.34 in May 2020. The median wage is the wage at which half the workers in an occupation earned more than that amount and half earned less. The lowest 10 percent earned less than $12.00, and the highest 10 percent earned more than $59.97.


Rising retail sales, particularly online and in e-commerce, will encourage businesses to increase their digital advertising and marketing budgets. Demand for models to appear in digital advertisements is expected to lead to increased employment for these workers. However, less expensive digital and social media options are allowing companies promote their products and brands directly to consumers, which may moderate employment demand for models.


We believe that it is critical to analyze the threat of AI-enabled influence operations and outline steps that can be taken before language models are used for influence operations at scale. We hope our research will inform policymakers that are new to the AI or disinformation fields, and spur in-depth research into potential mitigation strategies for AI developers, policymakers, and disinformation researchers.


When researchers evaluate influence operations, they consider the actors, behaviors, and content. The widespread availability of technology powered by language models has the potential to impact all three facets:


Our bottom-line judgment is that language models will be useful for propagandists and will likely transform online influence operations. Even if the most advanced models are kept private or controlled through application programming interface (API) access, propagandists will likely gravitate towards open-source alternatives and nation states may invest in the technology themselves.


While we expect to see diffusion of the technology as well as improvements in the usability, reliability, and efficiency of language models, many questions about the future remain unanswered. Because these are critical possibilities that can change how language models may impact influence operations, additional research to reduce uncertainty is highly valuable.


Mental models are how we understand the world. Not only do they shape what we think and how we understand but they shape the connections and opportunities that we see. Mental models are how we simplify complexity, why we consider some things more relevant than others, and how we reason.


A mental model is simply a representation of how something works. We cannot keep all of the details of the world in our brains, so we use models to simplify the complex into understandable and organizable chunks.


This page provides a list of nationally and locally accepted models that meet National Flood Insurance Program (NFIP) requirements for flood hazard mapping activities, technical documentation for acceptable models and models no longer acceptable. This page is intended for engineers, surveyors, floodplain managers and FEMA mapping partners.


All computer models referenced from this web page have met the requirements of Subparagraph 65.6(a)(6) of the NFIP regulations that explain conditions by which a computer model may be used for flood hazard mapping. For further information on these regulations and to learn how to get a model added to this list, please refer to the Policy for Accepting Numerical Models for Use in the NFIP.


The resources below include the current list of acceptable models, which have been separated into nationally and locally accepted categories; supporting technical documentation for certain acceptable models and a list of numerical models which FEMA no longer accepts for NFIP usage.


Tables 1 and 2 contain a scorecard for each prototype building. The scorecard is a spreadsheet (Microsoft Excel, .xls, format) that summarizes the building descriptions, thermal zone internal loads, schedules, and other key modeling input information. The suite of prototype models is available for download (compressed, .zip, format) for the respective edition of Standard 90.1 and IECC. Each file includes EnergyPlus model input files (.idf) and corresponding output files (.html) across all climate locations.


The energy models for the 2015, 2018, and 2021 editions of the IECC are listed in Table 4. Each compressed (.zip) file includes EnergyPlus model input files (.idf) and corresponding output files (.htm) for each of the eight climate zones (1-8) and three moisture regimes (A=Moist, B=Dry, C=Marine) defined in the IECC.


The energy models for the 2015, 2018 and 2021 versions of the IECC are listed in Table 4 and can be downloaded either by specific IECC edition or as complete sets by climate zone. The complete sets contain prototypes with earlier versions of the IECC. The idf files may be opened and modified in EnergyPlus.


The single family prototypes are now complete EnergyPlus files utilizing the airflow network for duct leakage modeling. Previous single family prototype models posted on the Energy Codes website did not contain duct leakage specifications. Calculating loads for duct leakage required multiple EnergyPlus simulations with and without duct leakage and post processing the results for both single family and multifamily buildings. As a result, there may be large differences in energy consumption when comparing the latest single family prototypes results to older prototype results downloaded from this website. The multifamily prototype models do not contain duct leakage specifications, and the duct leakage adjustment are applied during the post-processing. We are working on updating the MF models to incorporate the airflow network with duct leakage loops. 041b061a72


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