Through these Pandora Papers investigations, the International Consortium of Investigative Journalists found that at least 27 pieces with Latchford ties remain on display in prominent museums, including six in the Denver Art Museum. Alyssa Hurst: The conversation about looted art Nov 24, · These papers will give you a broad overview of AI research advancements this year. Of course, there are many more breakthrough papers worth reading as well. We have also published the top 10 lists of key research papers in natural language processing and computer vision Nov 10, · Jerz > Writing > Academic > Research Papers [ Title | Thesis | Blueprint | Quoting | Citing | MLA Format ]. This document focuses on the kind of short, narrowly-focused research papers that might be the final project in a freshman writing class or level literature survey course. In high school, you probably wrote a lot of personal essays (where your goal was to demonstrate you were
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Despite the challenges ofthe AI research community produced a number of meaningful technical breakthroughs. GPT-3 by OpenAI may be the most famous, but there are definitely many other research papers worth your attention.
For example, teams from Google introduced a revolutionary chatbot, Meena, and EfficientDet object detectors in image recognition. Researchers from Yale introduced a novel AdaBelief optimizer that combines many benefits of existing optimization methods. OpenAI researchers demonstrated how deep reinforcement learning techniques can achieve superhuman performance in Dota 2. These papers will give you a broad overview of AI research advancements this year, research papers on art.
Of course, there are many more breakthrough papers worth reading as well. We have also published the top 10 lists of key research papers in natural language processing and computer vision. In addition, you can read our premium research summarieswhere we feature the top 25 conversational AI research papers introduced recently. Subscribe to our AI Research mailing research papers on art at the bottom of this article to be alerted when we release new summaries.
Our research aims to improve the accuracy of Earthquake Early Warning EEW systems by means of machine learning. EEW systems are designed to detect and characterize medium and large research papers on art before their damaging effects reach a certain location. Traditional EEW methods based on seismometers fail to accurately identify large earthquakes due to their sensitivity to the ground motion velocity, research papers on art.
The recently introduced high-precision GPS stations, on the other hand, are ineffective to identify medium earthquakes due to their propensity to produce noisy data. In addition, GPS stations and seismometers may be deployed in large numbers across different locations and may produce a significant volume of data, consequently affecting the response time and the robustness of EEW systems.
In practice, EEW can be seen as a typical classification problem in the machine learning field: multi-sensor data are given in input, and earthquake severity is the classification result. In this paper, we introduce the Distributed Multi-Sensor Earthquake Early Warning DMSEEW system, a novel machine learning-based approach that combines data from both types of sensors GPS stations and seismometers to detect medium and large earthquakes. DMSEEW is based on a new stacking ensemble method which has been evaluated on a real-world dataset validated with geoscientists.
The system builds on a geographically distributed infrastructure, ensuring an efficient computation in terms of response research papers on art and robustness to partial infrastructure failures. Our experiments show that DMSEEW is more accurate than the traditional seismometer-only approach and the combined-sensors GPS and seismometers approach that adopts the rule of relative strength. The authors claim that traditional Earthquake Early Warning EEW systems that are based on seismometers, as well as recently introduced GPS systems, have their disadvantages with regards to predicting large and medium earthquakes respectively.
Thus, research papers on art, the researchers suggest approaching an early earthquake prediction problem with machine learning by using the data from seismometers and GPS stations as input data. In particular, they introduce the Distributed Multi-Sensor Earthquake Early Warning DMSEEW system, research papers on art, which is specifically tailored for efficient computation on large-scale distributed cyberinfrastructures.
The evaluation demonstrates that the DMSEEW system is more accurate than other baseline approaches with regard to real-time earthquake detection. These problems typically exist as parts of larger frameworks, wherein quantities of research papers on art are ultimately defined by integrating over posterior distributions.
These quantities are frequently intractable, motivating the use of Monte Carlo methods, research papers on art. Despite substantial progress in scaling up Gaussian processes to large training sets, methods for accurately generating draws from their posterior distributions still scale cubically in the number of test locations. We identify a decomposition of Gaussian processes that naturally lends research papers on art to scalable sampling by separating out the prior from the data.
Building off of this factorization, we propose an easy-to-use and general-purpose approach for fast posterior sampling, which seamlessly pairs with sparse approximations to afford scalability both during training and at test time.
Research papers on art this paper, the authors explore techniques for efficiently sampling from Gaussian process GP posteriors. After investigating the behaviors of research papers on art approaches to sampling and fast approximation strategies using Fourier features, they find that many of these strategies are complementary. They, therefore, introduce an approach that incorporates the best of different sampling approaches, research papers on art.
First, they suggest decomposing the posterior as the sum of a prior and an update. Then they combine this idea with techniques from literature on approximate GPs and obtain an easy-to-use general-purpose approach for fast posterior sampling. The experiments demonstrate that decoupled sample paths accurately represent GP posteriors at a much lower cost. On April 13th,OpenAI Five became the first AI system to defeat the world champions at an esports game.
The game of Dota 2 presents novel challenges for AI systems such as long time horizons, imperfect information, and complex, continuous state-action spaces, all challenges which will become increasingly central to more capable AI systems. OpenAI Five leveraged existing reinforcement learning techniques, scaled to learn from batches of approximately 2 million frames every 2 seconds.
We developed a distributed training system and tools for continual training which allowed us to train OpenAI Five for 10 months. By defeating the Dota 2 world champion Team OGOpenAI Five demonstrates that self-play reinforcement learning can achieve superhuman performance on a difficult task, research papers on art. The OpenAI research team demonstrates that modern reinforcement learning techniques can achieve superhuman performance in such a challenging esports game as Dota 2.
The challenges of this particular task for the AI system lies in the long time horizons, partial observability, and high dimensionality of observation and action spaces. To tackle this game, the researchers scaled existing RL systems to unprecedented levels with thousands of GPUs utilized for 10 months.
The resulting OpenAI Five model was able to defeat the Dota 2 world champions and won We present Meena, research papers on art, a multi-turn open-domain chatbot trained end-to-end on data mined and filtered from public domain social media conversations. This 2. We also propose a human evaluation metric called Sensibleness and Specificity Average SSAwhich captures key elements of a human-like multi-turn conversation.
Our experiments show strong correlation between perplexity and SSA. In contrast to most modern conversational agents, which are highly specialized, the Google research team introduces a chatbot Meena that can chat about virtually anything. The researchers also propose a new human evaluation metric for open-domain chatbots, called Sensibleness and Specificity Average SSAwhich can capture important attributes for human conversation. They demonstrate that this metric correlates highly with perplexity, an automatic metric that is readily available.
Thus, the Meena chatbot, which is trained to minimize perplexity, can conduct conversations that are more sensible and specific compared to other chatbots. Recent work has demonstrated substantial gains on many NLP tasks and benchmarks by pre-training on a large corpus of text followed by fine-tuning on a specific task, research papers on art.
While typically task-agnostic in architecture, this method still requires task-specific fine-tuning datasets of thousands or tens of thousands of examples. By contrast, humans can generally perform a new language task from only a few examples or from simple instructions — something which current NLP systems still largely struggle to do.
Here we show that scaling up language models greatly improves task-agnostic, few-shot performance, sometimes even reaching competitiveness with prior state-of-the-art fine-tuning approaches. Specifically, we train GPT-3, an autoregressive language model with billion parameters, 10× more than any previous non-sparse language model, and test its performance in the few-shot setting.
For all tasks, GPT-3 is applied without any gradient updates or research papers on art, with tasks and few-shot demonstrations specified purely via text interaction with the model.
GPT-3 achieves strong performance on many NLP datasets, including translation, question-answering, and cloze tasks, as well as several tasks that require on-the-fly reasoning or domain adaptation, such as unscrambling words, using research papers on art novel word in a sentence, research papers on art performing 3-digit arithmetic.
Finally, we find that GPT-3 can generate samples of news articles which human evaluators have difficulty distinguishing from articles written by humans. We discuss broader societal impacts of this finding and of GPT-3 in general. The OpenAI research team draws attention to the fact that the need for a labeled dataset for every new language task limits the applicability of language models.
They test their solution by training a B-parameter autoregressive language model, research papers on art, called GPT-3and evaluating its performance on over two dozen NLP tasks. The evaluation under few-shot learning, one-shot learning, and zero-shot learning demonstrates that GPT-3 achieves promising results and even occasionally outperforms the state of the art achieved by fine-tuned models.
Although measuring held-out accuracy has been the primary approach to evaluate generalization, it often overestimates the performance of NLP models, while alternative approaches for evaluating models either focus on individual tasks or on specific behaviors.
Inspired by principles of behavioral testing in software research papers on art, we introduce CheckList, a task-agnostic methodology for testing NLP models. CheckList includes a matrix of general linguistic capabilities and test types that facilitate comprehensive test ideation, as well as a software tool to generate a large and diverse number of test cases quickly.
We illustrate the utility of CheckList with tests for three tasks, identifying critical failures in both commercial and state-of-art models. In a user study, a team responsible for a commercial sentiment analysis model found new and actionable bugs in an extensively tested model.
In another user study, NLP practitioners with CheckList created twice as many tests, and found almost three times as many bugs as users without it. The authors point out the shortcomings of existing approaches to evaluating performance of NLP models.
A single aggregate statistic, like accuracy, makes it difficult to estimate where the model is failing and how to fix it. The alternative evaluation approaches usually focus on individual tasks or specific capabilities. To address the lack of comprehensive evaluation approaches, the researchers introduce CheckLista new evaluation methodology for testing of NLP models.
The approach is inspired by principles of behavioral testing in software engineering. Basically, CheckList is a matrix of linguistic capabilities and test types that facilitates test ideation. Multiple user studies demonstrate that CheckList is very effective at discovering actionable bugs, even in extensively tested NLP models.
Model efficiency has become increasingly important in computer vision. In this paper, research papers on art systematically study neural network architecture design choices for object detection and propose several key optimizations to improve efficiency. Based on these optimizations and EfficientNet backbones, we have developed a new family of object detectors, research papers on art, called EfficientDet, research papers on art, which consistently achieve much better efficiency than prior art across a wide spectrum of resource constraints.
In particular, with single-model and single-scale, our EfficientDet-D7 achieves state-of-the-art The large size of object detection models deters their deployment in real-world applications such as self-driving cars and robotics. To address this problem, the Google Research team introduces two optimizations, namely 1 a weighted bi-directional feature pyramid network BiFPN for efficient multi-scale feature fusion and 2 a novel compound scaling method.
By combining these optimizations with the EfficientNet backbones, the authors develop a family of object detectors, called EfficientDet. The experiments demonstrate that these object detectors consistently achieve higher accuracy with far fewer parameters and multiply-adds FLOPs.
We propose a method to learn 3D deformable object categories from raw single-view images, without external supervision.
The method is based on an autoencoder that factors each input image into depth, albedo, viewpoint and illumination.
In order to disentangle these components without supervision, we use the fact that many object categories have, at least in principle, research papers on art, a symmetric structure. We show that reasoning about illumination allows us to exploit the underlying object symmetry even if the appearance is not symmetric due to shading, research papers on art. Furthermore, we model objects that are probably, but not certainly, symmetric by predicting a symmetry probability map, learned end-to-end with the other components of the model.
Our experiments show that this method can recover very accurately the 3D shape of human faces, cat faces and cars from single-view images, without any supervision or a prior shape model. On benchmarks, we demonstrate superior accuracy compared to another method that uses supervision at the level of 2D image correspondences.
The research group from the University of Oxford studies the problem of learning 3D deformable object categories from single-view RGB images without additional supervision. To decompose the image into depth, albedo, illumination, research papers on art, and viewpoint without direct supervision for these factors, they suggest starting by assuming objects to be symmetric.
Then, considering that real-world objects are never fully symmetrical, at least due research papers on art variations in pose and illumination, the researchers augment the model by explicitly modeling illumination and predicting a dense map with probabilities that any given pixel has a symmetric counterpart.
The experiments demonstrate that the introduced approach achieves better reconstruction results than other unsupervised methods. Moreover, it outperforms the recent state-of-the-art method that leverages keypoint supervision. While the Transformer architecture has become the de-facto standard for natural language processing tasks, its applications to computer vision remain limited, research papers on art. In vision, attention is either applied in conjunction with convolutional networks, or used to replace certain components of convolutional networks while keeping their overall structure in place.
Arts-based research: definition, procedures \u0026 application (Dr Patricia Leavy)
, time: 50:12Introductions for research papers
Research paper examples are of great value for students who want to complete their assignments timely and efficiently. If you are a student in the university, your first stop in the quest for research paper examples will be the campus library where you can get to view the research sample papers of lecturers and other professionals in diverse fields plus those of fellow students who preceded Through these Pandora Papers investigations, the International Consortium of Investigative Journalists found that at least 27 pieces with Latchford ties remain on display in prominent museums, including six in the Denver Art Museum. Alyssa Hurst: The conversation about looted art 20th century art history essay topics how to write a 4 page paper fast resume crm project manager example cover letter research proposal how to force yourself to write an essay: top 10 reasons to not do your homework honours thesis introduction, esl essay writers for hire uk premade resume in word, economics admissions essay: write term paper
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