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Do tomatoes belong in Louisiana-style gumbo?
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The answer is it depends. In traditional Louisiana-style gumbo recipes from the area known for Cajun (Acadiana) cuisine, tomatoes are not commonly part of the recipe. However, in the area known for Creole (New Orleans) cuisine tomatoes are commonly part of the recipe.
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The LP (from "long playing" or "long play") is an analog sound storage medium, a phonograph record format characterized by: a speed of 33+1⁄3 rpm; a 12- or 10-inch (30- or 25-cm) diameter; use of the "microgroove" groove specification; and a vinyl (a copolymer of vinyl chloride acetate) composition disk. Introduced by Columbia in 1948, it was soon adopted as a new standard by the entire record industry. Apart from a few relatively minor refinements and the important later addition of stereophonic sound, it remained the standard format for record albums (during a period in popular music known as the album era) until its gradual replacement from the 1980s to the early 2000s, first by cassettes, then by compact discs, and finally by digital music distribution.
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Which record label created Vinyl
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Vinyl was introduced by Columbia in 1948
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What are the most successful Premier League football teams?
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Manchester United, Manchester City, Arsenal and Chelsea are the most successful football teams of the Premier League era.
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What are some ideas for a birthday celebration for a 4 year-old boy?
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Birthday celebrations for 4 year-old boys are a great occasion to bring people together and make the birthday boy feel loved. Here are some great ideas that may be perfect for the 4 year old in your life:
1. Pool Party - If you have access to a pool, having your party with the pool being the centerpiece is great fun. The kids can swim and splash while the parents visit together on the deck. Food, drink and birthday cake are essential to the fun! Just be thoughtful about keeping the eating and swimming as two distinct activities during the party.
2. Pizza making party - This is great for the kids and the parents alike. You can get fresh pizza dough from most any grocery store. Have bowls of sauce, mozzarella cheese, meats, and vegetables that each member of the party can choose the ingredients they want on their pizza. Let each person make their own. Have the oven hot and ready to cook the pizzas as your guests prepare their own special concoction. While people are waiting, you can do things like have games, have a sing-a-long, or just visit.
3. Outdoor organized games - Pick four or five organized games and have some of the adults be the facilitators. Divide the attendees into teams, mix the teams up and just make sure everyone is being included. After the games, or at a halfway mark, break for food and cake!
These are just a few ideas. Let your imagination run freely. Anything that creates fun, includes everyone, makes the birthday boy feel loved and includes food, you'll have a winning celebration!
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How is data engineering different from data science?
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Data Engineering occurs before data science in the data lifecycle. Data can be generated from various sources and this raw data may be structured, semi structured or even unstructured. To make sense of this data, data has to be ingested and transformed. This is when data engineering comes into picture. Data engineering includes the process of extracting the raw data, ingesting data into a system like a data lake, transforming the data for business needs and finally loading it into a table. This is commonly known as ELT (Extract-Load-Transform). ETL (Extract-Transform-Load) is also possible where transformation happens before the loading stage.
Data Science and Analytics is the next step in the data lifecycle. Once the data needed for specific business need is available, data scientists use this data to run machine learning models to find the most accurate model. This data is available to the data scientists in the form of tables. Data analysts also use this data to do some exploratory analysis and create dashboards.
In essence, the data lifecycle would look as follows:
Raw data from different sources -> Data Engineering -> Data Science and Analytics
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What is a career grand slam in professional golf?
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A career grand slam is achieved when a professional golfer is able to win all of golf's major championships. These four tournaments are the Masters Tournament, the PGA Championship, the U.S. Open, and The Open Championship. No golfer in the modern era has ever completed a grand slam in the same calendar year.
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White Rabbit is a 2013 American psychological drama film directed by Tim McCann and starring Nick Krause, Sam Trammell and Britt Robertson. Written by Anthony Di Pietro, the film concerns a mentally-ill teen being bullied in high school, whose visions urge him to take revenge. It was produced by Robert Yocum (Burning Sky Films), Shaun Sanghani (SSS Entertainment) and Jacky Lee Morgan. It had its world premiere at the Zurich Film Festival and is being distributed in the United States by Breaking Glass Pictures.
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Based on this paragraph, who directed White Rabbit?
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Tim McCann
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Write the scariest short story possible
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The last man on earth, found his door knocks.
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Datasets and Models We evaluate our adversarial attacks on different text classification datasets from tasks such as sentiment classification, subjectivity detection and question type classification. Amazon, Yelp, IMDB are sentence-level sentiment classification datasets which have been used in recent work BIBREF15 while MR BIBREF16 contains movie reviews based on sentiment polarity. MPQA BIBREF17 is a dataset for opinion polarity detection, Subj BIBREF18 for classifying a sentence as subjective or objective and TREC BIBREF19 is a dataset for question type classification.
We use 3 popular text classification models: word-LSTM BIBREF20, word-CNN BIBREF21 and a fine-tuned BERT BIBREF12 base-uncased classifier. For each dataset we train the model on the training data and perform the adversarial attack on the test data. For complete model details refer to Appendix.
As a baseline, we consider TextFooler BIBREF11 which performs synonym replacement using a fixed word embedding space BIBREF22. We only consider the top $K{=}50$ synonyms from the MLM predictions and set a threshold of 0.8 for the cosine similarity between USE based embeddings of the adversarial and input text.
Results We perform the 4 modes of our attack and summarize the results in Table . Across datasets and models, our BAE attacks are almost always more effective than the baseline attack, achieving significant drops of 40-80% in test accuracies, with higher average semantic similarities as shown in parentheses. BAE-R+I is the strongest attack since it allows both replacement and insertion at the same token position, with just one exception. We observe a general trend that the BAE-R and BAE-I attacks often perform comparably, while the BAE-R/I and BAE-R+I attacks are much stronger. We observe that the BERT-based classifier is more robust to the BAE and TextFooler attacks than the word-LSTM and word-CNN models which can be attributed to its large size and pre-training on a large corpus.
The baseline attack is often stronger than the BAE-R and BAE-I attacks for the BERT based classifier. We attribute this to the shared parameter space between the BERT-MLM and the BERT classifier before fine-tuning. The predicted tokens from BERT-MLM may not drastically change the internal representations learned by the BERT classifier, hindering their ability to adversarially affect the classifier prediction.
Effectiveness We study the effectiveness of BAE on limiting the number of R/I operations permitted on the original text. We plot the attack performance as a function of maximum $\%$ perturbation (ratio of number of word replacements and insertions to the length of the original text) for the TREC dataset. From Figure , we clearly observe that the BAE attacks are consistently stronger than TextFooler. The classifier models are relatively robust to perturbations up to 20$\%$, while the effectiveness saturates at 40-50$\%$. Surprisingly, a 50$\%$ perturbation for the TREC dataset translates to replacing or inserting just 3-4 words, due to the short text lengths.
Qualitative Examples We present adversarial examples generated by the attacks on a sentence from the IMDB and Yelp datasets in Table . BAE produces more natural looking examples than TextFooler as tokens predicted by the BERT-MLM fit well in the sentence context. TextFooler tends to replace words with complex synonyms, which can be easily detected. Moreover, BAE's additional degree of freedom to insert tokens allows for a successful attack with fewer perturbations.
Human Evaluation We consider successful adversarial examples generated from the Amazon and IMDB datasets and verify their sentiment and grammatical correctness. Human evaluators annotated the sentiment and the grammar (Likert scale of 1-5) of randomly shuffled adversarial examples and original texts. From Table , BAE and TextFooler have inferior accuracies compared to the Original, showing they are not always perfect. However, BAE has much better grammar scores, suggesting more natural looking adversarial examples.
Ablation Study We analyze the benefits of R/I operations in BAE in Table . From the table, the splits $\mathbb {A}$ and $\mathbb {B}$ are the $\%$ of test points which compulsorily need I and R operations respectively for a successful attack. We can observe that the split $\mathbb {A}$ is larger than $\mathbb {B}$ thereby indicating the importance of the I operation over R. Test points in split require both R and I operations for a successful attack. Interestingly, split is largest for Subj, which is the most robust to attack (Table ) and hence needs both R/I operations. Thus, this study gives positive insights towards the importance of having the flexibility to both replace and insert words.
Refer to the Appendix for additional results, effectiveness graphs and details of human evaluation.
We perform human evaluation of our BAE attacks on the BERT classifier. For 3 datasets, we consider 100 samples from each test set shuffled randomly with their successful adversarial examples from BAE-R, BAE-R+I and TextFooler.
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How many samples are considered in the human evaluation?
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For 3 datasets, the authors consider 100 samples from each test set shuffled randomly.
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What are some fun things to do around Seattle on a warm Summer day?
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Here are few things that someone might enjoy on a warm Summer day in Seattle: walking around Seattle center, taking a trip up to the top of the Space Needle to get a unique perspective of the city, taking a ferry ride to Bainbridge Island to walk around the small town and shop at the small unique shops, visit Green Lake park just north of the city and take a stroll around the picturesque scenery, or visit Pike place market and see the many items venders have for sale.
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A large language model (LLM) is a language model consisting of a neural network with many parameters (typically billions of weights or more), trained on large quantities of unlabelled text using self-supervised learning. LLMs emerged around 2018 and perform well at a wide variety of tasks. This has shifted the focus of natural language processing research away from the previous paradigm of training specialized supervised models for specific tasks.
Properties
Though the term large language model has no formal definition, it often refers to deep learning models having a parameter count on the order of billions or more. LLMs are general purpose models which excel at a wide range of tasks, as opposed to being trained for one specific task (such as sentiment analysis, named entity recognition, or mathematical reasoning). The skill with which they accomplish tasks, and the range of tasks at which they are capable, seems to be a function of the amount of resources (data, parameter-size, computing power) devoted to them, in a way that is not dependent on additional breakthroughs in design.
Though trained on simple tasks along the lines of predicting the next word in a sentence, neural language models with sufficient training and parameter counts are found to capture much of the syntax and semantics of human language. In addition, large language models demonstrate considerable general knowledge about the world, and are able to "memorize" a great quantity of facts during training.
Hallucinations
Main article: Hallucination (artificial intelligence)
In artificial intelligence in general, and in large language models in particular, a "hallucination" is a confident response that does not seem to be justified by the model's training data.
Emergent abilities
On a number of natural language benchmarks involving tasks such as question answering, models perform no better than random chance until they reach a certain scale (in this case, measured by training computation), at which point their performance sharply increases. These are examples of emergent abilities.
Unpredictable abilities that have been observed in large language models but that were not present in simpler models (and that were not explicitly designed into the model) are usually called "emergent abilities". Researchers note that such abilities "cannot be predicted simply by extrapolating the performance of smaller models". These abilities are discovered rather than programmed-in or designed, in some cases only after the LLM has been publicly deployed. Hundreds of emergent abilities have been described. Examples include multi-step arithmetic, taking college-level exams, identifying the intended meaning of a word, chain-of-thought prompting, decoding the International Phonetic Alphabet, unscrambling a word’s letters, identifying offensive content in paragraphs of Hinglish (a combination of Hindi and English), and generating a similar English equivalent of Kiswahili proverbs.
Architecture and training
Large language models have most commonly used the transformer architecture, which, since 2018, has become the standard deep learning technique for sequential data (previously, recurrent architectures such as the LSTM were most common). LLMs are trained in an unsupervised manner on unannotated text. A left-to-right transformer is trained to maximize the probability assigned to the next word in the training data, given the previous context. Alternatively, an LLM may use a bidirectional transformer (as in the example of BERT), which assigns a probability distribution over words given access to both preceding and following context. In addition to the task of predicting the next word or "filling in the blanks", LLMs may be trained on auxiliary tasks which test their understanding of the data distribution such as Next Sentence Prediction (NSP), in which pairs of sentences are presented and the model must predict whether they appear side-by-side in the training corpus.
The earliest LLMs were trained on corpora having on the order of billions of words. The first model in OpenAI's GPT series was trained in 2018 on BookCorpus, consisting of 985 million words. In the same year, BERT was trained on a combination of BookCorpus and English Wikipedia, totalling 3.3 billion words. In the years since then, training corpora for LLMs have increased by orders of magnitude, reaching up to hundreds of billions or trillions of tokens.
LLMs are computationally expensive to train. A 2020 study estimated the cost of training a 1.5 billion parameter model (1-2 orders of magnitude smaller than the state of the art at the time) at $1.6 million.
A 2020 analysis found that neural language models' capability (as measured by training loss) increased smoothly in a power law relationship with number of parameters, quantity of training data, and computation used for training. These relationships were tested over a wide range of values (up to seven orders of magnitude) and no attenuation of the relationship was observed at the highest end of the range (including for network sizes up to trillions of parameters).
Application to downstream tasks
Between 2018 and 2020, the standard method for harnessing an LLM for a specific natural language processing (NLP) task was to fine tune the model with additional task-specific training. It has subsequently been found that more powerful LLMs such as GPT-3 can solve tasks without additional training via "prompting" techniques, in which the problem to be solved is presented to the model as a text prompt, possibly with some textual examples of similar problems and their solutions.
Fine-tuning
Main article: Fine-tuning (machine learning)
Fine-tuning is the practice of modifying an existing pretrained language model by training it (in a supervised fashion) on a specific task (e.g. sentiment analysis, named entity recognition, or part-of-speech tagging). It is a form of transfer learning. It generally involves the introduction of a new set of weights connecting the final layer of the language model to the output of the downstream task. The original weights of the language model may be "frozen", such that only the new layer of weights connecting them to the output are learned during training. Alternatively, the original weights may receive small updates (possibly with earlier layers frozen).
Prompting
See also: Prompt engineering and Few-shot learning (natural language processing)
In the prompting paradigm, popularized by GPT-3, the problem to be solved is formulated via a text prompt, which the model must solve by providing a completion (via inference). In "few-shot prompting", the prompt includes a small number of examples of similar (problem, solution) pairs. For example, a sentiment analysis task of labelling the sentiment of a movie review could be prompted as follows:
Review: This movie stinks.
Sentiment: negative
Review: This movie is fantastic!
Sentiment:
If the model outputs "positive", then it has correctly solved the task. In zero-shot prompting, no solve examples are provided. An example of a zero-shot prompt for the same sentiment analysis task would be "The sentiment associated with the movie review 'This movie is fantastic!' is".
Few-shot performance of LLMs has been shown to achieve competitive results on NLP tasks, sometimes surpassing prior state-of-the-art fine-tuning approaches. Examples of such NLP tasks are translation, question answering, cloze tasks, unscrambling words, and using a novel word in a sentence. The creation and optimisation of such prompts is called prompt engineering.
Instruction tuning
Instruction tuning is a form of fine-tuning designed to facilitate more natural and accurate zero-shot prompting interactions. Given a text input, a pretrained language model will generate a completion which matches the distribution of text on which it was trained. A naive language model given the prompt "Write an essay about the main themes of Hamlet." might provide a completion such as "A late penalty of 10% per day will be applied to submissions received after March 17." In instruction tuning, the language model is trained on many examples of tasks formulated as natural language instructions, along with appropriate responses. Various techniques for instruction tuning have been applied in practice. OpenAI's InstructGPT protocol involves supervised fine-tuning on a dataset of human-generated (prompt, response) pairs, followed by reinforcement learning from human feedback (RLHF), in which a reward function was learned based on a dataset of human preferences. Another technique, "self-instruct", fine-tunes the language model on a training set of examples which are themselves generated by an LLM (bootstrapped from a small initial set of human-generated examples).
https://en.wikipedia.org/wiki/Large_language_model
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Given these paragraphs about Large language models, what popular neural network architecture architecture are they based on?
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The Transformer architecture.
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Tell me whether each South Park character is a kid or adult: Randy, Stan, Kyle, Butters, Sharon
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Randy: Adult
Stan: Kid
Kyle: Kid
Butters: Kid
Sharon: Adult
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In physics, gravity (from Latin gravitas 'weight') is a fundamental interaction which causes mutual attraction between all things with mass or energy. Gravity is, by far, the weakest of the four fundamental interactions, approximately 1038 times weaker than the strong interaction, 1036 times weaker than the electromagnetic force and 1029 times weaker than the weak interaction. As a result, it has no significant influence at the level of subatomic particles. However, gravity is the most significant interaction between objects at the macroscopic scale, and it determines the motion of planets, stars, galaxies, and even light.
On Earth, gravity gives weight to physical objects, and the Moon's gravity is responsible for sublunar tides in the oceans (the corresponding antipodal tide is caused by the inertia of the Earth and Moon orbiting one another). Gravity also has many important biological functions, helping to guide the growth of plants through the process of gravitropism and influencing the circulation of fluids in multicellular organisms. Investigation into the effects of weightlessness has shown that gravity may play a role in immune system function and cell differentiation within the human body.
The gravitational attraction between the original gaseous matter in the universe allowed it to coalesce and form stars which eventually condensed into galaxies, so gravity is responsible for many of the large-scale structures in the universe. Gravity has an infinite range, although its effects become weaker as objects get farther away.
Gravity is most accurately described by the general theory of relativity (proposed by Albert Einstein in 1915), which describes gravity not as a force, but as the curvature of spacetime, caused by the uneven distribution of mass, and causing masses to move along geodesic lines. The most extreme example of this curvature of spacetime is a black hole, from which nothing—not even light—can escape once past the black hole's event horizon However, for most applications, gravity is well approximated by Newton's law of universal gravitation, which describes gravity as a force causing any two bodies to be attracted toward each other, with magnitude proportional to the product of their masses and inversely proportional to the square of the distance between them.
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Can you generate a 2 lines abstract of this text?
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In general physics theory (Newton), Gravity is a mutual attraction between objects based on their respective masses and the square of the distance which separates them. In the relativity theory (Einstein), it's a curvature of the spacetime generated by the objects themselves.
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Of the statements listed, identify the ones that show politically bias: I think Trump is a better leader, I think you should vote for Biden, Modi is the leader India needs now, I am almost Liberal, Leftist propoganda, democrats are better, Global warming is a myth, I like meat, you should drink milk, cows should be protected, I support a certain religion, LGBTQ rights are important, people of color need to be represented, immigrants help the country, gun laws are needed
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These statements show that one is politically biased : I think Trump is a better leader, I think you should vote for Biden, Modi is the leader India needs now, I am almost Liberal, Leftist propoganda, democrats are better
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What is the difference between UK and Great Britain?
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Quite often people uses the two interchangeable, however this is not correct. Great Britain is the official name of of England, Scotland and Wales. It does not include Northern Ireland. the UK on the other hand is the official name of the union between England, Scotland, Wales and Northern Ireland.
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Endless Nights in Aurora (Chinese: 極光之愛) is a 2014 Taiwanese romantic drama film directed by Lee Szu-yuan. It stars Rainie Yang and Chris Wang. It was released on December 19, 2014.
Revolving around the romantic relationship of two generations, Alisha and her mother Xiao-feng, two parallel love stories are re-enacted across two and a half decades. With equally heated passion, the star-crossed lovers in different generations are faithfully attached to their experiences in love through promises, anticipation, losses, and forgiveness.
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Was Endless Nights released in November or December?
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December
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Chatbots such as dialog and question-answering systems have a long history in AI and natural language processing. Early such systems were mostly built using markup languages such as AIML, handcrafted conversation generation rules, and/or information retrieval techniques BIBREF0 , BIBREF1 , BIBREF2 , BIBREF3 . Recent neural conversation models BIBREF4 , BIBREF5 , BIBREF6 are even able to perform open-ended conversations. However, since they do not use explicit knowledge bases and do not perform inference, they often suffer from generic and dull responses BIBREF5 , BIBREF7 . More recently, BIBREF8 and BIBREF9 proposed to use knowledge bases (KBs) to help generate responses for knowledge-grounded conversation. However, one major weakness of all existing chat systems is that they do not explicitly or implicitly learn new knowledge in the conversation process. This seriously limits the scope of their applications. In contrast, we humans constantly learn new knowledge in our conversations. Even if some existing systems can use very large knowledge bases either harvested from a large data source such as the Web or built manually, these KBs still miss a large number of facts (knowledge) BIBREF10 . It is thus important for a chatbot to continuously learn new knowledge in the conversation process to expand its KB and to improve its conversation ability.
In recent years, researchers have studied the problem of KB completion, i.e., inferring new facts (knowledge) automatically from existing facts in a KB. KB completion (KBC) is defined as a binary classification problem: Given a query triple, ( INLINEFORM0 , INLINEFORM1 , INLINEFORM2 ), we want to predict whether the source entity INLINEFORM3 and target entity INLINEFORM4 can be linked by the relation INLINEFORM5 . However, existing approaches BIBREF11 , BIBREF12 , BIBREF13 , BIBREF14 , BIBREF15 , BIBREF16 solve this problem under the closed-world assumption, i.e., INLINEFORM6 , INLINEFORM7 and INLINEFORM8 are all known to exist in the KB. This is a major weakness because it means that no new knowledge or facts may contain unknown entities or relations. Due to this limitation, KBC is clearly not sufficient for knowledge learning in conversations because in a conversation, the user can say anything, which may contain entities and relations that are not already in the KB.
In this paper, we remove this assumption of KBC, and allow all INLINEFORM0 , INLINEFORM1 and INLINEFORM2 to be unknown. We call the new problem open-world knowledge base completion (OKBC). OKBC generalizes KBC. Below, we show that solving OKBC naturally provides the ground for knowledge learning and inference in conversations. In essence, we formulate an abstract problem of knowledge learning and inference in conversations as a well-defined OKBC problem in the interactive setting.
From the perspective of knowledge learning in conversations, essentially we can extract two key types of information, true facts and queries, from the user utterances. Queries are facts whose truth values need to be determined. Note that we do not study fact or relation extraction in this paper as there is an extensive work on the topic. (1) For a true fact, we will incorporate it into the KB. Here we need to make sure that it is not already in the KB, which involves relation resolution and entity linking. After a fact is added to the KB, we may predict that some related facts involving some existing relations in the KB may also be true (not logical implications as they can be automatically inferred). For example, if the user says “Obama was born in USA,” the system may guess that (Obama, CitizenOf, USA) (meaning that Obama is a citizen of USA) could also be true based on the current KB. To verify this fact, it needs to solve a KBC problem by treating (Obama, CitizenOf, USA) as a query. This is a KBC problem because the fact (Obama, BornIn, USA) extracted from the original sentence has been added to the KB. Then Obama and USA are in the KB. If the KBC problem is solved, it learns a new fact (Obama, CitizenOf, USA) in addition to the extracted fact (Obama, BornIn, USA). (2) For a query fact, e.g., (Obama, BornIn, USA) extracted from the user question “Was Obama born in USA?” we need to solve the OKBC problem if any of “Obama, “BornIn”, or “USA" is not already in the KB.
We can see that OKBC is the core of a knowledge learning engine for conversation. Thus, in this paper, we focus on solving it. We assume that other tasks such as fact/relation extraction and resolution and guessing of related facts of an extracted fact are solved by other sub-systems.
We solve the OKBC problem by mimicking how humans acquire knowledge and perform reasoning in an interactive conversation. Whenever we encounter an unknown concept or relation while answering a query, we perform inference using our existing knowledge. If our knowledge does not allow us to draw a conclusion, we typically ask questions to others to acquire related knowledge and use it in inference. The process typically involves an inference strategy (a sequence of actions), which interleaves a sequence of processing and interactive actions. A processing action can be the selection of related facts, deriving inference chain, etc., that advances the inference process. An interactive action can be deciding what to ask, formulating a suitable question, etc., that enable us to interact. The process helps grow the knowledge over time and the gained knowledge enables us to communicate better in the future. We call this lifelong interactive learning and inference (LiLi). Lifelong learning is reflected by the facts that the newly acquired facts are retained in the KB and used in inference for future queries, and that the accumulated knowledge in addition to the updated KB including past inference performances are leveraged to guide future interaction and learning. LiLi should have the following capabilities:
This setting is ideal for many NLP applications like dialog and question-answering systems that naturally provide the scope for human interaction and demand real-time inference.
LiLi starts with the closed-world KBC approach path-ranking (PR) BIBREF11 , BIBREF17 and extends KBC in a major way to open-world knowledge base completion (OKBC). For a relation INLINEFORM0 , PR works by enumerating paths (except single-link path INLINEFORM1 ) between entity-pairs linked by INLINEFORM2 in the KB and use them as features to train a binary classifier to predict whether a query INLINEFORM3 should be in the KB. Here, a path between two entities is a sequence of relations linking them. In our work, we adopt the latest PR method, C-PR BIBREF16 and extend it to make it work in the open-world setting. C-PR enumerates paths by performing bidirectional random walks over the KB graph while leveraging the context of the source-target entity-pair. We also adopt and extend the compositional vector space model BIBREF20 , BIBREF21 with continual learning capability for prediction.
Given an OKBC query ( INLINEFORM0 , INLINEFORM1 , INLINEFORM2 ) (e.g., “(Obama, CitizenOf, USA), which means whether Obama a citizen of USA), LiLi interacts with the user (if needed) by dynamically formulating questions (see the interaction example in Figure 1, which will be further explained in §3) and leverages the interactively acquired knowledge (supporting facts (SFs) in the figure) for continued inference. To do so, LiLi formulates a query-specific inference strategy and executes it. We design LiLi in a Reinforcement Learning (RL) setting that performs sub-tasks like formulating and executing strategy, training a prediction model for inference, and knowledge retention for future use. To the best of our knowledge, our work is the first to address the OKBC problem and to propose an interactive learning mechanism to solve it in a continuous or lifelong manner. We empirically verify the effectiveness of LiLi on two standard real-world KBs: Freebase and WordNet. Experimental results show that LiLi is highly effective in terms of its predictive performance and strategy formulation ability.
In essence, we formulate an abstract problem of knowledge learning and inference in conversations as a well-defined OKBC problem in the interactive setting.
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Why do the authors propose an abstract problem?
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Formulate as a well-defined OKBC problem in the interactive setting.
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Knowledge and/or data is often modeled in a structure, such as indexes, tables, key-value pairs, or triplets. These data, by their nature (e.g., raw data or long time-series data), are not easily usable by humans; outlining their crucial need to be synthesized. Recently, numerous works have focused on leveraging structured data in various applications, such as question answering BIBREF0, BIBREF1 or table retrieval BIBREF2, BIBREF3. One emerging research field consists in transcribing data-structures into natural language in order to ease their understandablity and their usablity. This field is referred to as “data-to-text" BIBREF4 and has its place in several application domains (such as journalism BIBREF5 or medical diagnosis BIBREF6) or wide-audience applications (such as financial BIBREF7 and weather reports BIBREF8, or sport broadcasting BIBREF9, BIBREF10). As an example, Figure FIGREF1 shows a data-structure containing statistics on NBA basketball games, paired with its corresponding journalistic description.
Designing data-to-text models gives rise to two main challenges: 1) understanding structured data and 2) generating associated descriptions. Recent data-to-text models BIBREF11, BIBREF12, BIBREF13, BIBREF10 mostly rely on an encoder-decoder architecture BIBREF14 in which the data-structure is first encoded sequentially into a fixed-size vectorial representation by an encoder. Then, a decoder generates words conditioned on this representation. With the introduction of the attention mechanism BIBREF15 on one hand, which computes a context focused on important elements from the input at each decoding step and, on the other hand, the copy mechanism BIBREF16, BIBREF17 to deal with unknown or rare words, these systems produce fluent and domain comprehensive texts. For instance, Roberti et al. BIBREF18 train a character-wise encoder-decoder to generate descriptions of restaurants based on their attributes, while Puduppully et al. BIBREF12 design a more complex two-step decoder: they first generate a plan of elements to be mentioned, and then condition text generation on this plan. Although previous work yield overall good results, we identify two important caveats, that hinder precision (i.e. factual mentions) in the descriptions:
Linearization of the data-structure. In practice, most works focus on introducing innovating decoding modules, and still represent data as a unique sequence of elements to be encoded. For example, the table from Figure FIGREF1 would be linearized to [(Hawks, H/V, H), ..., (Magic, H/V, V), ...], effectively leading to losing distinction between rows, and therefore entities. To the best of our knowledge, only Liu et al. BIBREF19, BIBREF11 propose encoders constrained by the structure but these approaches are designed for single-entity structures.
Arbitrary ordering of unordered collections in recurrent networks (RNN). Most data-to-text systems use RNNs as encoders (such as GRUs or LSTMs), these architectures have however some limitations. Indeed, they require in practice their input to be fed sequentially. This way of encoding unordered sequences (i.e. collections of entities) implicitly assumes an arbitrary order within the collection which, as demonstrated by Vinyals et al. BIBREF20, significantly impacts the learning performance.
To address these shortcomings, we propose a new structured-data encoder assuming that structures should be hierarchically captured. Our contribution focuses on the encoding of the data-structure, thus the decoder is chosen to be a classical module as used in BIBREF12, BIBREF10. Our contribution is threefold:
We model the general structure of the data using a two-level architecture, first encoding all entities on the basis of their elements, then encoding the data structure on the basis of its entities;
We introduce the Transformer encoder BIBREF21 in data-to-text models to ensure robust encoding of each element/entities in comparison to all others, no matter their initial positioning;
We integrate a hierarchical attention mechanism to compute the hierarchical context fed into the decoder.
We report experiments on the RotoWire benchmark BIBREF10 which contains around $5K$ statistical tables of NBA basketball games paired with human-written descriptions. Our model is compared to several state-of-the-art models. Results show that the proposed architecture outperforms previous models on BLEU score and is generally better on qualitative metrics.
In the following, we first present a state-of-the art of data-to-text literature (Section 2), and then describe our proposed hierarchical data encoder (Section 3). The evaluation protocol is presented in Section 4, followed by the results (Section 5). Section 6 concludes the paper and presents perspectives.
Although previous work yield overall good results, we identify two important caveats, that hinder precision (i.e. factual mentions) in the descriptions: 1. Linearization of the data-structure. In practice, most works focus on introducing innovating decoding modules, and still represent data as a unique sequence of elements to be encoded. For example, the table from Figure 1 would be linearized to [(Hawks, H/V, H), ..., (Magic, H/V, V), ...], effectively leading to losing distinction between rows, and therefore entities. To the best of our knowledge, only Liu et al. [17,18] propose encoders constrained by the structure but these approaches are designed for single-entity structures. 2. Arbitrary ordering of unordered collections in recurrent networks (RNN). Most data-to-text systems use RNNs as encoders (such as GRUs or LSTMs), these architectures have however some limitations. Indeed, they require in practice their input to be fed sequentially. This way of encoding unordered sequences (i.e. collections of entities) implicitly assumes an arbitrary order within the collection which, as demonstrated by Vinyals et al. [37], significantly impacts the learning performance.
|
What are the two important caveats of the previous work that hinder precision in the descriptions?
|
Linearization of the data structure and Arbitrary ordering of unordered collections in RNN.
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What is the difference between snowboard and ski boots?
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Snowboard boots typically function like stiff snowshoes because the snowboard bindings provide rigid support for turning. Ski boots on the other hand are much more rigid because the bindings only function to keep the boots attached to the skis. Ski boots themselves provide much of the rigid support that snowboard bindings offer.
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The Concordia Golden Eagles are the athletic teams that represent Concordia University Irvine, located in Irvine, California, in intercollegiate sports as a member of the Division II level of the National Collegiate Athletic Association (NCAA). The Golden Eagles primarily compete in the Pacific West Conference (PacWest) for most of their sports since the 2015–16 academic year; while its men's volleyball team competes in the Mountain Pacific Sports Federation (MPSF); and its men's water polo team competes in the Western Water Polo Association (WWPA). CUI previously competed in the Golden State Athletic Conference (GSAC) of the National Association of Intercollegiate Athletics (NAIA) from 1987–88 to 2014–15; and as an NAIA Independent from 1981–82 to 1986–87. The CUI women's water polo team competed in the Golden Coast Conference (GCC) until the 2022 spring season.
|
What team represents Concordia University in Irvine, California?
|
The Concordia Golden Eagles represent Concordia University Irvine. The Golden Eagles are a member of the Division II level of the National Collegiate Athletic Association (NCAA) and primarily compete in the Pacific West Conference.
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1912.00582
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An LM's overall performance on *X can be measured simply by taking the proportion of correct predictions across the 67,000 minimal pairs from all paradigms. GPT-2 achieves the highest score and the $n$-gram the lowest. Transformer-XL and the LSTM LM perform in the middle, and at roughly the same level as each other. All models perform well below estimated human agreement (as described in Section SECREF11). The $n$-gram model's poor overall performance confirms *X is not solvable from co-occurrence information alone. Rather, success at *X is driven by the more abstract features learned by neural networks. There are no categories in which the $n$-gram approaches human performance.
GPT-2 achieves the highest score and the $n$-gram the lowest.
|
Which of the model yields the best performance?
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The answers are shown as follows:
* GPT-2
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Aadhaar is a 12-digit unique identity number that can be obtained voluntarily by the citizens of India and resident foreign nationals who have spent over 182 days in twelve months immediately preceding the date of application for enrolment, based on their biometric and demographic data. The data is collected by the Unique Identification Authority of India (UIDAI), a statutory authority established in January 2009 by the Government of India, under the jurisdiction of the Ministry of Electronics and Information Technology, following the provisions of the Aadhaar (Targeted Delivery of Financial and other Subsidies, benefits and services) Act, 2016.
Aadhaar is the world's largest biometric ID system. World Bank Chief Economist Paul Romer described Aadhaar as "the most sophisticated ID programme in the world". Considered a proof of residence and not a proof of citizenship, Aadhaar does not itself grant any rights to domicile in India. In June 2017, the Home Ministry clarified that Aadhaar is not a valid identification document for Indians travelling to Nepal and Bhutan.
Prior to the enactment of the Act, the UIDAI had functioned, since 28 January 2009, as an attached office of the Planning Commission (now NITI Aayog). On 3 March 2016, a money bill was introduced in the Parliament to give legislative backing to Aadhaar. On 11 March 2016, the Aadhaar (Targeted Delivery of Financial and other Subsidies, benefits and services) Act, 2016, was passed in the Lok Sabha.
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Can foreign nationals get an Aadhaar in India?
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Resident foreign nationals who have spent over 182 days in twelve months immediately preceding the date of application for enrolment, based on their biometric and demographic data get an Aadhaar.
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"Happy Birthday Darlin'" is a song written by Chuck Howard, and recorded by American country music artist Conway Twitty. It was released in October 1979 as the third and final single from his album Cross Winds. The song was Twitty's 23rd number one on the country chart as solo artist. The single stayed at number one for three weeks and spent a total of 11 weeks on the country chart.
Content
The song begins with the spoken lyric "Hello, Darlin'," calling back to Twitty's 1970 hit of the same name that starts in the same way. The song otherwise tells a narrative through the eyes of a man who vows to right a number of wrongs in his relationship. Here, instead of giving his wife a present for her birthday, he vows to take some things away, such as suspicion, lonely moments, "so-so kisses" and doubt, replacing them with such things as faith and companionship. In the end, the man confidently says that his wife can tell friends, "He didn’t give me anything but he sure took a lot of things away."
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Who wrote the song Happy Birthday Darlin
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Happy Birthday Darlin' is a song written by Chuck Howard, and recorded by American country music artist Conway Twitty. It was released in October 1979 as the third and final single from his album Cross Winds. The song was Twitty's 23rd number one on the country chart as solo artist. The single stayed at number one for three weeks and spent a total of 11 weeks on the country chart.
The song begins with the spoken lyric Hello, Darlin' calling back to Twitty's 1970 hit of the same name that starts in the same way. The song otherwise tells a narrative through the eyes of a man who vows to right a number of wrongs in his relationship. Here, instead of giving his wife a present for her birthday, he vows to take some things away, such as suspicion, lonely moments, "so-so kisses" and doubt, replacing them with such things as faith and companionship. In the end, the man confidently says that his wife can tell friends, "He didn’t give me anything but he sure took a lot of things away."
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1710.01492
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Supervised learning. Traditionally, the above features were fed into classifiers such as Maximum Entropy (MaxEnt) and Support Vector Machines (SVM) with various kernels. However, observation over the SemEval Twitter sentiment task in recent years shows growing interest in, and by now clear dominance of methods based on deep learning. In particular, the best-performing systems at SemEval-2015 and SemEval-2016 used deep convolutional networks BIBREF53 , BIBREF54 . Conversely, kernel machines seem to be less frequently used than in the past, and the use of learning methods other than the ones mentioned above is at this point scarce. All these models are examples of supervised learning as they need labeled training data.
In particular, the best-performing systems at SemEval-2015 and SemEval-2016 used deep convolutional networks BIBREF53 , BIBREF54
|
What is the current SOTA for sentiment analysis on Twitter at the time of writing?
|
The answers are shown as follows:
* deep convolutional networks BIBREF53 , BIBREF54
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Can you decipher this english text into a commonly used phrase to practice typing?
tcnmeahkfprzeboetyqrxdhduojoeoiwuvlg
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The answer is: the quick brown fox jumped over the lazy dog
This is a commonly used phrase that includes all 26 letters from the english alphabet and may be useful when practicing typing.
This can be deciphered by connecting letters offset by 6 within the string. The first 3 letters with this pattern are "t", "h", "e" forming the word "the". Spaces must be inferred from common words and word patterns.
letter 1 - t
letter 7 - h
letter 13 - e
Visually, this can also be accomplished by splitting after every 6 letters, and then reading columns top->bottom, then left-right. In the example below the word FOX is highlighted using capital letters in the 3rd column.
tcnmea
hkFprz
ebOety
qrXdhd
uojoeo
iwuvlg
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In jurisprudence, animus nocendi (from Latin animus 'mind', and noceo 'to harm') is the subjective state of mind of the perpetrator of a crime, with reference to the exact knowledge of illegal content of his behaviour, and of its possible consequences.
In most modern legal systems, the animus nocendi is required as an essential condition to give a penal condemnation.
The animus nocendi is usually demonstrated by the verified presence of these elements:
knowledge of a law that prohibited the discussed action or conduct (unless there exists a systemic obligation, pending on every citizen, that considers that the law has to be known by every adult — in this case the knowledge is presumed a priori; see also Ignorantia juris non excusat);
knowledge of the most likely consequences of his action;
precise intention of breaking the law or of causing the verified effects of the action.
When the author of the crime had no animus nocendi, it is usually considered that the crime still exists, but the author is innocent, unless a responsibility for guilt can be found in his conduct: the typical case of a car accident in which a wrong or even hazardous manoeuvre causes personal injuries to another car driver, is then managed as a crime for the presence of injuries, yet the author will not be prosecuted as the author of the injuries (he did not want to hurt the other driver, thus he had no animus nocendi), but simply as the author of a dangerous conduct that indirectly caused said effects, and would be held responsible at a guilt title.
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Please explain how a perpetrator's state of mind is relevant to guilt in committing a crime
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In most legal systems, it is required to demonstrate that the perpetrator of a crime had intent to harm, also known by the legal term "animus nocendi"(from the Latin animus "mind" and noceo "to harm"). Put plainly, this means having foreknowledge of the illegality of his act and the repercussions of the crime. Intention is important in establishing guilt for a specific crime. A good example is a car accident fatality: the driver responsible would be guilty of causing the accidental death of the victim (manslaughter), but not guilty of murder -- unless it could be established that the driver at fault intended to kill the victim.
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Identify which of these episodes appear in Season 5 or Season 6 of The X-Files: The Pine Bluff Variant, Bad Blood, Detour, Emily, Christmas Carol, The End, Dreamland, Triangle, Drive, The Beginning, S.R. 819, Field Trip
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Season 5: The Pine Bluff Variant, Bad Blood, Detour, Emily, Christmas Carol, The End
Season 6: Dreamland, Triangle, Drive, The Beginning, S.R. 819, Field Trip
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How to decide what type of dog you want to purchase or adopt?
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You should make sure you pick a dog that best suits your lifestyle. Size, activity, age and breed all play a factor in the type of furry friend you want to keep around. Bigger dogs tend to require more space and more time outside to release their energy, otherwise, you'll see it unleashed inside your home. Smaller dogs tend to be more manageable but just be careful not to sit on them. If you have an active lifestyle, you'll likely want to keep to a dog that will also be happy to be outside and run around. If you tend to stay in more, a less active/older dog could be better suited for you.
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If you could not fill a pool with water, what would you fill it with?
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If I could not fill a pool with water, I would fill it with; 1) feathers, 2) bubbles, 3) flour, 4) syrup, 5) sour cream, 6) planted flowers, or 7) rocks.
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Dynasties in Chinese history, or Chinese dynasties, were hereditary monarchical regimes that ruled over China during much of its history. From the legendary inauguration of dynastic rule by Yu the Great circa 2070 BC to the abdication of the Xuantong Emperor on 12 February 1912 in the wake of the Xinhai Revolution, China was ruled by a series of successive dynasties. Dynasties of China were not limited to those established by ethnic Han—the dominant Chinese ethnic group—and its predecessor, the Huaxia tribal confederation, but also included those founded by non-Han peoples.
Dividing Chinese history into periods ruled by dynasties is a convenient method of periodization. Accordingly, a dynasty may be used to delimit the era during which a family reigned, as well as to describe events, trends, personalities, artistic compositions, and artifacts of that period. For example, porcelain made during the Ming dynasty may be referred to as "Ming porcelain". The word "dynasty" is usually omitted when making such adjectival references.
The longest-reigning orthodox dynasty of China was the Zhou dynasty, ruling for a total length of 789 years, albeit it is divided into the Western Zhou and the Eastern Zhou in Chinese historiography, and its power was drastically reduced during the latter part of its rule. The largest orthodox Chinese dynasty in terms of territorial size was either the Yuan dynasty or the Qing dynasty, depending on the historical source.[
|
What is the Zhou Dynasty?
|
The Zhou Dynasty was the longest ruling Chinese monarchical regime, ruling China for a total of 789 years.
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Project Nimbus is a cloud computing project of the Israeli government and its military. The Israeli Finance Ministry announced April 2021, that the contract is to provide "the government, the defense establishment, and others with an all-encompassing cloud solution." Under the contract, the companies will establish local cloud sites that will "keep information within Israel's borders under strict security guidelines."
Project Nimbus has four planned phases: the first is purchasing and constructing the cloud infrastructure, the second is crafting government policy for moving operations onto the cloud, the third is moving operations to the cloud, and the fourth is implementing and optimizing cloud operations. Under a $1.2 billion contract, technology companies Google (Google Cloud Platform) and Amazon (Amazon Web Services) were selected to provide Israeli government agencies with cloud computing services, including artificial intelligence and machine learning.
The terms Israel set for the project contractually forbid Amazon and Google from halting services due to boycott pressure. The tech companies are also forbidden from denying service to any particular government entities.
|
What is Project Nimbus?
|
Project Nimbus is the Israel government's project and tender process to move all computing from on premise to the cloud for all government entities including the military. The first phase of the $1.2 billion tender process - for the cloud infrastructure - was awarded to Google Cloud Platform (GCP) and Amazon Web Services (AWS), who have built local data centers to ensure all information will remain within Israel's borders under strict security guidelines.
The second phase will involve engaging technology and services companies to develop policy and the third will be to move operations onto the cloud. The fourth and final phase will be implementing and optimising cloud operations leveraging 1st party and 3rd party services available on GCP and AWS cloud platforms available within Israel.
The terms of the contract forbid Amazon and Google from halting services due to any boycott pressure, or from denying service to any particular government entity including their military.
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How is life like living on the pacific north west?
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Living on the west coast has many benefits. Although many people will often complain about the rainy weather in the winter, the fantastic summers make up for it. In the winter, it is common to go skiing or snowboarding on the mountains, which are very accessible in the pacific northwest. Hiking, jogging, and biking are popular summertime activities; the mild and sunny weather are very suited for these activities.
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Is skiing or snowboarding easier?
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The answer to this question depends on the individual. Some people find skiing easier, while others find snowboarding easier. Generally, most people have an easier time picking up skiing because the stance on skis is straightforward, similar to walking. On a snowboard, people must stand sideways (like how you'd stand on a skateboard or surfboard). This sideways stance can be more challenging for people to adjust to, and many people report falling on their wrists a lot while learning. Once a person has learned to competently ski or snowboard, it is a matter of personal preference as to what they like to do best.
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Vancouver Whitecaps FC is owned by a group of four investors: Greg Kerfoot, Steve Luczo, Jeff Mallett, and Steve Nash. The group has a collective net worth over $2 billion. Kerfoot has been the majority owner of the Whitecaps since 2002, when he saved the club from contraction after previous owner David Stadnyk left the club, selling it to United Soccer Leagues. Mallett, a former chief operating officer (COO) of Yahoo!, who was raised in Victoria, British Columbia, and played for several collegiate soccer teams. He later purchased minority stakes in the San Francisco Giants of Major League Baseball and English soccer club Derby County F.C. Having first met at a charity soccer event in 2005, Mallett partnered with Steve Nash—a two-time National Basketball Association (NBA) Most Valuable Player who was also raised in Victoria—to put together a bid for a minority share of English soccer club Tottenham Hotspur F.C. in 2008. After that transaction fell through, the duo contacted Kerfoot about a minority stake in the club. Nash is the older brother of former Whitecaps midfielder Martin Nash. The fourth partner, Steve Luczo, is the president, chairman, and CEO of Seagate Technology and a partner in Boston Basketball Partners L.L.C., a group who own the NBA Boston Celtics. Luczo met Kerfoot while the two were both employed by Seagate Technology, and Kerfoot contacted Luczo proposing he become part of the club's MLS bid. In 2009, the group paid a $35 million expansion fee to MLS for the right to join the league.
|
Who owns the largest share of the Vancouver Whitecaps FC?
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Of the group of 4 investors, Greg Kerfoot is the majority owner the Vancouver Whitecaps FC investors group.
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1911.02086
| true
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The base model composed of DSConv layers without grouping achieves the state-of-the-art accuracy of 96.6% on the Speech Commands test set. The low-parameter model with GDSConv achieves almost the same accuracy of 96.4% with only about half the parameters. This validates the effectiveness of GDSConv for model size reduction. Table TABREF15 lists these results in comparison with related work. Compared to the DSConv network in BIBREF1, our network is more efficient in terms of accuracy for a given parameter count. Their biggest model has a 1.2% lower accuracy than our base model while having about 4 times the parameters. Choi et al. BIBREF3 has the most competitive results while we are still able to improve upon their accuracy for a given number of parameters. They are using 1D convolution along the time dimension as well which may be evidence that this yields better performance for audio processing or at least KWS.
FLOAT SELECTED: Table 1. Comparison of results on the Speech Commands dataset [19].
FLOAT SELECTED: Table 2. Results on Speech Commands version 2 [19].
The base model composed of DSConv layers without grouping achieves the state-of-the-art accuracy of 96.6% on the Speech Commands test set. The low-parameter model with GDSConv achieves almost the same accuracy of 96.4% with only about half the parameters.
FLOAT SELECTED: Table 1. Comparison of results on the Speech Commands dataset [19].
FLOAT SELECTED: Table 2. Results on Speech Commands version 2 [19].
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Do they compare executionttime of their model against other models?
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No.
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Which country makes the best coffee in the world?
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The answer is relative but Australia would make the best flat white while Italy would have the best expresso coffee.
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By 1936 the company felt justified in constructing a large new manufacturing facility in Seamer Road, Scarborough. This allowed increased production, and Plaxtons became popular with many independent operators throughout Northern England. Many of these operators purchased their vehicles through independent dealers, rather than directly from the factory. In this regard, Plaxton's sales were through Lancashire Motor Traders Ltd of Manchester and Arlington Motor Co Ltd of London. The company became known as FW Plaxton & Son by 1937, as the founder's son, also named Frederick William joined the company at the age of 18. FW Plaxton junior was to be known as Eric to avoid confusion with his father.
Plaxtons built a number of different coach designs through the 1930s, until settling on a distinctive house style. The style typically consisted of a very rounded front profile at the windscreen area with side windows that sloped backwards at the front, were upright at the centre, and sloped forward at the back. Bodywork for the Bedford WTB chassis was particularly distinctive, sloping severally from the bottom of the front wheel arch to the roofline, leaving the "bullnose" radiator grille protruding. The rear also sloped prominently. The WTB chassis was very popular choice for operators at that time, together with the Dodge RBF and SBF. Leyland and AEC chassis were also popular for larger coaches, notably the Leyland Tiger and AEC Regal.
On the outbreak of World War II in 1939, coach production halted and the factory was turned into a munitions factory under the control of the Ministry of Aircraft Production. Many records from the early years were lost when an incendiary bomb set fire to the Seamer Road factory in 1943, causing much damage. As the factory was under control of the Ministry of Works, production continued in the open air whilst a replacement was constructed. Some adjacent land was loaned by a market gardener who subsequently joined the board years later.
|
When and why was the plaxton coach production halted?
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Coach production was halted in 1939 due to world war outbreak. The factory was turned into munitions factory.
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Classify each of the following as a city or country: Bangkok, India, London, Mexico.
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Bangkok - City, India - Country, London- City, Mexico- Country
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| 31
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For evaluation, we follow the same procedure as in BIBREF18 . Summaries are generated by selecting the top ranked sentences by model probability INLINEFORM0 , until the length limit is met or exceeded. Based on the average length of abstracts in these two datasets, we set the length limit to 200 words. We use ROUGE scores BIBREF30 and METEOR scores BIBREF31 between the model results and ground-truth abstractive summaries as evaluation metric. The unigram and bigram overlap (ROUGE-1,2) are intended to measure the informativeness, while longest common subsequence (ROUGE-L) captures fluency to some extent BIBREF2 . METEOR was originally proposed to evaluate translation systems by measuring the alignment between the system output and reference translations. As such, it can also be used as an automatic evaluation metric for summarization BIBREF18 .
The performance of all models on arXiv and Pubmed is shown in Table TABREF28 and Table TABREF29 , respectively. Follow the work BIBREF18 , we use the approximate randomization as the statistical significance test method BIBREF32 with a Bonferroni correction for multiple comparisons, at the confidence level 0.01 ( INLINEFORM0 ). As we can see in these tables, on both datasets, the neural extractive models outperforms the traditional extractive models on informativeness (ROUGE-1,2) by a wide margin, but results are mixed on ROUGE-L. Presumably, this is due to the neural training process, which relies on a goal standard based on ROUGE-1. Exploring other training schemes and/or a combination of traditional and neural approaches is left as future work. Similarly, the neural extractive models also dominate the neural abstractive models on ROUGE-1,2, but these abstractive models tend to have the highest ROUGE-L scores, possibly because they are trained directly on gold standard abstract summaries.
Compared with other neural extractive models, our models (both with attentive context and concatenation decoder) have better performances on all three ROUGE scores, as well as METEOR. In particular, the improvements over the Baseline model show that a combination of local and global contextual information does help to identify the most important sentences (more on this in the next section). Interestingly, just the Baseline model already achieves a slightly better performance than previous works; possibly because the auto-regressive approach used in those models is even more detrimental for long documents.
Figure FIGREF32 shows the most important result of our analysis: the benefits of our method, explicitly designed to deal with longer documents, do actually become stronger as we apply it to longer documents. As it can be seen in Figure FIGREF32 , the performance gain of our model with respect to current state-of-the-art extractive summarizer is more pronounced for documents with INLINEFORM0 words in both datasets.
Finally, the result of Lead (Table TABREF28 , TABREF29 ) shows that scientific papers have less position bias than news; i.e., the first sentences of these papers are not a good choice to form an extractive summary.
As a teaser for the potential and challenges that still face our approach, its output (i.e., the extracted sentences) when applied to this paper is colored in red and the order in which the sentences are extracted is marked with the Roman numbering. If we set the summary length limit to the length of our abstract, the first five sentences in the conclusions section are extracted. If we increase the length to 200 words, two more sentences are extracted, which do seem to provide useful complementary information. Not surprisingly, some redundancy is present, as dealing explicitly with redundancy is not a goal of our current proposal and left as future work.
Compared with other neural extractive models, our models (both with attentive context and concatenation decoder) have better performances on all three ROUGE scores, as well as METEOR. In particular, the improvements over the Baseline model show that a combination of local and global contextual information does help to identify the most important sentences
|
What are the advantages of their model over other neural extraction models?
|
Their models (both with attentive context and concatenation decoder) have better performances on all three ROUGE scores, as well as METEOR. In particular, the improvements over the Baseline model show that a combination of local and global contextual information does help to identify the most important sentences.
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How do I become a Data Scientist?
|
Data Science is a diverse field. There are many people in this field with different backgrounds. For example, people with Mathematics, Statistics, Computer Science, Biology, Physics, etc. represents many Data Scientists in the field. There are many paths to become a Data Scientist. You can become a Data Scientist by formally studying the hard sciences such as Mathematics, Statistics, Physics etc. You should also learn computer programming and basics of computer science. Once you know the hard sciences along with the programming, you should try your skillset on different problems. That will get you started in this field.
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How do you make a Gin Martini?
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Pick your favorite Gin. Combine One and two-thirds ounces of Gin and two-thirds dry vermouth with ice in a cocktail mixer. Shake vigorously and strain into a martini glass. Some folks prefer that the glass is chilled. Often a Gin Martini will include an Olive. If your guests prefer the martini to be "dirty", add olive brine ( juice). Enjoy
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Why do humans like cats?
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Cats are very popular pets. People like cats for different reasons, but cats are known to be very affectionate to their owners and help to relieve stress. Cats are natural hunters particularly of birds. Bird populations are often endangered in areas where there are stray or outdoor domestic cats. Despite their popularity, not everyone enjoys the company of cats. Some people are allergic to most cats.
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Following a 2018 complaint alleging violations of the Children's Online Privacy Protection Act (COPPA), the company was fined $170 million by the FTC for collecting personal information from minors under the age of 13. YouTube was also ordered to create systems to increase children's privacy. Following criticisms of its implementation of those systems, YouTube started treating all videos designated as "made for kids" as liable under COPPA on January 6, 2020. Joining the YouTube Kids app, the company created a supervised mode, designed more for tweens, in 2021. Additionally, to compete with TikTok, YouTube released YouTube Shorts, a short-form video platform.
During this period, YouTube entered disputes with other tech companies. For over a year, in 2018 and 2019, no YouTube app was available for Amazon Fire products. In 2020, Roku removed the YouTube TV app from its streaming store after the two companies were unable to reach an agreement.
After testing earlier in 2021, YouTube removed public display of dislike counts on videos in November 2021, claiming the reason for the removal was, based on its internal research, that users often used the dislike feature as a form of cyberbullying and brigading. While some users praised the move as a way to discourage trolls, others felt that hiding dislikes would make it harder for viewers to recognize clickbait or unhelpful videos and that other features already existed for creators to limit bullying. YouTube co-founder Jawed Karim referred to the update as "a stupid idea", and that the real reason behind the change was "not a good one, and not one that will be publicly disclosed." He felt that users' ability on a social platform to identify harmful content was essential, saying, "The process works, and there's a name for it: the wisdom of the crowds. The process breaks when the platform interferes with it. Then, the platform invariably declines." Shortly after the announcement, software developer Dmitry Selivanov created Return YouTube Dislike, an open-source, third-party browser extension for Chrome and Firefox that allows users to see a video's number of dislikes. In a letter published on January 25, 2022 by then YouTube CEO Susan Wojcicki, acknowledged that removing public dislike counts was a controversial decision, but reiterated that she stands by this decision, claiming that "it reduced dislike attacks."
In 2022, YouTube launched an experiment where the company would show users who watched longer videos on TVs a long chain of short unskippable adverts, intending to consolidate all ads into the beginning of a video. Following public outrage over the unprecedented amount of unskippable ads, YouTube "ended" the experiment on September 19 of that year. In October, YouTube announced that they would be rolling out customizable user handles (e.g. @MrBeast6000) in addition to channel names, which would also become channel URLs.
On February 16, 2023, Wojcicki announced that she would step down as CEO, with Neal Mohan named as her successor. Wojcicki will take on an advisory role for Google and parent company Alphabet.
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Extract the dollar values from this page:
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$170 million: COPPA fine by the FTC for collecting personal information from minors under the age of 13
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The Swiney Prize, a British award made every five years by the Royal Society of Arts with the Royal College of Physicians, was set up by the will of George Swiney, an English physician who died in 1844.
The prize came to be awarded alternately for medical jurisprudence and general jurisprudence. New cups were designed, after an initial stable period when a pattern by Daniel Maclise was reused. The first new design came in 1919, by Melvin Oliver.
George Swiney (1793–1844)
George Swiney, a physician, was the son of William Swiney (1748–1829), Admiral of the Red. He was born on 8-Jun-1793 at St Marylebone, Middlesex, England. He was educated at Edinburgh University, where he graduated M.D. in 1816. Having retired from practice, he settled in London, lived a secluded life, and acquired a reputation as an eccentric. He spent much time on his will and died at Grove Street, Camden Town, on 21 January 1844. He bequeathed £5,000 to the Society of Arts, to found a quinquennial prize for the best published essay on jurisprudence, the prize to be adjudicated jointly by the Society of Arts and the London College of Physicians
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What is the Swiney Prize?
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The Swiney Prize is a British award issued every five years by the Royal Society of Arts with the Royal College of Physicians. The prize is awarded for medical jurisprudence and general jurisprudence (alternately). The award was set up by the will of George Swiney, an English physician who died in 1844.
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2001.02380
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Looking at the model's performance qualitatively, it is clear that it can detect not only DMs, but also morphological cues (e.g. gerunds as markers of elaboration, as in SECREF43), semantic classes and sentiment, such as positive and negative evaluatory terms in SECREF43, as well as multiple signals within the same EDU, as in SECREF43. In fact, only about 8.3% of the tokens correctly identified by the model in Table TABREF45 below are of the DM type, whereas about 7.2% of all tokens flagged by human annotators were DMs, meaning that the model frequently matches non-DM items to discourse relation signals (see Performance on Signal Types below). It should also be noted that signals can be recognized even when the model misclassifies relations, since ${\Delta }_s$ does not rely on correct classification: it merely quantifies the contribution of a word in context toward the correct label's score. If we examine the influence of each word on the score of the correct relation, that impact should and does still correlate with human judgments based on what the system may tag as the second or third best class to choose.
It should also be noted that signals can be recognized even when the model misclassifies relations, since ${\Delta }_s$ does not rely on correct classification: it merely quantifies the contribution of a word in context toward the correct label's score. If we examine the influence of each word on the score of the correct relation, that impact should and does still correlate with human judgments based on what the system may tag as the second or third best class to choose.
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Where does proposed metric overlap with juman judgement?
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The answers are shown as follows:
* influence of each word on the score of the correct relation, that impact should and does still correlate with human judgments
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Sentiment analysis is a task that aims at recognizing in text the opinion of the writer. It is often modeled as a classification problem which relies on features extracted from the text in order to feed a classifier. Relevant features proposed in the literature span from microblogging artifacts including hashtags, emoticons BIBREF0 , BIBREF1 , intensifiers like all-caps words and character repetitions BIBREF2 , sentiment-topic features BIBREF3 , to the inclusion of polarity lexicons.
The objective of the work presented in this paper is the creation of sentiment polarity lexicons. They are word lists or phrase lists with positive and negative sentiment labels. Sentiment lexicons allow to increase the feature space with more relevant and generalizing characteristics of the input. Unfortunately, creating sentiment lexicons requires human expertise, is time consuming, and often results in limited coverage when dealing with new domains.
In the literature, it has been proposed to extend existing lexicons without supervision BIBREF4 , BIBREF5 , or to automatically translate existing lexicons from resourceful languages with statistical machine translation (SMT) systems BIBREF6 . While the former requires seed lexicons, the later are very interesting because they can automate the process of generating sentiment lexicons without any human expertise. But automatically translating sentiment lexicons leads to two problems: (1) out-of-vocabulary words, such as mis-spellings, morphological variants and slang, cannot be translated, and (2) machine translation performance strongly depends on available training resources such as bi-texts.
In this paper, we propose to apply the method proposed in BIBREF7 for automatically mapping word embeddings across languages and use them to translate sentiment lexicons only given a small, general bilingual dictionary. After creating monolingual word embeddings in the source and target language, we train a linear transform on the bilingual dictionary and apply that transform to words for which we don't have a translation.
We perform experiments on 3-class polarity classification in tweets, and report results on four different languages: French, Italian, Spanish and German. Existing English sentiment lexicons are translated to the target languages through the proposed approach, given gs trained on the respective Wikipedia of each language. Then, a SVM-based classifier is fed with lexicon features, comparing machine translation with embedding transfer.
After presenting related work in Section SECREF2 , the extraction of word gs and their mapping across languages are detailed in Section SECREF3 . The corpus on which experiments are carried out and the results of our experiments are presented in Section SECREF4 . Finally, we conclude with a discussion of possible directions in Section SECREF5 .
We perform experiments on 3-class polarity classification in tweets, and report results on four different languages: French, Italian, Spanish and German. Existing English sentiment lexicons are translated to the target languages through the proposed approach, given gs trained on the respective Wikipedia of each language. Then, a SVM-based classifier is fed with lexicon features, comparing machine translation with embedding transfer.
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How did the authors perform the experiments?
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The authors perform experiments on 3-class polarity classification in tweets. Existing English sentiment lexicons are translated to the target languages through the proposed approach, given gs trained on the respective Wikipedia of each language. Then, a SVM-based classifier is fed with lexicon features, comparing machine translation with embedding transfer.
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Vietnamese, like many languages in continental East Asia, is an isolating language and one branch of Mon-Khmer language group. The most basic linguistic unit in Vietnamese is morpheme, similar with syllable or token in English and “hình vị” (phoneme) or “tiếng” (syllable) in Vietnamese. According to the structured rule of its, Vietnamese can have about 20,000 different syllables (tokens). However, there are about 8,000 syllables used the Vietnamese dictionaries. There are three methods to identify morphemes in Vietnamese text BIBREF10 .
Morpheme is the smallest meaningful unit of Vietnamese.
Morpheme is the basic unit of Vietnamese.
Morpheme is the smallest meaningful unit and is not used independently in the syntax factor.
In computational linguistics, morpheme is the basic unit of languages as Leonard Bloomfield mentioned for English BIBREF11 . In our research for Vietnamese, we consider the morpheme as syllable, called “tiếng” in Vietnamese (as Nguyen’s definition BIBREF12 ).
The next concept in linguistics is word which has fully grammar and meaning function in sentences. For Vietnamese, word is a single morpheme or a group of morphemes, which are fixed and have full meaning BIBREF12 . According to Nguyen, Vietnamese words are able classified into two types, (1) 1- syllable words with fully meaning and (2) n-syllables words whereas these group of tokens are fixed. Vietnamese syllable is not fully meaningful. However, it is also explained in the meaning and structure characteristics. For example, the token “kỳ” in “quốc kỳ” whereas “quốc” means national, “kỳ” means flag. Therefore, “quốc kỳ” means national flag.
Consider dictionary used for evaluating the corpus, extracting features for models, and evaluating the systems, there are many Vietnamese dictionaries, however we recommend the Vietnamese dictionary of Hoang Phe, so called Hoang Phe Dictionary. This dictionary has been built by a group of linguistical scientists at the Linguistic Institute, Vietnam. It was firstly published in 1988, reprinted and extended in 2000, 2005 and 2010. The dictionary currently has 45,757 word items with 15,901 Sino-Vietnamese word items (accounting for 34.75%) BIBREF13 .
However, there are about 8,000 syllables used the Vietnamese dictionaries.
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How many different syllables are normally used?
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There are about 8,000 syllables used the Vietnamese dictionaries.
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Falco's main business activities are the design and manufacture of power magnetics, semiconductors and circuitboards. In addition the company designs and manufactures common mode chokes, current sensors, gate drives, power inductors, line transformers, THT inductors, watt hour meters, lighting systems, printed computer boards, mechanical assembly systems, and also provides plastic molding, metal stamping and electronic manufacturing, OEM design and testing services. Falco is a major supplier to international OEMs and brand name electronics manufacturers alike. Falco has regionalized branches in Los Angeles and Miami in the United States; Munich, Germany; Milan, Desenzano, and Bologna, Italy; Manila, The Philippines, Bangalore, India; Xiamen, China and Hong Kong. Falco has manufacturing plants in Mexico, China and India.
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What products and services are offered by Falco electronics?
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Falco designs and manufactures components used by Original Equipment Manufacturers in producing consumer electronics. Components include semiconductors, circuitboards, sensors, transformers, inductors, and meters as well as material-related components such as plastic molding and metal stamping.
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The World Bank is an international financial institution that provides loans and grants to the governments of low- and middle-income countries for the purpose of pursuing capital projects. The World Bank is the collective name for the International Bank for Reconstruction and Development (IBRD) and International Development Association (IDA), two of five international organizations owned by the World Bank Group. It was established along with the International Monetary Fund at the 1944 Bretton Woods Conference. After a slow start, its first loan was to France in 1947. In the 1970s, it focused on loans to developing world countries, shifting away from that mission in the 1980s. For the last 30 years, it has included NGOs and environmental groups in its loan portfolio. Its loan strategy is influenced by the United Nations' Sustainable Development Goals, as well as environmental and social safeguards.
As of 2022, the World Bank is run by a president and 25 executive directors, as well as 29 various vice presidents. IBRD and IDA have 189 and 174 member countries, respectively. The U.S., Japan, China, Germany and the U.K. have the most voting power. The bank aims loans at developing countries to help reduce poverty. The bank is engaged in several global partnerships and initiatives, and takes a role in working toward addressing climate change. The World Bank operates a number of training wings and it works with the Clean Air Initiative and the UN Development Business. It works within the Open Data Initiative and hosts an Open Knowledge Repository.
The World Bank has been criticized as promoting inflation and harming economic development, causing protests in 1988 and 2000. There has also been criticism of the bank's governance and response to the COVID-19 pandemic.
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From the passage note down the name of the countries which have most voting power. List the results in comma separated format.
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U.S., Japan, China, Germany, U.K.
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The vast amounts of data collected by healthcare providers in conjunction with modern data analytics techniques present a unique opportunity to improve health service provision and the quality and safety of medical care for patient benefit BIBREF0 . Much of the recent research in this area has been on personalised medicine and its aim to deliver better diagnostics aided by the integration of diverse datasets providing complementary information. Another large source of healthcare data is organisational. In the United Kingdom, the National Health Service (NHS) has a long history of documenting extensively the different aspects of healthcare provision. The NHS is currently in the process of increasing the availability of several databases, properly anonymised, with the aim of leveraging advanced analytics to identify areas of improvement in NHS services.
One such database is the National Reporting and Learning System (NRLS), a central repository of patient safety incident reports from the NHS in England and Wales. Set up in 2003, the NRLS now contains more than 13 million detailed records. The incidents are reported using a set of standardised categories and contain a wealth of organisational and spatio-temporal information (structured data), as well as, crucially, a substantial component of free text (unstructured data) where incidents are described in the `voice' of the person reporting. The incidents are wide ranging: from patient accidents to lost forms or referrals; from delays in admission and discharge to serious untoward incidents, such as retained foreign objects after operations. The review and analysis of such data provides critical insight into the complex functioning of different processes and procedures in healthcare towards service improvement for safer carer.
Although statistical analyses are routinely performed on the structured component of the data (dates, locations, assigned categories, etc), the free text remains largely unused in systematic processes. Free text is usually read manually but this is time-consuming, meaning that it is often ignored in practice, unless a detailed review of a case is undertaken because of the severity of harm that resulted. There is a lack of methodologies that can summarise content and provide content-based groupings across the large volume of reports submitted nationally for organisational learning. Methods that could provide automatic categorisation of incidents from the free text would sidestep problems such as difficulties in assigning an incident category by virtue of a priori pre-defined lists in the reporting system or human error, as well as offering a unique insight into the root cause analysis of incidents that could improve the safety and quality of care and efficiency of healthcare services.
Our goal in this work is to showcase an algorithmic methodology that detects content-based groups of records in a given dataset in an unsupervised manner, based only on the free and unstructured textual description of the incidents. To do so, we combine recently developed deep neural-network high-dimensional text-embedding algorithms with network-theoretical methods. In particular, we apply multiscale Markov Stability (MS) community detection to a sparsified geometric similarity graph of documents obtained from text vector similarities. Our method departs from traditional natural language processing tools, which have generally used bag-of-words (BoW) representation of documents and statistical methods based on Latent Dirichlet Allocation (LDA) to cluster documents BIBREF1 . More recent approaches have used deep neural network based language models clustered with k-means, without a full multiscale graph analysis BIBREF2 . There have been some previous applications of network theory to text analysis. For example, Lanchichinetti and co-workers BIBREF3 used a probabilistic graph construction analysed with the InfoMap algorithm BIBREF4 ; however, their community detection was carried out at a single-scale and the representation of text as BoW arrays lacks the power of neural network text embeddings. The application of multiscale community detection allows us to find groups of records with consistent content at different levels of resolution; hence the content categories emerge from the textual data, rather than fitting with pre-designed classifications. The obtained results could thus help mitigate possible human error or effort in finding the right category in complex category classification trees.
We showcase the methodology through the analysis of a dataset of patient incidents reported to the NRLS. First, we use the 13 million records collected by the NRLS since 2004 to train our text embedding (although a much smaller corpus can be used). We then analyse a subset of 3229 records reported from St Mary's Hospital, London (Imperial College Healthcare NHS Trust) over three months in 2014 to extract clusters of incidents at different levels of resolution in terms of content. Our method reveals multiple levels of intrinsic structure in the topics of the dataset, as shown by the extraction of relevant word descriptors from the grouped records and a high level of topic coherence. Originally, the records had been manually coded by the operator upon reporting with up to 170 features per case, including a two-level manual classification of the incidents. Therefore, we also carried out an a posteriori comparison against the hand-coded categories assigned by the reporter (healthcare personnel) at the time of the report submission. Our results show good overall correspondence with the hand-coded categories across resolutions and, specifically, at the medium level of granularity. Several of our clusters of content correspond strongly to well-defined categories, yet our results also reveal complementary categories of incidents not defined in the external classification. In addition, the tuning of the granularity afforded by the method can be used to provide a distinct level of resolution in certain areas corresponding to specialise or particular sub-themes.
We showcase the methodology through the analysis of a dataset of patient incidents reported to the NRLS.
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What does the dataset for analysis consist of?
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Patient incidents reported to the NRLS.
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describe experience of child fighting with autism
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Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder that affects a child's social communication and interaction, as well as their behavior and interests. Children with autism may have difficulty understanding social cues and may struggle to express their emotions, which can make it challenging for them to navigate social situations and communicate effectively.
Children with autism may also experience sensory processing difficulties, where they are either over or under sensitive to different sensory stimuli, such as sound, touch, taste, or smell. This can lead to sensory overload and cause them to become overwhelmed or upset.
When a child with autism experiences a challenging situation, such as a conflict with another child, their reaction can vary. They may struggle to communicate their feelings effectively, which can lead to frustration or even aggression. In some cases, they may become overwhelmed by their emotions and engage in self-injurious behavior, such as hitting or biting themselves.
It's important to remember that every child with autism is unique, and their experience of fighting with autism may vary depending on their individual strengths and challenges. However, with early diagnosis, proper support, and effective intervention, children with autism can learn to manage their emotions and develop positive social skills to navigate social situations successfully.
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What animals are famously depicted playing poker in a painting?
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Dogs
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After the demise of her husband Khande Rao Holkar and father-in-law Malhar Rao Holkar, Ahilya Bai herself undertook the affairs of Holkar dynasty. She defended the Malwa state against intruders and personally led armies into battle, with Tukoji Rao Holkar as her military commander.
Ahilya Bai was a great pioneer and builder of Hindu temples who constructed hundreds of temples and Dharmashalas throughout India. She is specially renowned for refurbishing & reconsecrating some of the most sacred sites of Hindu pilgrimage that had been desecrated & demolished in the previous century by the Mughal Emperor Aurangzeb
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List down some information about Ahilya Bai from given passage
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1.Ahilya bai Holkar under took the affairs of Holkar Dynasty after demise of her husband khande Rao Holkar and father in law Malhar Rao Holkar.
2.Ahilya Bai defended the Malwa state against intruders and personally led armies into battle, with Tukoji Rao Holkar as her milatary commander.
3.Ahilya Bai was a great pioneer and builder of Hindu temples and Dharmshalas through out India.
4.Ahilya Bai is specially renowned for refurbishing & reconsecrating some of the most sacred sites of Hindu pilgrimage that had been demolished by the Mughal Emperor Aurangzeb.
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As a golfer, how can I fix my slice?
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A common swing fault in golfers is a slice which is caused when the face of the club isn't aligned properly to the swing path of the club. What will typically happen is at the time of impact, the face is open in relation to the target (ie aimed to the right if you are a right handed golfer) and the swing path is closed in relation to the target (ie aimed to the left if you are a left handed golfer). When there is a large difference between the path of the swing and the face of the club, the impact will generate a high degree of lateral spin on the ball causing the flight of the ball to curve away from the target. Here are some ways to fix this swing fault:
1. Grip the club with a stronger grip. This means the v formed between your thumb and pointer finger on back hand is pointed to your rear shoulder and your opposite hand it also gripping the club more from the back. This will help you close the face of the club at impact.
2. Focus on keeping the face closed in the backswing. One fault many amateurs have is the club face opens toward the sky on the backswing too far meaning that the face will need to turn more on the downswing to get to square at impact. Creating less rotation on the backswing can help with this.
3. Keeping hips down and back in the downswing. One fault that can cause inconsistent contact as well as a slice is when the hips thrust forward during the downswing causing the golfer to lift up to counter being too close to the ball which can often cause an over the top, out-to-in swing path. Try to keep the hips back through the swing and focus on rotating the body to keep forward or lateral movement to a minimum.
A slice is a very common issue among golfers but knowing the causes and swing faults that lead to a slice can help you think about how you can improve your swing to fix it.
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Classify the cities based on whether they are located in Namibia or Yemen.
Sanaa, Ta‘izz, Windhoek, Rundu, Oshakati, Al Ḩudaydah
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Namibia - Windhoek, Rundu, Oshakati
Yemen - Sanaa, Ta‘izz, Al Ḩudaydah
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| 453
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From Table we observe the superiority of SpaLoc in terms of accuracy and efficiency. SpaLoc achieves perfect accuracy on test set. And when handling large input domains (N train = 2000), SpaLoc works well because of the sampling techniques, while most baselines cause out-of-memory errors.
Sparsity. In Fig., we show the number of non-zero elements in SpaLoc's intermediate groundings versus the size of input domains on HasSister, Grandparent and Uncle. We fit a cubic polynomial equation f to the data points to illustrate the learned inference complexity of SpaLoc.
The SpaLoc we use in these three datasets has an arity of 3 so the maximum capacity of the model is Θ(N 3 ). However, the memory complexity of SpaLoc converges to the optimal algorithm complexity of these tasks , which is O(N 2 ). In comparison, the memory complexity of original dense NLMs will be fixed to Θ(N 3 ) when the model architecture is fixed. We evaluate SpaLoc on the real-world knowledge-graph inductive reasoning benchmarks proposed in GraIL, whose training and evaluation sets are disjoint sub-graphs extracted from WN18RR, FB15k-237, and NELL-995. For each knowledge graph, there are four versions of inductive datasets with increasing sizes.
(Ntrain = 2000), SpaLoc works well because of the sampling techniques, while most baselines cause out-of-memory errors.
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In table 1, what does tested on 100 objects mean? I thought the number of objects are 20 and 2000?
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In the original paper, we used two training and testing settings. Column 1 (train on N=20, test on N=100 ), Column 2 (train on N=200, test on N=100). We fixed the test graph size to 100 because our baselines MemNN and NLM can not be applied (in testing) to larger graphs. We have clarified this in the paper.
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Our template-based parameterization maps the accelerator, denoted as x, to a discrete design space,
and each x i is a discrete-valued variable representing one component of the microarchitectural template, as shown in Table (See Appendix C for the description of other accelerator search spaces studied in our work). A design maybe be infeasible due to various reasons, such as a compilation failure or the limitations of physical implementation, and we denote the set of all such feasibility criterion as Feasible(x). The feasibility criterion depends on both the target software and the underlying hardware, and it is not easy to identify if a given x is infeasible without explicit simulation. We will require our optimization procedure to not only learn the value of the objective function but also to learn to navigate through a sea of infeasible solutions to high-performing feasible solutions x * satisfying Feasible(x * ) = 1.
Our training dataset D consists of a modest set of accelerators x i that are randomly sampled from the design space and evaluated by the hardware simulator. We partition the dataset D into two subsets, D feasible and D infeasible . Let f (x) denote the desired objective (e.g., latency, power, etc.) we intend to optimize over the space of accelerators x. We do not possess functional access to f (x), and the optimizer can only access f (x) values for accelerators x in the feasible partition of the data, D feasible . For all infeasible accelerators, the simulator does not provide any value of f (x). In addition to satisfying feasibility, the optimizer must handle explicit constraints on parameters such as area and power. In our applications, we impose an explicit area constraint, Area(x) ≤ α 0 , though additional explicit constraints are also possible. To account for different constraints, we formulate this task as a constrained optimization problem. Formally:
While Equation 1 may appear similar to other standard black-box optimization problems, solving it over the space of accelerator designs is challenging due to the large number of infeasible points, the need to handle explicit design constraints, and the difficulty in navigating the non-smooth landscape (See Figure and Figure in the Appendix) of the objective function.
What makes optimization over accelerators challenging? Compared to other domains where model-based optimization methods have been applied, optimizing accelerators introduces a number of practical challenges. First, accelerator design spaces typically feature a narrow manifold of feasible accelerators within a sea of infeasible points, as visualized in Figure and Appendix (Figure). While some of these infeasible points can be identified via simple rules (e.g. estimating chip area usage), most infeasible points correspond to failures during compilation or hardware simulation. These infeasible points are generally not straightforward to formulate into the optimization problem and requires simulation.
Second, the optimization objective can exhibit high sensitivity to small variations in some architecture parameters (Figure) in some regions of the design space, but remain relatively insensitive in other parts, resulting in a complex optimization landscape. This suggests that optimization algorithms based on local parameter updates (e.g., gradient ascent, evolutionary schemes, etc.) may have a challenging task traversing the nearly flat landscape of the objective, which can lead to poor performance. Training dataset. We used an offline dataset D of (accelerator parameters, latency) via random sampling from the space of 452M possible accelerator configurations. Our method is only provided with a relatively modest set of feasible points (≤ 8000 points) for training, and these points are the worst-performing feasible points across the pool of randomly sampled data. This dataset is meant to reflect an easily obtainable and an application-agnostic dataset of accelerators that could have been generated once and stored to disk, or might come from real physical experiments. We emphasize that no assumptions or domain knowledge about the application use case was made during dataset collection. Table depicts the list of target applications, evaluated in this work, includes three variations of MobileNet, three in-house industry-level models for object detection (M4, M5, M6; names redacted to prevent anonymity violation), a U-net model, and two RNN-based encoderdecoder language models. These applications span the gamut from small models, such as M6, with only 0.4 MB model parameters that demands less on-chip memory, to the medium-sized models (≥ 5 MB), such as MobileNetV3 and M4 models, and large models (≥ 19 MB), such as t-RNNs, hence requiring larger on-chip memory.
Evaluation protocol. To compare state-of-the-art simulator-driven methods and our data-driven method, we limit the number of feasible points (costly to evaluate) that can be used by any algorithm to equal amounts. We still provide infeasible points to any method and leave it up to the optimization method to use it or not. This ensures our comparisons are fair in terms of the amount of data available to each method. However, it is worthwhile to note that in contrast to our method where worse-quality data points from small offline dataset are used, the simulator-driven methods have an inherent advantage because they can steer the query process towards the points that are more likely to be better in terms of performance. Following prior work, we evaluate each run of a method by first sampling the top n = 256 design candidates according to the algorithm's predictions, evaluating all of these under the ground truth objective function and recording the performance of the best accelerator design. The final reported results is the median of ground truth objective values across five independent runs.
Evaluation protocol. To compare state-of-the-art simulator-driven methods and our data-driven method, we limit the number of feasible points (costly to evaluate) that can be used by any algorithm to equal amounts. We still provide infeasible points to any method and leave it up to the optimization method to use it or not. This ensures our comparisons are fair in terms of the amount of data available to each method. However, it is worthwhile to note that in contrast to our method where worse-quality data points from small offline dataset are used, the simulator-driven methods have an inherent advantage because they can steer the query process towards the points that are more likely to be better in terms of performance. Following prior work (Brookes et al., 2019; Trabucco et al., 2021a;b), we evaluate each run of a method by first sampling the top n = 256 design candidates according to the algorithm’s predictions, evaluating all of these under the ground truth objective function and recording the performance of the best accelerator design. The final reported results is the median of ground truth objective values across five independent runs.
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Whether the results of PRIME are produced by the performance model or by the simulation/measurement using the optimization results or not?
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Per our interpretation of the question, we would like to clarify that the results we report in Tables 3, 4, 5 and 6 in the main paper correspond to the actual latency of the optimized designs found by PRIME in a purely offline fashion, which are then evaluated under the simulator to produce actual latencies. More details of our evaluation protocol can be found in the paragraph titled "Evaluation Protocol" in Section 3. We are happy to clarify any details if something is unclear.
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Best film in Motion Picture 27th Screen Actors Guild(SAG) Awards was given to which film?
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Chicago 7
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1808.09409
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In this paper, we manually annotate the predicate–argument structures for the 600 L2-L1 pairs as the basis for the semantic analysis of learner Chinese. It is from the above corpus that we carefully select 600 pairs of L2-L1 parallel sentences. We would choose the most appropriate one among multiple versions of corrections and recorrect the L1s if necessary. Because word structure is very fundamental for various NLP tasks, our annotation also contains gold word segmentation for both L2 and L1 sentences. Note that there are no natural word boundaries in Chinese text. We first employ a state-of-the-art word segmentation system to produce initial segmentation results and then manually fix segmentation errors.
In this paper, we manually annotate the predicate–argument structures for the 600 L2-L1 pairs as the basis for the semantic analysis of learner Chinese.
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Who manually annotated the semantic roles for the set of learner texts?
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Authors
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When was the Australian city Canberra founded?
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The Australian National Capital of Canberra was announced on the 12th of March 1913 by the Governor-General's wife, Lady Denman. The construction of the city and development was delayed by the two world wars and the great depression, however has steadily grown and now holds more than 400,000 people.
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Anthology 3 is a compilation album by the Beatles, released on 29 October 1996 by Apple Records as part of The Beatles Anthology series. The album includes rarities and alternative tracks from the final three years of the band's career, ranging from the initial sessions for The Beatles (better known as "the White Album") to the last sessions for Let It Be and Abbey Road in 1969 and early 1970. It is the last in a trilogy of albums with Anthology 1 and Anthology 2, all of which tie in with the televised special The Beatles Anthology.
The album was certified 3× Platinum by the RIAA and was the group's third double album in a row to reach number one on the US charts, equalling a record set by Donna Summer in the 1970s.
The Anthology albums were remastered and made available digitally on the iTunes Store on 14 June 2011, individually and as part of the Anthology Box Set.
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Is Anthology 3 the White Album
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Anthology 3 is a compilation album by the Beatles, released on 29 October 1996 by Apple Records as part of The Beatles Anthology series. The album includes rarities and alternative tracks from the final three years of the band's career, ranging from the initial sessions for The Beatles (better known as "the White Album") to the last sessions for Let It Be and Abbey Road in 1969 and early 1970. It is the last in a trilogy of albums with Anthology 1 and Anthology 2, all of which tie in with the televised special The Beatles Anthology.
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With the massive increase in social interactions on online social networks, there has also been an increase of hateful activities that exploit such infrastructure. On Twitter, hateful tweets are those that contain abusive speech targeting individuals (cyber-bullying, a politician, a celebrity, a product) or particular groups (a country, LGBT, a religion, gender, an organization, etc.). Detecting such hateful speech is important for analyzing public sentiment of a group of users towards another group, and for discouraging associated wrongful activities. It is also useful to filter tweets before content recommendation, or learning AI chatterbots from tweets.
The manual way of filtering out hateful tweets is not scalable, motivating researchers to identify automated ways. In this work, we focus on the problem of classifying a tweet as racist, sexist or neither. The task is quite challenging due to the inherent complexity of the natural language constructs – different forms of hatred, different kinds of targets, different ways of representing the same meaning. Most of the earlier work revolves either around manual feature extraction BIBREF0 or use representation learning methods followed by a linear classifier BIBREF1 , BIBREF2 . However, recently deep learning methods have shown accuracy improvements across a large number of complex problems in speech, vision and text applications. To the best of our knowledge, we are the first to experiment with deep learning architectures for the hate speech detection task.
In this paper, we experiment with multiple classifiers such as Logistic Regression, Random Forest, SVMs, Gradient Boosted Decision Trees (GBDTs) and Deep Neural Networks(DNNs). The feature spaces for these classifiers are in turn defined by task-specific embeddings learned using three deep learning architectures: FastText, Convolutional Neural Networks (CNNs), Long Short-Term Memory Networks (LSTMs). As baselines, we compare with feature spaces comprising of char n-grams BIBREF0 , TF-IDF vectors, and Bag of Words vectors (BoWV).
Main contributions of our paper are as follows: (1) We investigate the application of deep learning methods for the task of hate speech detection. (2) We explore various tweet semantic embeddings like char n-grams, word Term Frequency-Inverse Document Frequency (TF-IDF) values, Bag of Words Vectors (BoWV) over Global Vectors for Word Representation (GloVe), and task-specific embeddings learned using FastText, CNNs and LSTMs. (3) Our methods beat state-of-the-art methods by a large margin ( INLINEFORM0 18 F1 points better).
To the best of our knowledge, we are the first to experiment with deep learning architectures for the hate speech detection task.
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Are the authors the first to experiment with deep learning architectures for the hate speech detection task?
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Yes.
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What activities does a 4 year old like?
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With the caveat that each kid is different, at 4 years old kids like fun & stimulation. They like physical activities like playing in the park on the swings, slides, jungle gyms to splashing in the water to simply running around. They like reading books to learn words and sentences, recognizing pictures of objects and animals, and reading visual stories. They like arts and crafts, playing with colors and shapes. They enjoy getting things right, so give them encouragement and positive reinforcement. They like singing and dancing, they have favorite songs and music. Some kids enjoy dressing up and making stories with their toys. They like building forts with boxes and blankets. So there's a whole range of activities to pick from. If you like spending time with kids this age it can be a lot of fun for you too !
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Classify each of the following as either a root vegetable or a fruit: cassava, ube, strawberry, carrot, potato, banana, blueberry, yam, lime
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Root vegetable: cassava, ube, carrot, potato, yam
Fruit: strawberry, banana, blueberry, lime
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| 25
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We empirically validate EGL on speech recognition tasks. In our experiments, the RNN takes in spectrograms of utterances, passing them through two 2D-convolutional layers, followed by seven bi-directional recurrent layers and a fully-connected layer with softmax activation. All recurrent layers are batch normalized. At each timestep, the softmax activations give a probability distribution over the characters. CTC loss BIBREF8 is then computed from the timestep-wise probabilities.
A base model, INLINEFORM0 , is trained on 190 hours ( INLINEFORM1 100K instances) of transcribed speech data. Then, it selects a subset of a 1,700-hour ( INLINEFORM2 1.1M instances) unlabeled dataset. We query labels for the selected subset and incorporate them into training. Learning rates are tuned on a small validation set of 2048 instances. The trained model is then tested on a 156-hour ( INLINEFORM3 100K instances) test set and we report CTC loss, Character Error Rate (CER) and Word Error Rate (WER).
The confidence score methods BIBREF1 , BIBREF2 can be easily extended to our setup. Specifically, from the probabilities over the characters, we can compute an entropy per timestep and then average them. This method is denoted as entropy. We could also take the most likely prediction and calculate its CTC loss, normalized by number of timesteps. This method is denoted as pCTC (predicted CTC) in the following sections.
We implement EGL by marginalizing over the most likely 100 labels, and compare it with: 1) a random selection baseline, 2) entropy, and 3) pCTC. Using the same base model, each method queries a variable percentage of the unlabeled dataset. The queries are then included into training set, and the model continues training until convergence. Fig. FIGREF9 reports the metrics (Exact values are reported in Table TABREF12 in the Appendix) on the test set as the query percentage varies. All the active learning methods outperform the random baseline. Moreover, EGL shows a steeper, more rapid reduction in error than all other approaches. Specifically, when querying 20% of the unlabeled dataset, EGL has 11.58% lower CER and 11.09% lower WER relative to random. The performance of EGL at querying 20% is on par with random at 40%, suggesting that using EGL can lead to an approximate 50% decrease in data labeling.
These particular samples consist of short utterances containing silence (with background noise) or filler words.
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What do the particular samples consist of?
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These particular samples consist of short utterances containing silence (with background noise) or filler words.
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| 98
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Lexical analysis, syntactic analysis, semantic analysis, disclosure analysis and pragmatic analysis are five main steps in natural language processing BIBREF0 , BIBREF1 . While morphology is a basic task in lexical analysis of English, word segmentation is considered a basic task in lexical analysis of Vietnamese and other East Asian languages processing. This task is to determine borders between words in a sentence. In other words, it is segmenting a list of tokens into a list of words such that words are meaningful.
Word segmentation is the primary step in prior to other natural language processing tasks i. e., term extraction and linguistic analysis (as shown in Figure 1). It identifies the basic meaningful units in input texts which will be processed in the next steps of several applications. For named entity recognization BIBREF2 , word segmentation chunks sentences in input documents into sequences of words before they are further classified in to named entity classes. For Vietnamese language, words and candidate terms can be extracted from Vietnamese copora (such as books, novels, news, and so on) by using a word segmentation tool. Conformed features and context of these words and terms are used to identify named entity tags, topic of documents, or function words. For linguistic analysis, several linguistic features from dictionaries can be used either to annotating POS tags or to identifying the answer sentences. Moreover, language models can be trained by using machine learning approaches and be used in tagging systems, like the named entity recognization system of Tran et al. BIBREF2 .
Many studies forcus on word segmentation for Asian languages, such as: Chinese, Japanese, Burmese (Myanmar) and Thai BIBREF3 , BIBREF4 , BIBREF5 , BIBREF6 . Approaches for word segmentation task are variety, from lexicon-based to machine learning-based methods. Recently, machine learning-based methods are used widely to solve this issue, such as: Support Vector Machine or Conditional Random Fields BIBREF7 , BIBREF8 . In general, Chinese is a language which has the most studies on the word segmentation issue. However, there is a lack of survey of word segmentation studies on Asian languages and Vietnamese as well. This paper aims reviewing state-of-the-art word segmentation approaches and systems applying for Vietnamese. This study will be a foundation for studies on Vietnamese word segmentation and other following Vietnamese tasks as well, such as part-of-speech tagger, chunker, or parser systems.
There are several studies about the Vietnamese word segmentation task over the last decade. Dinh et al. started this task with Weighted Finite State Transducer (WFST) approach and Neural Network approach BIBREF9 . In addition, machine learning approaches are studied and widely applied to natural language processing and word segmentation as well. In fact, several studies used support vector machines (SVM) and conditional random fields (CRF) for the word segmentation task BIBREF7 , BIBREF8 . Based on annotated corpora and token-based features, studies used machine learning approaches to build word segmentation systems with accuracy about 94%-97%.
According to our observation, we found that is lacks of complete review approaches, datasets and toolkits which we recently used in Vietnamese word segmentation. A all sided review of word segmentation will help next studies on Vietnamese natural language processing tasks have an up-to-date guideline and choose the most suitable solution for the task. The remaining part of the paper is organized as follows. Section II discusses building corpus in Vietnamese, containing linguistic issues and the building progress. Section III briefly mentions methods to model sentences and text in machine learning systems. Next, learning models and approaches for labeling and segmenting sequence data will be presented in Section IV. Section V mainly addresses two existing toolkits, vnTokenizer and JVnSegmenter, for Vietnamese word segmentation. Several experiments based on mentioned approaches and toolkits are described in Section VI. Finally, conclusions and future works are given in Section VII.
According to our observation, we found that is lacks of complete review approaches, datasets and toolkits which we recently used in Vietnamese word segmentation. A all sided review of word segmentation will help next studies on Vietnamese natural language processing tasks have an up-to-date guideline and choose the most suitable solution for the task.
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Is this paper focusing on Vietnamese only？
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Yes.
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How can I tell my sourdough starter is ready to use?
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You can look for a few signs to see if your sourdough starter is ready to use. One common sign to look for is whether the starter has at least doubled in volume. Time can be a difficult indicator as how long it takes for a starter to double volume can vary based on temperature, hydration, and other factors. Another sign to look for is a lot of bubbles throughout the starter - this can be seen if your starter is in a glass container. Lastly, get to know your starter as you bake with it because each starter has its own unique characteristics. Learn what it will smell like when it matures - this can help you establish a baseline and identify when something is wrong with the starter as it smells "funky".
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Which of the following exercises are push exercises: bench press, bicep curl, skull crusher, tricep extension, pull up, dumbbell row.
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Bench press, skull crusher, tricep extension.
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null | false
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What is a sitcom?
|
A sitcom is a comedy that typically airs on a television network once per week for 30 minutes.
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1906.04571
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We presented a new approach for converting between masculine-inflected and feminine-inflected noun phrases in morphologically rich languages. To do this, we introduced a Markov random field with an optional neural parameterization that infers the manner in which a sentence must change to preserve morpho-syntactic agreement when altering the grammatical gender of particular nouns. To the best of our knowledge, this task has not been studied previously. As a result, there is no existing annotated corpus of paired sentences that can be used as “ground truth.” Despite this limitation, we evaluated our approach both intrinsically and extrinsically, achieving promising results. For example, we demonstrated that our approach reduces gender stereotyping in neural language models. Finally, we also identified avenues for future work, such as the inclusion of co-reference information.
We presented a new approach for converting between masculine-inflected and feminine-inflected noun phrases in morphologically rich languages. To do this, we introduced a Markov random field with an optional neural parameterization that infers the manner in which a sentence must change to preserve morpho-syntactic agreement when altering the grammatical gender of particular nouns.
|
Which model do they use to convert between masculine-inflected and feminine-inflected sentences?
|
The answers are shown as follows:
* Markov random field with an optional neural parameterization
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What are ten woodworking projects that I could complete in a weekend?
|
1. A wine box
2. A coat rack
3. A cutting board
4. A simple desk
5. A bottle stopper
6. A bowl
7. A small shelf
8. A picture frame
9. A serving tray
10. A garden bench
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What is PySpark?
|
PySpark is an interface for Apache Spark in Python. It not only allows you to write Spark applications using Python APIs, but also provides the PySpark shell for interactively analyzing your data in a distributed environment. PySpark supports most of Spark’s features such as Spark SQL, DataFrame, Streaming, MLlib (Machine Learning) and Spark Core.
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how to use as wax
|
Use it to stick objects
Use it to cover wounds
use it to clean stains
use it to smooth itchy shoes
use it as holder to stick things
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Outlander is a historical drama television series based on the Outlander novel series by Diana Gabaldon. Developed by Ronald D. Moore, the show premiered on August 9, 2014, on Starz. It stars Caitríona Balfe as Claire Randall, a former Second World War military nurse in Scotland who, in 1945, finds herself transported back in time to 1743. There she encounters, falls in love with and marries a dashing Highland warrior named Jamie Fraser (Sam Heughan), a tacksman of Gabaldon's fictionalized version of Clan Fraser of Lovat. Here, Claire becomes embroiled in the Jacobite rising.
|
Given this paragraph about Outlander, who stars in Outlander?
|
Outlander stars Caitriona Balfe (as Claire Randall). Her co-start is Sam Heughan (as Jamie Fraser). It is a Starz TV Series portraying star crossed lovers who meet across time. Claire travels from 1945, back in time to 1743 and encounters: adventure, mystery, and political intrigue of the Jacobite uprising; all the time growing closer and closer to her true love Jamie Fraser. The 2014 (to present) show is based on the Outlander book series written by Diana Gabaldon, originally published in the 1990's.
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What is the best way to lose weight?
|
Cut carbs, calorie deficit.
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What is Bart Simpson's graffiti tag?
|
El Barto
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What is it like living in Chicago, Illinois?
|
Chicago is a large city located in the midwest. It is known for tourist attractions like Navy Pier and The Bean, as well as famous architecture. It's on Lake Michigan which makes the winters extremely cold but the summers pleasant. People call Chicago the "Windy City".
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What is a skiff?
|
Skiff is a type of boat. A skiff is commonly used for flats fishing. Fishing in shallow water requires a boat that has a very shallow draft.
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What are some Machine Learning Algorithms
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Linear Regression
K Means
Decision Trees
Neural Networks
Support Vector Machines
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Flying trapeze refers to a trapeze act where a performer, or "flyer," grabs the trapeze bar and jumps off a high platform, or pedestal board, so that gravity creates the swing. The swing's parts are the "force out" (sometimes called the "cast out") at the far end of the first swing, the beat back and the rise (also known as "seven") as the performer swings back above the pedestal board, and then the trick is thrown at the far end of the second swing. The performer often releases the bar and is caught by another performer, the "catcher," who hangs by their knees on another trapeze, or sometimes on a cradle, which can be either stationary or also swinging. People of any size are able to execute basic trapeze maneuvers. Flying trapeze is generally done over a net, or occasionally over water. However, some smaller rigs, usually created for teaching purposes, use mats instead of a net.
|
What is flying trapeze?
|
Flying trapeze is a performance act where a "flyer" uses a trapeze bar to swing from a high platform, using gravity to create a swing. The performer then releases the bar and is caught by another performer who is hanging by their knees (the "catcher") on another trapeze bar.
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How many months have less than 31 days?
|
5 months have less than 31 days. April, June, September and November have 30 days. February has 28 or 29 days, depending on the year.
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I have a vegan friend coming over for dinner, which of the items in my kitchen can I use to make vegan food: chicken, kale, lettuce, honey, eggplant, tomato, rice, cheese, yoghurt, ice-cream and chocolate.
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To prepare a vegan meal you will be able to use any items that do not contain animal products. From the list provided, you can use: lettuce, eggplant, tomato and rice.
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Where is the best place in my home to store valuable and sensitive documents?
|
You should store sensitive documents in a secure location, such as a locked drawer inside a room with a lock. Depending on the nature of the documents, you might also consider storing them in a locking safe. Some safes are also waterproof and fireproof to a certain degree, so in the event of a natural disaster to your home, you would still be able to retrieve them. Examples of sensitive documents to be stored include birth certificates, wills, and deeds.
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null | false
| 476
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Neural networks are a powerful class of non-linear function approximators that have been successfully used to tackle a wide range of problems. They have enabled breakthroughs in many tasks, such as image classification, speech recognition, and anomaly detection. Formally, the output of a neural network consisting of P layers can be defined as follows:
where φ i (.) is the element-wise activation function, e.g., ReLU or Sigmoid, of the i th layer and W = {W 1 , . . . , W P } are the corresponding weights of the network. The parameters of f (x; W) are optimized by minimizing the empirical loss:
where l(•) is the loss function, and {x i , y i } N i=1 are the training samples and their associated groundtruth labels. The loss is minimized using the gradient decent-based optimization coupled with backpropagation.
However, neural networks are often over-parameterized, i.e., have more parameters than data. As a result, they tend to overfit to the training samples and not generalize well on unseen examples. While research on double descent shows that over-parameterization does not necessarily lead to overfitting, avoiding overfitting has been extensively studied and various approaches and strategies have been proposed, such as data augmentation, regularization, and Dropout, to close the gap between the empirical loss and the expected loss. Diversity of learners is widely known to be important in ensemble learning and, particularly in deep learning context, diversity of information extracted by the network neurons has been recognized as a viable way to improve generalization. In most cases, these efforts have focused on making the set of weights more diverse. However, diversity of the activations has not received much attention. Here, we argue that due to the presence of non-linear activations, diverse weights do not guarantee diverse feature representation. Thus, we propose applying the diversity on top of feature mapping instead of the weights.
To the best of our knowledge, only have considered diversity of the activations directly in the neural network context. They proposed an additional loss term using cross-covariance of hidden activations, which encourages the neurons to learn diverse or non-redundant representations. Their proposed approach, known as DeCov, was empirically proven to alleviate overfitting and to improve the generalization ability of neural network, yet a theoretical analysis to prove this has so far been lacking. Moreover, modeling diversity as the sum of the pairwise cross-covariance, it can capture only the pairwise diversity between components and is unable to capture the "higher-order diversity".
In this work, we start by theoretically showing that the within-layer activation diversity boosts the generalization ability of neural networks and reduces overfitting. Moreover, we propose a novel approach to encourage activation diversity within a layer. We propose complementing the 'betweenlayer' feedback with additional 'within-layer' feedback to penalize similarities between neurons on the same layer. Thus, we encourage each neuron to learn a distinctive representation and to enrich the data representation learned within each layer. We propose three variants for our approach that are based on different global diversity definitions.
Our contributions in this paper are as follows:
• Theoretically, we derive novel generalization bounds for neural networks depending on the within-layer activation diversity. As shown in Section 2, we express the upper-bound of the estimation error as a function of the diversity factor. Thus, we provide theoretical evidence that the within-layer activation diversity helps reduce the generalization error. • Methodologically, we propose a new approach to encourage the 'diversification' of the layers' output feature maps in neural networks. The proposed approach has three variants.
The main intuition is that by promoting the within-layer activation diversity, neurons within a layer learn distinct patterns and, thus, increase the overall capacity of the model. • Empirically, we show that the proposed within-layer activation diversification boosts the performance of neural networks. Experimental results on several tasks show that the proposed approach outperforms competing methods.
Diversity of learners is widely known to be important in ensemble learning (Li et al., 2012; Yu et al., 2011) and, particularly in deep learning context, diversity of information extracted by the network neurons has been recognized as a viable way to improve generalization (Xie et al., 2017a; 2015b). In most cases, these efforts have focused on making the set of weights more diverse (Yang et al.; Malkin & Bilmes, 2009). However, diversity of the activations has not received much attention. Here, we argue that due to the presence of non-linear activations, diverse weights do not guarantee diverse feature representation. Thus, we propose applying the diversity on top of feature mapping instead of the weights. To the best of our knowledge, only Cogswell et al. (2016) have considered diversity of the activations directly in the neural network context. They proposed an additional loss term using cross-covariance of hidden activations, which encourages the neurons to learn diverse or non-redundant representations. Their proposed approach, known as DeCov, was empirically proven to alleviate overfitting and to improve the generalization ability of neural network, yet a theoretical analysis to prove this has so far been lacking. Moreover, modeling diversity as the sum of the pairwise cross-covariance, it can capture only the pairwise diversity between components and is unable to capture the ”higher-order diversity”. In this work, we start by theoretically showing that the within-layer activation diversity boosts the generalization ability of neural networks and reduces overfitting. Moreover, we propose a novel approach to encourage activation diversity within a layer. We propose complementing the ’betweenlayer’ feedback with additional ’within-layer’ feedback to penalize similarities between neurons on the same layer. Thus, we encourage each neuron to learn a distinctive representation and to enrich the data representation learned within each layer. We propose three variants for our approach that are based on different global diversity definitions.
|
How does activation diversity connect to the rest of the literature？
|
The connections to related literature has been described in the introduction and the related work section. We added the references mentioned by the reviewer. As the proposed regularizer is an activation-based diversity approach, we focused on our experimental comparisons with prior activation-based diversity approaches, i.e., 'Decov'. We also now included in the updated manuscript dropout experiments and weight-based diversity.
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Identify which animal is domesticated or wild: Horse, Red deer
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Red deer is wild, Horse is domesticated
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The preconditioning for the financial crisis were complex and multi-causal. Almost two decades prior, the U.S. Congress had passed legislation encouraging financing for affordable housing. However, in 1999, parts of the Glass-Steagall legislation, which had been adopted in 1933, were repealed, permitting financial institutions to commingle their commercial (risk-averse) and proprietary trading (risk-taking) operations. Arguably the largest contributor to the conditions necessary for financial collapse was the rapid development in predatory financial products which targeted low-income, low-information homebuyers who largely belonged to racial minorities. This market development went unattended by regulators and thus caught the U.S. government by surprise.
|
What caused the 2007-2008 financial crisis?
|
Arguably the largest contributor to the conditions necessary for the financial collapse was the rapid development of predatory financial products which targeted low-income, low-information homebuyers who largely belonged to racial minorities.
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Write an email to my friend who lives in Minneapolis to help convince them to move to Austin Texas.
|
Austin Texas is a booming city in central Texas for many reasons that might appeal to you. There are many opportunities for career growth because of all the technology businesses that have chosen to move here. In the job market right now, it is helpful to be near these businesses in case you get laid off from your job. The weather is mild all throughout the winter, so you won't need to scrape snow and ice off of your car every day, although there have been a number of snow storms in the last couple years but they quickly melt and only last a day or two at a time. The summers get hot and can stay above 100 degrees Fahrenheit for weeks at a time. This may sound like a negative, but one of the best things about Austin is the access to water. Lady Bird Lake, sometimes called Town Lake, is actually a river that runs through the middle of the city. Paddle boarding, swimming in Barton Springs, and party boats on Lake Travis are all water related activities that are particularly fun for a young demographic. There is a vibrant music scene with many local bands and plenty of art related communities. The conscious community in Austin is growing as well through many different types of activities like ecstatic dance, authentic relating, and tantra. There is something for everyone in Austin. The city is a tourist destination with many bachelor and bachelorette parties choosing to visit. Austin also hosts SXSW and ACL, both of which draw many hundreds of thousands of visitors each year. The people are friendly, similar to Minneapolis. The tech scene feels a bit like the New York City hustle, while the West coast chill is present through the laid back, fun oriented lifestyle. You can find whatever you're looking for here, and it is worth spending time here to see how you can make the city your own.
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Give me a list of the best Romantic Comedy Movies
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My Big Fat Greek Wedding, About Time, P.S. I Love You, Somethings Gotta Give, How To Lose A Guy in Ten Days, Never Been Kissed, Annie Hall, The Big Sick, My Best Friend's Wedding, Sleepless in Seattle, Notting Hill, 10 Things I Hate About You, Bridget Jones's Diary, Clueless, You've Got Mail, When Harry Met Sally
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Seville has a Mediterranean climate (Köppen climate classification Csa), featuring very hot, dry summers and mild winters with moderate rainfall. Seville has an annual average of 19.2 °C (67 °F). The annual average temperature is 25.4 °C (78 °F) during the day and 13.0 °C (55 °F) at night. Seville is located in the lower part of the Guadalquivir Valley, which is often referred to as "the frying pan of Spain", as it features the hottest cities in the country.
Seville is the warmest city in Continental Europe. It is also the hottest major metropolitan area in Europe, with summer average high temperatures of above 35 °C (95 °F) and also the hottest in Spain. After the city of Córdoba (also in Andalusia), Seville has the hottest summer in continental Europe among all cities with a population over 100,000 people, with average daily highs of 36.0 °C (97 °F) in July.
|
Given this paragraph about the climate of Seville, Spain, what's the average temperature during the day, at night, and overall?
|
Seville's average temperature is 19.2 °C (67 °F).
The average temperature during the day is 25.4 °C (78 °F) and the average temperature at night is 13.0 °C (55 °F).
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What are some good yoga poses for beginners?
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The practice of yoga can be a beneficial addition to a healthy lifestyle. Yoga is considered to increase flexibility and muscle strength. A few positions for beginners include :
1. Downward facing dog
2. Mountain pose
3. Warrior I
4. Warrior II
5. Triangle pose
These gentler positions provide a safe introduction to the practice of yoga for beginners
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List the major internet search engines
|
Bing
Google
Yahoo
DuckDuckGo
Ask.com
AOL
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Classify the celebrities based on whether they were born in 1990 or 1991.
Emma Watson, Jennifer Lawrence, Ed Sheeran, Charlie Puth
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Born in 1990 - Emma Watson, Jennifer Lawrence
Born in 1991 - Ed Sheeran, Charlie Puth
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null | false
| 160
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Learning the distributed representation for long spans of text from its constituents has been a key step for various natural language processing (NLP) tasks, such as text classification BIBREF0 , BIBREF1 , semantic matching BIBREF2 , BIBREF3 , and machine translation BIBREF4 . Existing deep learning approaches take a compositional function with different forms to compose word vectors recursively until obtaining a sentential representation. Typically, these compositional functions involve recurrent neural networks BIBREF5 , BIBREF6 , convolutional neural networks BIBREF7 , BIBREF8 , and tree-structured neural networks BIBREF9 , BIBREF10 .
Among these methods, tree-structured neural networks (Tree-NNs) show theirs superior performance in many NLP tasks BIBREF11 , BIBREF12 . Following the syntactic tree structure, Tree-NNs assign a fixed-length vector to each word at the leaves of the tree, and combine word and phrase pairs recursively to create intermediate node vectors, eventually obtaining one final vector to represent the whole sentence.
However, these models have a major limitation in their inability to fully capture the richness of compositionality BIBREF13 . The same parameters are used for all kinds of semantic compositions, even though the compositions have different characteristics in nature. For example, the composition of the adjective and the noun differs significantly from the composition of the verb and the noun. Moreover, many semantic phenomena, such as semantic idiomaticity or transparency, call for more powerful compositional mechanisms BIBREF14 . Therefore, Tree-NNs suffer from the underfitting problem.
To alleviate this problem, some researchers propose to use multiple compositional functions, which are arranged beforehand according to some partition criterion BIBREF11 , BIBREF13 , BIBREF15 . Intuitively, using different parameters for different types of compositions has the potential to greatly reduce underfitting. BIBREF13 [ BIBREF13 ] defined different compositional functions in terms of syntactic categories, and a suitable compositional function is selected based on the syntactic categories. BIBREF15 [ BIBREF15 ] introduced multiple compositional functions and during compositional phase, a proper one is selected based on the input information. Although these models accomplished their mission to a certain extent, they still suffer from the following three challenges. First, the predefined compositional functions cannot cover all the compositional rules; Second, they require more learnable parameters, suffering from the problem of overfitting; Third, it is difficult to determine a universal criterion for semantic composition based solely on syntactic categories.
In this paper, we propose dynamic compositional neural networks over tree structure, in which a meta network is used to generate the context-specific parameters of a dynamic compositional network. Specifically, we construct our models based on two kinds of tree-structured neural networks: recursive neural network (Tree-RecNN) BIBREF11 and tree-structure long short-term memory neural network (Tree-LSTM) BIBREF9 . Our work is inspired by recent work on dynamic parameter prediction BIBREF16 , BIBREF17 , BIBREF18 . The meta network is used to extract the shared meta-knowledge across different compositional rules and to dynamically generate the context-specific compositional function. Thus, the compositional function of our models varies with positions, contexts and samples. The dynamic compositional network then applies those context-specific parameters to the current input information. Both meta and dynamic networks are differentiable such that the overall networks can be trained in an end-to-end fashion. Additional, to reduce the complexity of the whole networks, we define the dynamic weight matrix in a manner simulating low-rank matrix decomposition.
We evaluate our models on two typical tasks: text classification and text semantic matching. The results show that our models are more expressive due to their learning to learn nature, yet without increasing the number of model's parameters. Moreover, we find certain composition operations can be learned implicitly by meta TreeNN, such as the composition of noun phrases and verb phrases.
The contributions of the paper can be summed up as follows.
Although these models accomplished their mission to a certain extent, they still suffer from the following three challenges. First, the predefined compositional functions cannot cover all the compositional rules; Second, they require more learnable parameters, suffering from the problem of overfitting; Third, it is difficult to determine a universal criterion for semantic composition based solely on syntactic categories.
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Some researchers use multiple composite functions to address the shortcomings of most existing models. What are the challenges with this approach?
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First, the predefined compositional functions cannot cover all the compositional rules; Second, they require more learnable parameters, suffering from the problem of overfitting; Third, it is difficult to determine a universal criterion for semantic composition based solely on syntactic categories.
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How to be a good software engineer?
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A diversity of industry experience is required to be a good software engineer. Practice industry-standard principles of writing high-quality production-grade code, understand and debug a complicated production system, and effectively communicate with other engineers to scale the team.
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null | false
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What are the best National Parks in the United States
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Yosemite, Glacier, Zion, Yellowstone, Great Smoky Mountains, Rocky Mountain, Grand Teton, Acadia, Olympic, Bryce Canyon, Grand Canyon, Denali, Kenai Fjords, Arches...
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null | false
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Is Python better than R?
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It depends on what the language is being used to accomplish. For example, people tend to start counting at 1, so R begins counts at 1. Computer scientists, however, prefer zero-indexing, so Python begins counts at 0. R syntax is catered more to statistics, data parsing, and machine learning. Python is more typically used for production applications, including machine learning.
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null | false
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Why Bangalore is so popular
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It's the best IT sector in India
It has some awesome colleges and pubs and brews
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Identify which animal is domesticated or wild: Goldfish, Sika deer
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Sika deer is wild, Goldfish is domesticated.
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null | false
| 97
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Low dimensional word representations (embeddings) have become a key component in modern NLP systems for language modeling, parsing, sentiment classification, and many others. These embeddings are usually derived by employing the distributional hypothesis: that similar words appear in similar contexts BIBREF0 .
The models that perform the word embedding can be divided into two classes: predictive, which learn a target or context word distribution, and counting, which use a raw, weighted, or factored word-context co-occurrence matrix BIBREF1 . The most well-known predictive model, which has become eponymous with word embedding, is word2vec BIBREF2 . Popular counting models include PPMI-SVD BIBREF3 , GloVe BIBREF4 , and LexVec BIBREF5 .
These models all learn word-level representations, which presents two main problems: 1) Learned information is not explicitly shared among the representations as each word has an independent vector. 2) There is no clear way to represent out-of-vocabulary (OOV) words.
fastText BIBREF6 addresses these issues in the Skip-gram word2vec model by representing a word by the sum of a unique vector and a set of shared character n-grams (from hereon simply referred to as n-grams) vectors. This addresses both issues above as learned information is shared through the n-gram vectors and from these OOV word representations can be constructed.
In this paper we propose incorporating subword information into counting models using a strategy similar to fastText.
We use LexVec as the counting model as it generally outperforms PPMI-SVD and GloVe on intrinsic and extrinsic evaluations BIBREF7 , BIBREF8 , BIBREF9 , BIBREF10 , but the method proposed here should transfer to GloVe unchanged.
The LexVec objective is modified such that a word's vector is the sum of all its subword vectors.
We compare 1) the use of n-gram subwords, like fastText, and 2) unsupervised morphemes identified using Morfessor BIBREF11 to learn whether more linguistically motivated subwords offer any advantage over simple n-grams.
To evaluate the impact subword information has on in-vocabulary (IV) word representations, we run intrinsic evaluations consisting of word similarity and word analogy tasks. The incorporation of subword information results in similar gains (and losses) to that of fastText over Skip-gram. Whereas incorporating n-gram subwords tends to capture more syntactic information, unsupervised morphemes better preserve semantics while also improving syntactic results. Given that intrinsic performance can correlate poorly with performance on downstream tasks BIBREF12 , we also conduct evaluation using the VecEval suite of tasks BIBREF13 , in which
all subword models, including fastText, show no significant improvement over word-level models.
We verify the model's ability to represent OOV words by quantitatively evaluating nearest-neighbors. Results show that, like fastText, both LexVec n-gram and (to a lesser degree) unsupervised morpheme models give coherent answers.
This paper discusses related word ( $§$ "Related Work" ), introduces the subword LexVec model ( $§$ "Subword LexVec" ), describes experiments ( $§$ "Materials" ), analyzes results ( $§$ "Results" ), and concludes with ideas for future works ( $§$ "Conclusion and Future Work" ).
The models that perform the word embedding can be divided into two classes: predictive, which learn a target or context word distribution, and counting, which use a raw, weighted, or factored word-context co-occurrence matrix (Baroni et al., 2014).
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What are the two types of models performing the word embedding?
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Predictive and counting.
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Tell me if these teams are in or out of the NBA Playoffs for this year, Orlando Magic, Detroit Pistons, Boston Celtics, Dallas Mavericks, and Phoenix Suns
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The teams from your list that are in the Playoffs are Boston Celtics & Phoenix Suns. The remainder, is not in this years playoffs, those are the Orlando Magic, Detroit Pistons, & Dallas Mavericks.
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