We propose a semi-parametric and dynamic rank factor model for topic modeling, capable of (i) discovering topic prevalence over time, and (ii) learning contemporary multi-scale dependence structures, providing topic and word correlations as a byproduct. The high-dimensional and time-evolving ordinal/rank observations (such as word counts), after an arbitrary monotone transformation, are well accommodated through an underlying dynamic sparse factor model. The framework naturally admits heavy-tailed innovations, capable of inferring abrupt temporal jumps in the importance of topics. Posterior inference is performed through straightforward Gibbs sampling, based on the forward-filtering backward-sampling algorithm. Moreover, an efficient data subsampling scheme is leveraged to speed up inference on massive datasets. The modeling framework is illustrated on two real datasets: the US State of the Union Address and the JSTOR collection from Science .

Large language models (LLMs) encode extensive world knowledge through pre-training on massive datasets, which can then be fine-tuned for the question-answering (QA) task. However, effective strategies for fine-tuning LLMs for the QA task remain largely unexplored. To address this gap, we categorize supervised fine-tuning (SFT) data based on the extent of knowledge memorized by the pretrained LLMs and conduct a series of empirical analyses. Our experiments, involving four LLMs from three different model families, focus on three key factors: the amount of data required for SFT, the impact of different SFT datasets on model performance, and how data requirements vary across LLMs. The results show that as few as 60 data points during the SFT stage can activate the knowledge encoded during pre-training, enabling LLMs to perform the QA task. Additionally, SFT with data of varying memory levels has a significant impact on LLM performance, with the optimal dataset differing based on the specific model being fine-tuned.

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The holiday shopping season started a bit early this year, but that's only made things more confusing--what's really a deal? It's hard to know which deals to snag and which to walk away from. Luckily, we've done the hard work for you. WIRED reviewers try countless gadgets, tools, and digital delights of all kinds every week, and we have developed smart shopping tips and tricks to weed out fake discounts and bring you the real deals. We can say with confidence that these are the absolute best Black Friday and Cyber Monday deals you're going to find this weekend. You will find regular updates as products go out of stock and prices change, and we'll keep scouring to find more deals worth grabbing. Updated November 26, 2022: We've added deals on binoculars, VSSL's coffee grinder, the AeroPress Go, and more. We've updated pricing and availability throughout. We test products year-round and handpicked these deals. Products that are sold out or no longer discounted as of publishing will be crossed out . We'll update this guide throughout the Black Friday and Cyber Monday weekend. If you buy something using links in our stories, we may earn a commission. This helps support our journalism. Just like upgrading the bed you sleep on, few things will improve your life like a good chair. If you park your body in front of a desk all day, it's a good idea to give the humble chair more attention. Some of our favorites are on sale right now. See our Best Office Chairs guide for more. Our top office chair pick is a bit cheaper right now. We've tested more than 35 office chairs in the past year and this is the one to get based on comfort, quality, adjustability, and price. At this price, you'd be hard-pressed to find a cheaper way to get seven points of adjustment. The only thing we don't like is that pet hair tends to cling, so keep a lint roller handy. The Zeph looks wonderful--there are dozens of color customizations--but the only adjustment you can make is to raise the seat up or down.

During the difficult years since the start of the ongoing COVID-19 pandemic, the need for efficient artificial intelligence models, tools, and applications has been more evident than ever. Machine learning and data science, not to mention the huge amount of data they produce, form a clear new source of valuable information. New and innovative approaches are required to tackle the new research challenges faced in this area. In this framework, artificial intelligence is crucial and thus may be described as one of the most important research areas of our time. Since this view is applicable to the research community, it also faces huge challenges from the perspective of data management and involves emerging disciplines in information processing and related tools and applications.

Neural models combining representation learning and reasoning in an end-to-end trainable manner are receiving increasing interest. However, their use is severely limited by their computational complexity, which renders them unusable on real world datasets. We focus on the Neural Theorem Prover (NTP) model proposed by Rockt{\"{a}}schel and Riedel (2017), a continuous relaxation of the Prolog backward chaining algorithm where unification between terms is replaced by the similarity between their embedding representations. For answering a given query, this model needs to consider all possible proof paths, and then aggregate results - this quickly becomes infeasible even for small Knowledge Bases (KBs). We observe that we can accurately approximate the inference process in this model by considering only proof paths associated with the highest proof scores. This enables inference and learning on previously impracticable KBs.

Stochastic processes have many applications, including in finance and physics. It is an interesting model to represent many phenomena. Unfortunately the theory behind it is very difficult, making it accessible to a few'elite' data scientists, and not popular in business contexts. One of the most simple examples is a random walk, and indeed easy to understand with no mathematical background. However, time-continuous stochastic processes are always defined and studied using advanced and abstract mathematical tools such as measure theory, martingales, and filtration.

Learn to carry out pre-processing, visualization and machine learning tasks such as: clustering, classification and regression in R. You will be able to mine insights from text data and Twitter to give yourself & your company a competitive edge. My name is Minerva Singh and I am an Oxford University MPhil (Geography and Environment) graduate. I recently finished a PhD at Cambridge University (Tropical Ecology and Conservation). I have several years of experience in analyzing real life data from different sources using data science related techniques and producing publications for international peer reviewed journals.

Over the past few years, Deep Learning (DL) architectures and algorithms have made impressive advances in fields such as image recognition and speech processing. Their application to Natural Language Processing (NLP) was less impressive at first, but has now proven to make significant contributions, yielding state-of-the-art results for some common NLP tasks. Named entity recognition (NER), part of speech (POS) tagging or sentiment analysis are some of the problems where neural network models have outperformed traditional approaches. The progress in machine translation is perhaps the most remarkable among all. In this article I will go through some advancements for NLP in 2017 that rely on DL techniques.

The Center for Argument Technology (ARG-tech) located at the University of Dundee now provides tools based on in-house artificial intelligence designed for arguments. While that may sound completely useless given humans do extremely well at arguing each other, this AI is meant to make those arguments more productive, so everyone involved can reach an agreement. According to ARG-tech director Chris Reed, his group first turned to the BBC's Moral Maze 10 years ago. They created large "maps" based on every debate that took place on the show, and turned those maps into infographics using an algorithm to "determine the most central themes." From that data, the team pulled important issues, where participants stood, the highest points in conflict, and more.