What are the top 3 methods used to find Autoregressive Parameters in Data Science?
In order to find autoregressive parameters, you will first need to understand what autoregression is. Autoregression is a statistical method used to create a model that describes data as a function of linear regression of lagged values of the dependent variable. In other words, it is a model that uses past values of a dependent variable in order to predict future values of the same dependent variable.
In time series analysis, autoregression is the use of previous values in a time series to predict future values. In other words, it is a form of regression where the dependent variable is forecasted using a linear combination of past values of the independent variable. The parameter values for the autoregression model are estimated using the method of least squares.
The autoregressive parameters are the coefficients in the autoregressive model. These coefficients can be estimated in a number of ways, including ordinary least squares (OLS), maximum likelihood (ML), or least squares with L1 regularization (LASSO). Once estimated, the autoregressive parameters can be used to predict future values of the dependent variable.
To find the autoregressive parameters, you need to use a method known as least squares regression. This method finds the parameters that minimize the sum of the squared residuals. The residual is simply the difference between the predicted value and the actual value. So, in essence, you are finding the parameters that best fit the data.
![What are the top 3 methods used to find Autoregressive Parameters in Data Science?](https://sp-ao.shortpixel.ai/client/to_auto,q_glossy,ret_img,w_525,h_437/https://enoumen.com/wp-content/uploads/2022/10/autoregressive_models-1024x852.png)
How to Estimate Autoregressive Parameters?
There are three main ways to estimate autoregressive parameters: ordinary least squares (OLS), maximum likelihood (ML), or least squares with L1 regularization (LASSO).
Ordinary Least Squares: Ordinary least squares is the simplest and most common method for estimating autoregressive parameters. This method estimates the parameters by minimizing the sum of squared errors between actual and predicted values.
Maximum Likelihood: Maximum likelihood is another common method for estimating autoregressive parameters. This method estimates the parameters by maximizing the likelihood function. The likelihood function is a mathematical function that quantifies the probability of observing a given set of data given certain parameter values.
Least Squares with L1 Regularization: Least squares with L1 regularization is another method for estimating autoregressive parameters. This method estimates the parameters by minimizing the sum of squared errors between actual and predicted values while also penalizing models with many parameters. L1 regularization penalizes models by adding an extra term to the error function that is proportional to the sum of absolute values of the estimator coefficients.
Finding Autoregressive Parameters: The Math Behind It
To find the autoregressive parameters using least squares regression, you first need to set up your data in a certain way. You need to have your dependent variable in one column and your independent variables in other columns. For example, let’s say you want to use three years of data to predict next year’s sales (the dependent variable). Your data would look something like this:
| Year | Sales |
|——|——-|
| 2016 | 100 |
| 2017 | 150 |
| 2018 | 200 |
Next, you need to calculate the means for each column. For our sales example, that would look like this:
$$ \bar{Y} = \frac{100+150+200}{3} = 150$$
Now we can calculate each element in what’s called the variance-covariance matrix:
$$ \operatorname {Var} (X)=\sum _{i=1}^{n}\left({x_{i}}-{\bar {x}}\right)^{2} $$
and
$$ \operatorname {Cov} (X,Y)=\sum _{i=1}^{n}\left({x_{i}}-{\bar {x}}\right)\left({y_{i}}-{\bar {y}}\right) $$
For our sales example, that calculation would look like this:
Advertise with us - Post Your Good Content Here
We are ranked in the Top 20 on Google
AI Dashboard is available on the Web, Apple, Google, and Microsoft, PRO version
$$ \operatorname {Var} (Y)=\sum _{i=1}^{3}\left({y_{i}}-{\bar {y}}\right)^{2}=(100-150)^{2}+(150-150)^{2}+(200-150)^{2})=2500 $$
and
$$ \operatorname {Cov} (X,Y)=\sum _{i=1}^{3}\left({x_{i}}-{\bar {x}}\right)\left({y_{i}}-{\bar {y}}\right)=(2016-2017)(100-150)+(2017-2017)(150-150)+(2018-2017)(200-150))=-500 $$
Now we can finally calculate our autoregressive parameters! We do that by solving this equation:
$$ \hat {\beta }=(X^{\prime }X)^{-1}X^{\prime }Y=\frac {1}{2500}\times 2500\times (-500)=0.20 $$\.20 . That’s it! Our autoregressive parameter is 0\.20 . Once we have that parameter, we can plug it into our autoregressive equation:
$$ Y_{t+1}=0\.20 Y_t+a_1+a_2+a_3footnote{where $a_1$, $a_2$, and $a_3$ are error terms assuming an AR(3)} .$$ And that’s how you solve for autoregressive parameters! Of course, in reality you would be working with much larger datasets, but the underlying principles are still the same. Once you have your autoregressive parameters, you can plug them into the equation and start making predictions!.
Which Method Should You Use?
The estimation method you should use depends on your particular situation and goals. If you are looking for simple and interpretable results, then Ordinary Least Squares may be the best method for you. If you are looking for more accurate predictions, then Maximum Likelihood or Least Squares with L1 Regularization may be better methods for you.
Autoregressive models STEP BY STEP:
1) Download data: The first step is to download some data. This can be done by finding a publicly available dataset or by using your own data if you have any. For this example, we will be using data from the United Nations Comtrade Database.
2) Choose your variables: Once you have your dataset, you will need to choose the variables you want to use in your autoregression model. In our case, we will be using the import and export values of goods between countries as our independent variables.
3) Estimate your model: After choosing your independent variables, you can estimate your autoregression model using the method of least squares. OLS estimation can be done in many statistical software packages such as R or STATA.
4) Interpret your results: Once you have estimated your model, it is important to interpret the results in order to understand what they mean. The coefficients represent the effect that each independent variable has on the dependent variable. In our case, the coefficients represent the effect that imports and exports have on trade balance. A positive coefficient indicates that an increase in the independent variable leads to an increase in the dependent variable while a negative coefficient indicates that an increase in the independent variable leads to a decrease in the dependent variable.
5)Make predictions: Finally, once you have interpreted your results, you can use your autoregression model to make predictions about future values of the dependent variable based on past values of the independent variables.
Conclusion: In this blog post, we have discussed what autoregression is and how to find autoregressive parameters.
Estimating an autoregression model is a relatively simple process that can be done in many statistical software packages such as R or STATA.
In statistics and machine learning, autoregression is a modeling technique used to describe the linear relationship between a dependent variable and one more independent variables. To find the autoregressive parameters, you can use a method known as least squares regression which minimizes the sum of squared residuals. This blog post also explains how to set up your data for calculating least squares regression as well as how to calculate Variance and Covariance before finally calculating your autoregressive parameters. After finding your parameters you can plug them into an autoregressive equation to start making predictions about future events!
We have also discussed three different methods for estimating those parameters: Ordinary Least Squares, Maximum Likelihood, and Least Squares with L1 Regularization. The appropriate estimation method depends on your particular goals and situation.
![Machine Learning For Dummies](https://sp-ao.shortpixel.ai/client/to_auto,q_glossy,ret_img,w_525,h_787/https://enoumen.com/wp-content/uploads/2022/03/machine_learning_for_dummies_720X1080-683x1024.png)
Machine Learning For Dummies App
Machine Learning For Dummies on iOs: https://apps.apple.com/
Machine Learning For Dummies on Windows: https://www.
Machine Learning For Dummies Web/Android on Amazon: https://www.amazon.
What are some good datasets for Data Science and Machine Learning?
Machine Learning Engineer Interview Questions and Answers
Machine Learning Breaking News
Transformer – Machine Learning Models
Machine Learning – Software Classification
Autoregressive Model
Autoregressive generative models can estimate complex continuous data distributions such as trajectory rollouts in an RL environment, image intensities, and audio. Traditional techniques discretize continuous data into various bins and approximate the continuous data distribution using categorical distributions over the bins. This approximation is parameter inefficient as it cannot express abrupt changes in density without using a significant number of additional bins. Adaptive Categorical Discretization (ADACAT) is proposed in this paper as a parameterization of 1-D conditionals that is expressive, parameter efficient, and multimodal. A vector of interval widths and masses is used to parameterize the distribution known as ADACAT. Figure 1 showcases the difference between the traditional uniform categorical discretization approach with the proposed ADACAT.
Each component of the ADACAT distribution has non-overlapping support, making it a specific subfamily of mixtures of uniform distributions. ADACAT generalizes uniformly discretized 1-D categorical distributions. The proposed architecture allows for variable bin widths and more closely approximates the modes of two Gaussians mixture than a uniformly discretized categorical, making it highly expressive than the latter. Additionally, a distribution’s support is discretized using quantile-based discretization, which bins data into groups with similar measured data points. ADACAT uses deep autoregressive frameworks to factorize the joint density into numerous 1-D conditional ADACAT distributions in problems with more than one dimension.
Continue reading | Check out the paper and github link.
Pytorch – Computer Application
https://torchmetrics.readthedocs.io/en/stable//index.html
Best practices for training PyTorch model
What are some ways we can use machine learning and artificial intelligence for algorithmic trading in the stock market?
What are some good datasets for Data Science and Machine Learning?
Top 100 Data Science and Data Analytics and Data Engineering Interview Questions and Answers
Machine Learning Engineer Interview Questions and Answers
- [R] How do you search for implementations of Mixture of Expert models that can be trained locally in a laptop or desktop without ultra-high end GPUs?by /u/Furiousguy79 (Machine Learning) on July 25, 2024 at 11:12 pm
Hi, I am a 2nd year PhD student in CS. My supervisor just got this idea about MoEs and fairness and asked me to implement it ( work on a toy classification problem on tabular data and NOT language data). However as it is not their area of expertise, they did not give any guidelines on how to approach it. My main question is: How do I search for or proceed with implementing a mixture of expert models? The ones that I find are for chatting and such but I mainly work with tabular EHR data. This is my first foray into this area (LLMs and MoEs) and I am kind of lost with all these Mixtral, openMoE, etc. As we do not have access to Google Collab or have powerful GPUs I have to rely on local training (My lab PC has 2080ti and my laptop has 4070). Any guideline or starting point on how to proceed would be greatly appreciated. submitted by /u/Furiousguy79 [link] [comments]
- [D] What do you use LLMs for?by /u/RND_RandoM (Machine Learning) on July 25, 2024 at 10:31 pm
Just wanted to start a small discussion about why you use LLMs and which model works best for your use case. I am asking because I am concerned that there is little use for LLMs apart from doing role play, helping with coding, and answering general questions submitted by /u/RND_RandoM [link] [comments]
- [R] Moderating LLM Inputs with PromptGuardby /u/Different-General700 (Machine Learning) on July 25, 2024 at 10:12 pm
Meta's release of its latest Llama language model family this week, including the massive Llama-3 405B model, has generated a great deal of excitement among AI developers. These open-weights frontier models, which have been updated with a new license that allows unrestricted use of outputs, will enable significant improvements to AI-powered applications, and enable widespread commercial use of synthetic data. Less discussed, but no less important, are Meta's latest open moderation tools, including a new model called PromptGuard. PromptGuard is a small, lightweight classification model trained to detect malicious prompts, including jailbreaks and prompt injections. These attacks can be used to manipulate language models to produce harmful outputs or extract sensitive information. Companies building enterprise-ready applications must be able to detect and mitigate these attacks to ensure their models are safe to use, especially in sensitive and highly-regulated domains like healthcare, finance, and law. PromptGuard is a text classification model based on mDeBERTa-v3-base, a small transformer model with multilingual capabilities. Meta trained this model to output probabilities for 3 classes: BENIGN, INJECTION, and JAILBREAK. The JAILBREAK class is designed to identify malicious user prompts (such as the "Do Anything Now(opens in a new tab)" or DAN prompt, which instructs a language model to ignore previous instructions and enter an unrestricted mode). On the other hand, the INJECTION class is designed to identify retrieved contexts, such as a webpage or document, which have been poisoned with malicious content to influence the model's output. In our tests, we find that the model is able to identify common jailbreaks like DAN, but also labels benign prompts as injections. This likely happens because the model is trained to handle both prompts and retrieved contexts (such as web searches and news articles), and a benign prompt may appear similar to a malicious context. As stated in the model card: Application developers typically want to allow users flexibility in how they interact with an application, and to only filter explicitly violating prompts (what the ‘jailbreak’ label detects). Third-party content has a different expected distribution of inputs (we don’t expect any “prompt-like” content in this part of the input) This indicates that when applying the model to user prompts, you may want to ignore the INJECTION label, and only filter JAILBREAK inputs. On the other hand, when filtering third-party context to show to the model, such as a news article, you'd want to remove both JAILBREAK and INJECTION labels. We wrote a quick blog post about how you can use PromptGuard to protect your language models from malicious inputs. You can read more here: https://www.trytaylor.ai/blog/promptguard submitted by /u/Different-General700 [link] [comments]
- [P] How to make "Out-of-sample" Predictionsby /u/Individual_Ad_1214 (Machine Learning) on July 25, 2024 at 7:47 pm
My data is a bit complicated to describe so I'm going try to describe something analogous. Each example is randomly generated, but you can group them based on a specific but latent (by latent I mean this isn't added into the features used to develop a model, but I have access to it) feature (in this example we'll call this number of bedrooms). Feature x1 Feature x2 Feature x3 ... Output (Rent) Row 1 Row 2 Row 3 Row 4 Row 5 Row 6 Row 7 2 Row 8 1 Row 9 0 So I can group Row 1, Row 2, and Row 3 based on a latent feature called number of bedrooms (which in this case is 0 bedroom). Similarly, Row 4, Row 5, & Row 6 have 2 Bedrooms, and Row 7, Row 8, & Row 9 have 4 Bedrooms. Furthermore, these groups also have an optimum price which is used to create output classes (output here is Rent; increase, keep constant, or decrease). So say the optimum price for the 4 bedrooms group is $3mil, and row 7 has a price of $4mil (=> 3 - 4 = -1 mil, i.e a -ve value so convert this to class 2, or above optimum or increase rent), row 8 has a price of $3mil (=> 3 - 3 = 0, convert this to class 1, or at optimum), and row 9 has a price of $2mil (3 - 2 = 1, i.e +ve value, so convert this to class 0, or below optimum, or decrease rent). I use this method to create an output class for each example in the dataset (essentially, if example x has y number of bedrooms, I get the known optimum price for that number of bedrooms and I subtract the example's price from the optimum price). Say I have 10 features (e.g. square footage, number of bathrooms, parking spaces etc.) in the dataset, these 10 features provide the model with enough information to figure out the "number of bedrooms". So when I am evaluating the model, feature x1 feature x2 feature x3 ... Row 10 e.g. I pass into the model a test example (Row 10) which I know has 4 bedrooms and is priced at $6mil, the model can accurately predict class 2 (i.e increase rent) for this example. Because the model was developed using data with a representative number of bedrooms in my dataset. Features.... Output (Rent) Row 1 0 Row 2 0 Row 3 0 However, my problem arises at examples with a low number of bedrooms (i.e. 0 bedrooms). The input features doesn't have enough information to determine the number of bedrooms for examples with a low number of bedrooms (which is fine because we assume that within this group, we will always decrease the rent, so we set the optimum price to say $2000. So row 1 price could be $8000, (8000 - 2000 = 6000, +ve value thus convert to class 0 or below optimum/decrease rent). And within this group we rely on the class balance to help the model learn to make predictions because the proportion is heavily skewed towards class 0 (say 95% = class 0 or decrease rent, and 5 % = class 1 or class 2). We do this based the domain knowledge of the data (so in this case, we would always decrease the rent because no one wants to live in a house with 0 bedrooms). MAIN QUESTION: We now want to predict (or undertake inference) for examples with number of bedrooms in between 0 bedrooms and 2 bedrooms (e.g 1 bedroom NOTE: our training data has no example with 1 bedroom). What I notice is that the model's predictions on examples with 1 bedroom act as if these examples had 0 bedrooms and it mostly predicts class 0. My question is, apart from specifically including examples with 1 bedroom in my input data, is there any other way (more statistics or ML related way) for me to improve the ability of my model to generalise on unseen data? submitted by /u/Individual_Ad_1214 [link] [comments]
- [D] Will An Unsupervised FSD Eventually Be Efficient Enough Run on Tesla's HW3?by /u/ZeApelido (Machine Learning) on July 25, 2024 at 7:32 pm
Tesla has a version (V12.5) of their supervised "Full Self Driving" that potential showing signficant improvements, though we will wait to see how much miles per critical disengagment have gone up. (Maybe 600-1000. Previous versions at 100-200 miles per critical disengagement). In order to make this improvement, they upped the parameter count by 5x the previous models. They are just barely making it function on HW3 (works on HW4). These models are already taking advantage of distillation and compression techniques. Considering that the miles per critical disengagement still needs to go up another 100x, I would think model parameter count will have to go up signficantly, maybe 10x-100x? While there are continuing advances in model distillation and compression, I find it hard to fathom that much larger models needed to achieve unsupervised driving will be compressed even further. Tweets like this imply (presumably from advances like LLAMA 2 to LLAMA 3) that these compression ratios will continue at a massive pace. https://x.com/wintonARK/status/1816537413206048915 What do you think? To me, the likely needed increase in model size to get to robotaxi level fidelity will outweigh any advances in distillation so that HW3 will unlikely be able to handle the model. submitted by /u/ZeApelido [link] [comments]
- [R] EMNLP Paper review scoresby /u/Immediate-Hour-8466 (Machine Learning) on July 25, 2024 at 7:06 pm
EMNLP paper review scores Overall assessment for my paper is 2, 2.5 and 3. Is there any chance that it may still be selected? The confidence is 2, 2.5 and 3. The soundness is 2, 2.5, 3.5. I am not sure how soundness and confidence may affect my paper's selection. Pls explain how this works. Which metrics should I consider important. Thank you! submitted by /u/Immediate-Hour-8466 [link] [comments]
- [N] OpenAI announces SearchGPTby /u/we_are_mammals (Machine Learning) on July 25, 2024 at 6:41 pm
https://openai.com/index/searchgpt-prototype/ We’re testing SearchGPT, a temporary prototype of new AI search features that give you fast and timely answers with clear and relevant sources. submitted by /u/we_are_mammals [link] [comments]
- Compute is on Output. Not input. [D]by /u/juliannorton (Machine Learning) on July 25, 2024 at 5:34 pm
Transformer-based LLMs like GPT, Gemini, LLaMa, are decoder-only architectures. This means that the compute utilized and the time it takes for an output is directly related to the size of the output, NOT the size of the input. Meaning you could feed it 100 pages, but if the output is 1 page, that's what matters for compute. It’s notoriously tricky to get an LLM to output text that’s short and concise, and many practitioners give up trying to get it to be concise after some tinkering with prompt engineering. However, fine-tuning deals with the problem at the source, retraining the model to prefer outputting shorter outputs. submitted by /u/juliannorton [link] [comments]
- [P] Local Llama 3.1 and Marqo Retrieval Augmented Generationby /u/elliesleight (Machine Learning) on July 25, 2024 at 4:45 pm
I built a simple starter demo of a Knowledge Question and Answering System using Llama 3.1 (8B GGUF) and Marqo. Feel free to experiment and build on top of this yourselves! GitHub: https://github.com/ellie-sleightholm/marqo-llama3_1 submitted by /u/elliesleight [link] [comments]
- [N] AI achieves silver-medal standard solving International Mathematical Olympiad problemsby /u/we_are_mammals (Machine Learning) on July 25, 2024 at 4:16 pm
https://deepmind.google/discover/blog/ai-solves-imo-problems-at-silver-medal-level/ They solved 4 of the 6 IMO problems (although it took days to solve some of them). This would have gotten them a score of 28/42, just one point below the gold-medal level. submitted by /u/we_are_mammals [link] [comments]
- [R] Explainability of HuggingFace Models (LLMs) for Text Summarization/Generation Tasksby /u/PhoenixHeadshot25 (Machine Learning) on July 25, 2024 at 3:19 pm
Hi community, I am exploring the Responsible AI domain where I have started reading about methods and tools to make Deep Learning Models explainable. I have already used SHAP and LIMe for ML model explainability. However, I am unsure about their use in explaining LLMs. I know that these methods are model agnostic but can we use these methods for Text Generation or Summarization tasks? I got reference docs from Shap explaining GPT2 for text generation tasks, but I am unsure about using it for other newer LLMs. Additionally, I would like to know, are there any better ways for Explainable AI for LLMs? submitted by /u/PhoenixHeadshot25 [link] [comments]
- Worth it to take a pay cut for the data scientist title?by /u/son_of_tv_c (Data Science) on July 25, 2024 at 3:17 pm
I have an MS in stats and 7 years as an analyst under my belt. I've been looking for a data scientist jb ever since I got the MS 4 years ago (got it part time while I started as an analyst) and have been having a hell of a time at it. I get plenty of interest in analyst positoins, but little interest in data scientist positoins. As I'm sure you all know, there is considerable overlap between the titles but HR drones and ATS doesn't necessarily know this. All they care about is key words. I've been offered a data scientist positoin at a company that I am ready to accept. The positoin is a little underpaid for a DS but about enough for me right now, but I'm thinking it could be a great stepping stone. I work that for 2-3 years then I'm competitive for higher compensated DS positoins. However I just got off the phone with a recriuter for a DA positoin that would pay between 25-40k more than the DS positoin (it's just a band at this point). The responsibilities are similar, it's just that this place has more money and is located in a HCOL are (both are remote though so COL and relocating are not a factor for me). More money now would be great, but I don't really know if this is going to leave me in a better position in a few years. Obviously, we're talking an offer vs just one phone screen, the higher DA positoin isn't a sure thing right now. But I'm just wondering if you guys would even keep pursing the DA positoin or just take the DS positoin and make up the difference in a few years with a higher paid DS positoin? Also I hate that this is a factor but I've done 12 interveiws just this month, I really REALLY don't want to do anymore, so it's a huge factor in me wanting to just drop out of the DA interveiw process and take the DS. submitted by /u/son_of_tv_c [link] [comments]
- [D] High-Dimensional Probabilistic Modelsby /u/smorad (Machine Learning) on July 25, 2024 at 2:58 pm
What is the standard way to model high-dimensional stochastic processes today? I have some process defined over images x, and I would like to compute P(x' | x, z) for all x'. I know there are Normalizing Flows, Gaussian Processes, etc, but I do not know which to get started with. I specifically want to compute the probabilities, not just sample some x' ~ P(x, z). submitted by /u/smorad [link] [comments]
- [R] Shared Imagination: LLMs Hallucinate Alikeby /u/zyl1024 (Machine Learning) on July 25, 2024 at 1:49 pm
Happy to share our recent paper, where we demonstrate that LLMs exhibit surprising agreement on purely imaginary and hallucinated contents -- what we call a "shared imagination space". To arrive at this conclusion, we ask LLMs to generate questions on hypothetical contents (e.g., a made-up concept in physics) and then find that they can answer each other's (unanswerable and nonsensical) questions with much higher accuracy than random chance. From this, we investigate in multiple directions on its emergence, generality and possible reasons, and given such consistent hallucination and imagination behavior across modern LLMs, discuss implications to hallucination detection and computational creativity. Link to the paper: https://arxiv.org/abs/2407.16604 Link to the tweet with result summary and highlight: https://x.com/YilunZhou/status/1816371178501476473 Please feel free to ask any questions! The main experiment setup and finding. submitted by /u/zyl1024 [link] [comments]
- Forecast time series modelby /u/uraz5432 (Data Science) on July 25, 2024 at 1:00 pm
Hello, I am new to forecasting, looking for suggestions on what model/ models to use for my use case. I have time series data on free trial signups for users to our product. Mainly two categories of users: US users and non US users. The users have unlimited free trial. They can convert to full paid customers anytime if they want to get all the features. We have 50% of the users convert to paid within the first month of the trial, 10% in month 2, and so on. By 4th month of free trial sign up, the conversion to paid is around 1%, and this is then a non zero value for as far as the data goes back ( let’s say 15 months from trial sign up). I have last two years of data for the free trial by each segment. I am using a simple linear model with seasonally for the month to forecast 12 months out for the free trial orders. How can I use the above to forecast the conversion to paid by month for next 12 months? Like mentioned above, the conversions can happen between month 1 to month 15 from the trial sign up date. I would need to forecast US and non US separately. What would be some models to try? Any suggestions on forecasting trials and paid conversions are appreciated. Thanks in advance. submitted by /u/uraz5432 [link] [comments]
- How do you describe your job to others who don’t know?by /u/Rare_Art_9541 (Data Science) on July 25, 2024 at 12:53 pm
I always struggle with describing what I do without overcomplicating it. Especially with my parents. They speak Spanish and what I try to describe in Spanish I can’t communicate it. submitted by /u/Rare_Art_9541 [link] [comments]
- [R] Paper NAACL 2024: "Reliability Estimation of News Media Sources: Birds of a Feather Flock Together"by /u/sergbur (Machine Learning) on July 25, 2024 at 9:10 am
For people working on information verification in general, for instance, working on fact checking, fake news detection or even using RAG from news articles this paper may be useful. Authors use different reinforcement learning techniques to estimate reliability values of news media outlets based on how they interact on the web. The method is easy to scale since the source code is available to build larger hyperlink-based interaction graphs from Common Crawl News. Authors also released the computed values and dataset with news media reliability annotation: Github repo: https://github.com/idiap/News-Media-Reliability Paper: https://aclanthology.org/2024.naacl-long.383/ Live Demo Example: https://lab.idiap.ch/criteria/ In the demo, the retrieved news articles will be order not only by the match to the query but also by the estimated reliability for each sources (URL domains are color coded from green to red, for instance, scrolling down will show results coming from less reliable sources marked with red-ish colors). Alternatively, if a news URL or a news outlet domain (e.g. apnews.com) is given as a query, information about the estimated values are detailed (e.g. showing the neighboring sources interacting with the media, etc.) Have a nice day, everyone! 🙂 submitted by /u/sergbur [link] [comments]
- [D] ACL ARR June (EMNLP) Review Discussionby /u/always_been_a_toy (Machine Learning) on July 25, 2024 at 4:45 am
Too anxious about reviews as they didn’t arrive yet! Wanted to share with the community and see the reactions to the reviews! Rant and stuff! Be polite in comments. submitted by /u/always_been_a_toy [link] [comments]
- "[Discussion]" Where do you get your updates on latest research in video generation and computer vision?by /u/Sobieski526 (Machine Learning) on July 24, 2024 at 9:20 pm
As the title says, looking for some tips on how you keep track of the latest research in video generation and CV. I have been reading through https://cvpr.thecvf.com/ and it's a great source, are there any simiar ones? submitted by /u/Sobieski526 [link] [comments]
- [R] Pre-prompting your LLM increases performanceby /u/CalendarVarious3992 (Machine Learning) on July 24, 2024 at 8:33 pm
Research done at UoW shows that pre-prompting your LLM, or providing context prior to asking your question leads to better results. Even when the context is self generated. https://arxiv.org/pdf/2110.08387 For example asking, "What should I do while in Rome?" is less effective than a series of prompts, "What are the top restaraunts in Rome?" "What are the top sight seeing locations in Rome?" "Best things to do in Rome" "What should I do in Rome?" I always figured this was the case from anecdotal evidence but good to see people who are way starter than me explain it in this paper. And while chain prompting is a little more time consuming there's chrome extensions like ChatGPT Queue that ease up the process. Are their any other "hacks" to squeeze out better performance ? submitted by /u/CalendarVarious3992 [link] [comments]
Active Hydrating Toner, Anti-Aging Replenishing Advanced Face Moisturizer, with Vitamins A, C, E & Natural Botanicals to Promote Skin Balance & Collagen Production, 6.7 Fl Oz
Age Defying 0.3% Retinol Serum, Anti-Aging Dark Spot Remover for Face, Fine Lines & Wrinkle Pore Minimizer, with Vitamin E & Natural Botanicals
Firming Moisturizer, Advanced Hydrating Facial Replenishing Cream, with Hyaluronic Acid, Resveratrol & Natural Botanicals to Restore Skin's Strength, Radiance, and Resilience, 1.75 Oz
Skin Stem Cell Serum
Smartphone 101 - Pick a smartphone for me - android or iOS - Apple iPhone or Samsung Galaxy or Huawei or Xaomi or Google Pixel
Can AI Really Predict Lottery Results? We Asked an Expert.
![](https://djamgatech.com/wp-content/uploads/2022/05/azure_fundamentals_book_cover1.jpeg)
![Football/Soccer World Cup 2022 Guide and Past World Cups History and Quiz illustrated](https://sp-ao.shortpixel.ai/client/to_auto,q_glossy,ret_img,w_300/http://enoumen.com/wp-content/uploads/2022/10/world_cup_guide_quiz_trivia3.png)
Djamgatech
![](data:image/svg+xml,%3Csvg%20xmlns=%22http://www.w3.org/2000/svg%22%20viewBox=%220%200%20400%20266.66666666667%22%3E%3C/svg%3E)
Read Photos and PDFs Aloud for me iOS
Read Photos and PDFs Aloud for me android
Read Photos and PDFs Aloud For me Windows 10/11
Read Photos and PDFs Aloud For Amazon
Get 20% off Google Workspace (Google Meet) Business Plan (AMERICAS): M9HNXHX3WC9H7YE (Email us for more)
Get 20% off Google Google Workspace (Google Meet) Standard Plan with the following codes: 96DRHDRA9J7GTN6(Email us for more)
FREE 10000+ Quiz Trivia and and Brain Teasers for All Topics including Cloud Computing, General Knowledge, History, Television, Music, Art, Science, Movies, Films, US History, Soccer Football, World Cup, Data Science, Machine Learning, Geography, etc....
![](data:image/svg+xml,%3Csvg%20xmlns=%22http://www.w3.org/2000/svg%22%20viewBox=%220%200%20210%20140%22%3E%3C/svg%3E)
List of Freely available programming books - What is the single most influential book every Programmers should read
- Bjarne Stroustrup - The C++ Programming Language
- Brian W. Kernighan, Rob Pike - The Practice of Programming
- Donald Knuth - The Art of Computer Programming
- Ellen Ullman - Close to the Machine
- Ellis Horowitz - Fundamentals of Computer Algorithms
- Eric Raymond - The Art of Unix Programming
- Gerald M. Weinberg - The Psychology of Computer Programming
- James Gosling - The Java Programming Language
- Joel Spolsky - The Best Software Writing I
- Keith Curtis - After the Software Wars
- Richard M. Stallman - Free Software, Free Society
- Richard P. Gabriel - Patterns of Software
- Richard P. Gabriel - Innovation Happens Elsewhere
- Code Complete (2nd edition) by Steve McConnell
- The Pragmatic Programmer
- Structure and Interpretation of Computer Programs
- The C Programming Language by Kernighan and Ritchie
- Introduction to Algorithms by Cormen, Leiserson, Rivest & Stein
- Design Patterns by the Gang of Four
- Refactoring: Improving the Design of Existing Code
- The Mythical Man Month
- The Art of Computer Programming by Donald Knuth
- Compilers: Principles, Techniques and Tools by Alfred V. Aho, Ravi Sethi and Jeffrey D. Ullman
- Gödel, Escher, Bach by Douglas Hofstadter
- Clean Code: A Handbook of Agile Software Craftsmanship by Robert C. Martin
- Effective C++
- More Effective C++
- CODE by Charles Petzold
- Programming Pearls by Jon Bentley
- Working Effectively with Legacy Code by Michael C. Feathers
- Peopleware by Demarco and Lister
- Coders at Work by Peter Seibel
- Surely You're Joking, Mr. Feynman!
- Effective Java 2nd edition
- Patterns of Enterprise Application Architecture by Martin Fowler
- The Little Schemer
- The Seasoned Schemer
- Why's (Poignant) Guide to Ruby
- The Inmates Are Running The Asylum: Why High Tech Products Drive Us Crazy and How to Restore the Sanity
- The Art of Unix Programming
- Test-Driven Development: By Example by Kent Beck
- Practices of an Agile Developer
- Don't Make Me Think
- Agile Software Development, Principles, Patterns, and Practices by Robert C. Martin
- Domain Driven Designs by Eric Evans
- The Design of Everyday Things by Donald Norman
- Modern C++ Design by Andrei Alexandrescu
- Best Software Writing I by Joel Spolsky
- The Practice of Programming by Kernighan and Pike
- Pragmatic Thinking and Learning: Refactor Your Wetware by Andy Hunt
- Software Estimation: Demystifying the Black Art by Steve McConnel
- The Passionate Programmer (My Job Went To India) by Chad Fowler
- Hackers: Heroes of the Computer Revolution
- Algorithms + Data Structures = Programs
- Writing Solid Code
- JavaScript - The Good Parts
- Getting Real by 37 Signals
- Foundations of Programming by Karl Seguin
- Computer Graphics: Principles and Practice in C (2nd Edition)
- Thinking in Java by Bruce Eckel
- The Elements of Computing Systems
- Refactoring to Patterns by Joshua Kerievsky
- Modern Operating Systems by Andrew S. Tanenbaum
- The Annotated Turing
- Things That Make Us Smart by Donald Norman
- The Timeless Way of Building by Christopher Alexander
- The Deadline: A Novel About Project Management by Tom DeMarco
- The C++ Programming Language (3rd edition) by Stroustrup
- Patterns of Enterprise Application Architecture
- Computer Systems - A Programmer's Perspective
- Agile Principles, Patterns, and Practices in C# by Robert C. Martin
- Growing Object-Oriented Software, Guided by Tests
- Framework Design Guidelines by Brad Abrams
- Object Thinking by Dr. David West
- Advanced Programming in the UNIX Environment by W. Richard Stevens
- Hackers and Painters: Big Ideas from the Computer Age
- The Soul of a New Machine by Tracy Kidder
- CLR via C# by Jeffrey Richter
- The Timeless Way of Building by Christopher Alexander
- Design Patterns in C# by Steve Metsker
- Alice in Wonderland by Lewis Carol
- Zen and the Art of Motorcycle Maintenance by Robert M. Pirsig
- About Face - The Essentials of Interaction Design
- Here Comes Everybody: The Power of Organizing Without Organizations by Clay Shirky
- The Tao of Programming
- Computational Beauty of Nature
- Writing Solid Code by Steve Maguire
- Philip and Alex's Guide to Web Publishing
- Object-Oriented Analysis and Design with Applications by Grady Booch
- Effective Java by Joshua Bloch
- Computability by N. J. Cutland
- Masterminds of Programming
- The Tao Te Ching
- The Productive Programmer
- The Art of Deception by Kevin Mitnick
- The Career Programmer: Guerilla Tactics for an Imperfect World by Christopher Duncan
- Paradigms of Artificial Intelligence Programming: Case studies in Common Lisp
- Masters of Doom
- Pragmatic Unit Testing in C# with NUnit by Andy Hunt and Dave Thomas with Matt Hargett
- How To Solve It by George Polya
- The Alchemist by Paulo Coelho
- Smalltalk-80: The Language and its Implementation
- Writing Secure Code (2nd Edition) by Michael Howard
- Introduction to Functional Programming by Philip Wadler and Richard Bird
- No Bugs! by David Thielen
- Rework by Jason Freid and DHH
- JUnit in Action
#BlackOwned #BlackEntrepreneurs #BlackBuniness #AWSCertified #AWSCloudPractitioner #AWSCertification #AWSCLFC02 #CloudComputing #AWSStudyGuide #AWSTraining #AWSCareer #AWSExamPrep #AWSCommunity #AWSEducation #AWSBasics #AWSCertified #AWSMachineLearning #AWSCertification #AWSSpecialty #MachineLearning #AWSStudyGuide #CloudComputing #DataScience #AWSCertified #AWSSolutionsArchitect #AWSArchitectAssociate #AWSCertification #AWSStudyGuide #CloudComputing #AWSArchitecture #AWSTraining #AWSCareer #AWSExamPrep #AWSCommunity #AWSEducation #AzureFundamentals #AZ900 #MicrosoftAzure #ITCertification #CertificationPrep #StudyMaterials #TechLearning #MicrosoftCertified #AzureCertification #TechBooks
Top 1000 Canada Quiz and trivia: CANADA CITIZENSHIP TEST- HISTORY - GEOGRAPHY - GOVERNMENT- CULTURE - PEOPLE - LANGUAGES - TRAVEL - WILDLIFE - HOCKEY - TOURISM - SCENERIES - ARTS - DATA VISUALIZATION
![zCanadian Quiz and Trivia, Canadian History, Citizenship Test, Geography, Wildlife, Secenries, Banff, Tourism](data:image/svg+xml,%3Csvg%20xmlns=%22http://www.w3.org/2000/svg%22%20viewBox=%220%200%20400%20266.66666666667%22%3E%3C/svg%3E)
Top 1000 Africa Quiz and trivia: HISTORY - GEOGRAPHY - WILDLIFE - CULTURE - PEOPLE - LANGUAGES - TRAVEL - TOURISM - SCENERIES - ARTS - DATA VISUALIZATION
![Africa Quiz, Africa Trivia, Quiz, African History, Geography, Wildlife, Culture](data:image/svg+xml,%3Csvg%20xmlns=%22http://www.w3.org/2000/svg%22%20viewBox=%220%200%20400%20266.66666666667%22%3E%3C/svg%3E)
Exploring the Pros and Cons of Visiting All Provinces and Territories in Canada.
![Exploring the Pros and Cons of Visiting All Provinces and Territories in Canada](data:image/svg+xml,%3Csvg%20xmlns=%22http://www.w3.org/2000/svg%22%20viewBox=%220%200%20300%20200%22%3E%3C/svg%3E)
Exploring the Advantages and Disadvantages of Visiting All 50 States in the USA
![Exploring the Advantages and Disadvantages of Visiting All 50 States in the USA](data:image/svg+xml,%3Csvg%20xmlns=%22http://www.w3.org/2000/svg%22%20viewBox=%220%200%20300%20200%22%3E%3C/svg%3E)
Health Health, a science-based community to discuss health news and the coronavirus (COVID-19) pandemic
- US infant mortality increased in 2022 for the first time in decades, CDC report showsby /u/cnn on July 25, 2024 at 6:37 pm
submitted by /u/cnn [link] [comments]
- Study raises hopes that shingles vaccine may delay onset of dementia | Dementia | The Guardianby /u/chilladipa on July 25, 2024 at 3:38 pm
submitted by /u/chilladipa [link] [comments]
- How fit is your city? New rankings by the American College of Sports Medicineby /u/idc2011 on July 25, 2024 at 3:35 pm
submitted by /u/idc2011 [link] [comments]
- Twice-Yearly Lenacapavir or Daily F/TAF for HIV Prevention in Cisgender Women | New England Journal of Medicineby /u/chilladipa on July 25, 2024 at 3:30 pm
submitted by /u/chilladipa [link] [comments]
- Biden Made a Healthy Decisionby /u/theatlantic on July 25, 2024 at 3:15 pm
submitted by /u/theatlantic [link] [comments]
Today I Learned (TIL) You learn something new every day; what did you learn today? Submit interesting and specific facts about something that you just found out here.
- TIL actor John Larroquette was the uncredited narrator of the prologue to the 1974 horror movie Texas Chainsaw Massacre. In lieu of cash, he was paid by the Director Tobe Hooper in Marijuana.by /u/openletter8 on July 25, 2024 at 6:56 pm
submitted by /u/openletter8 [link] [comments]
- TIL that the every Shakopee Mdewakanton Sioux indian receives a payout of around $1 million per year from casino profits.by /u/friendlystranger4u on July 25, 2024 at 6:22 pm
submitted by /u/friendlystranger4u [link] [comments]
- TIL Motorcycles in China are dictated by law to be decommissioned and destroyed in 13 years after registration regardless of the conditionsby /u/Easy_Piece_592 on July 25, 2024 at 5:56 pm
submitted by /u/Easy_Piece_592 [link] [comments]
- TIL a man named Jonathan Riches has filed more than 2,600 lawsuits since 2006. He even sued Guinness World Records to try to stop them from titling him as "the most litigious man in history".by /u/doopityWoop22 on July 25, 2024 at 5:03 pm
submitted by /u/doopityWoop22 [link] [comments]
- TIL that in 2018, an American half-pipe skier qualified for the Olympics despite minimal experience. Olympic requirements stated that an athlete needed to place in the top 30 at multiple events. She simply sought out events with fewer than 30 participants, showed up, and skied down without falling.by /u/ctdca on July 25, 2024 at 4:28 pm
submitted by /u/ctdca [link] [comments]
Reddit Science This community is a place to share and discuss new scientific research. Read about the latest advances in astronomy, biology, medicine, physics, social science, and more. Find and submit new publications and popular science coverage of current research.
- Abstinence-only sex education linked to higher pornography use among women | This finding adds to the ongoing conversation about the effectiveness and impacts of different sexuality education approaches.by /u/chrisdh79 on July 25, 2024 at 6:49 pm
submitted by /u/chrisdh79 [link] [comments]
- AlphaProof and AlphaGeometry 2 AI models achieve silver medal standard in solving International Mathematical Olympiad problemsby /u/Big_Profit9076 on July 25, 2024 at 5:59 pm
submitted by /u/Big_Profit9076 [link] [comments]
- Scientists have described a new species of chordate, Nuucichthys rhynchocephalus, the first soft-bodied vertebrate from the Drumian Marjum Formation of the American Great Basin.by /u/grimisgreedy on July 25, 2024 at 5:55 pm
submitted by /u/grimisgreedy [link] [comments]
- Secularists revealed as a unique political force in America, with an intriguing divergence from liberals. Unlike nonreligiosity, which denotes a lack of religious affiliation or belief, secularism involves an active identification with principles grounded in empirical evidence and rational thought.by /u/mvea on July 25, 2024 at 5:40 pm
submitted by /u/mvea [link] [comments]
- New shingles vaccine could reduce risk of dementia. The study found at least a 17% reduction in dementia diagnoses in the six years after the new recombinant shingles vaccination, equating to 164 or more additional days lived without dementia.by /u/Wagamaga on July 25, 2024 at 4:48 pm
submitted by /u/Wagamaga [link] [comments]
Reddit Sports Sports News and Highlights from the NFL, NBA, NHL, MLB, MLS, and leagues around the world.
- A's place their lone all-star, Mason Miller, on IL with fractured finger after hitting training tableby /u/Oldtimer_2 on July 25, 2024 at 8:15 pm
submitted by /u/Oldtimer_2 [link] [comments]
- Flyers sign All-Star Travis Konecny to an 8-year extension worth $70 millionby /u/Oldtimer_2 on July 25, 2024 at 7:45 pm
submitted by /u/Oldtimer_2 [link] [comments]
- Bills’ Von Miller says he believes domestic assault case to be closed, with no charges filedby /u/Oldtimer_2 on July 25, 2024 at 7:43 pm
submitted by /u/Oldtimer_2 [link] [comments]
- Padres' Dylan Cease throws no-hitter vs. Nationalsby /u/Oldtimer_2 on July 25, 2024 at 7:41 pm
submitted by /u/Oldtimer_2 [link] [comments]
- Appeal denied in Valieva case; U.S. skaters to get gold in Parisby /u/PrincessBananas85 on July 25, 2024 at 6:18 pm
submitted by /u/PrincessBananas85 [link] [comments]