**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.

**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:

$$ \operatorname {Var} (Y)=\sum _{i=1}^{3}\left({y_{i}}-{\bar {y}}\right)^{2}=(100-150)^{2}+(150-150)^{2}+(200-150)^{2})=2500 $$

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.**

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**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.

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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.*

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- [P] AI meme generatorby /u/QbeastIO (Machine Learning) on February 29, 2024 at 1:41 pm
We released an AI-driven meme generator for data engineering enthusiasts at Qbeast. This fun project helped us learn how to fine-tune AI models and customize datasets for humor. We're excited to share our experience with other enthusiasts who are interested in merging technology and creativity. Check out the story at https://qbeast.io/qbeasts-adventure-in-ai-driven-meme-creation/. submitted by /u/QbeastIO [link] [comments]

- [P] How to train Unet for whole heart segmentation? (MRI scans, 8 classes)by /u/ForceAffectionate245 (Machine Learning) on February 29, 2024 at 11:50 am
I have small computing resources (16gb GPU colab), which does not allow me to train 3d unet on high resolution scans (original resolution 512x512x160). I don’t want to break it into patches, since this is still better suited for microscopy tasks. Unet 2d cannot be trained on this data, since I need inter-slice dependencies, so I decided to train 3d unet at a small resolution of 128x128x160, this works out quite well, then I want to submit the resulting enlarged masks from 3d unet to the input as a second layer with a separate slice scan in Unet 2d with a high resolution (512x512x160) so that Unet 2d understands where the heart is and does multi-class segmentation. How adequate is this idea? I heard about the recurrent 2d model, but it seems that it will consume quite a lot of resources, because we need to keep the entire scan in memory. Thanks for any ideas and feedback. submitted by /u/ForceAffectionate245 [link] [comments]

- [D] RAG- Dimensionality reduction for embeddingsby /u/BlueOrangeBerries (Machine Learning) on February 29, 2024 at 11:40 am
Earlier last year around the release of GPT 4 I read about people doing dimensionality reduction on their vector embeddings, specifically principle component analysis, to make them better suited for RAG. Since then as the RAG scene has developed I haven’t seen much mention of doing this. Could anyone shed light on the merits of using dimensionality reduction for RAG? submitted by /u/BlueOrangeBerries [link] [comments]

- Can Hardware Specifications in Aircrafts support Machine Learning models? [R]by /u/aishwaryagm10 (Machine Learning) on February 29, 2024 at 9:25 am
Hi, I'm working on a project related to aircraft network security using Machine Learning Concepts and Cyber Threat database. I wanted to know details on the hardware components generally used in Military and Commercial Aircrafts to make sure my ML model and intelligence database can be accommodated in aircrafts on air without cloud dependency. It would be helpful if you could provide your insights on this idea of mine and details of the Microprocessors and Memory Storage components generally used in aircrafts. What is the amount of memory storage available in aircrafts and how much GPU is available. Will the CPU cycles be sufficient to run basic flight operations and the ML model alongside. These are some of my doubts. This information would be helpful for me to configure my AI models for network security. If you are aware of any publications or articles on the same, that would be helpful too. submitted by /u/aishwaryagm10 [link] [comments]

- [R] Pretrained diffusion U-nets for X-rays (single channel)by /u/roleparacelsus (Machine Learning) on February 29, 2024 at 9:11 am
Anyone know of any diffusion U-nets pretrained on X-ray data (single channel) that I could use to fine-tune? Image space or latent space - any. Preferably on mammograms, but any kind would be better than random initialization. submitted by /u/roleparacelsus [link] [comments]

- [D] Who still uses fine-tuning?by /u/handwerner142 (Machine Learning) on February 29, 2024 at 8:50 am
So now that OpenAI has just announced fine-tuning for their GPT-3.5 turbo model, I'm wondering: is fine-tuning still widely used? In the GPT-3 era fine-tuning was very useful because base models did not have an instruct equivalent, and even the instruct versions were far from matching the performance of GPT-3.5 and GPT-4. Today LLMs are so good that prompt engineering seems enough in most cases. Am I wrong? submitted by /u/handwerner142 [link] [comments]

- [R] Reversed Concept Drift (RCD) and algorithm for Concept Drift Detectionby /u/santiviquez (Machine Learning) on February 29, 2024 at 8:48 am
All ML models are designed to do one thing: learning a probability distribution in the form of P(y|X). In other words, they try to learn how to model an outcome 'y' given the input variables 'X'. This probability distribution, P(y|X), is also called Concept. Therefore, if the Concept changes, the model may become invalid. But how do we know if there is a new Concept in our data? Or, more importantly, how do we measure if the new Concept is affecting the model's performance? Here is a clever solution where the main ingredients are a reference dataset, one where the model's performance is known, and a dataset with the latest data we would like to monitor. Step-by-Step solution: We start by training an internal model on a chunk of the latest data. -> This allows us to learn the new possible Concept presented in the data. Next, we use the internal model to make predictions on the reference dataset. We then estimate the model's performance on the reference dataset, assuming the model's predictions on the monitoring data as ground truth. If the estimated performance of the internal model and the actual monitored model are very different, we then say that there has been a Concept Drift. To quantify how this Concept impacts performance, we subtract the actual model's performance on reference from the estimated performance and report a delta of the performance metric. -> This is what the plot below shows. The change of the F1-score due to Concept drift! This process is repeated for every new chunk of data that we get. https://preview.redd.it/z570r1dqmhlc1.jpg?width=2738&format=pjpg&auto=webp&s=3997aba54c71b13567bb78b8f5e4d244aa77c0b6 submitted by /u/santiviquez [link] [comments]

- Algorithm to detect Concept Driftby /u/santiviquez (Data Science) on February 29, 2024 at 8:24 am
All ML models are designed to do one thing: learning a probability distribution in the form of P(y|X). In other words, they try to learn how to model an outcome 'y' given the input variables 'X'. This probability distribution, P(y|X), is also called Concept. Therefore, if the Concept changes, the model may become invalid. But how do we know if there is a new Concept in our data? Or, more importantly, how do we measure if the new Concept is affecting the model's performance? Here is a clever solution where the main ingredients are a reference dataset, one where the model's performance is known, and a dataset with the latest data we would like to monitor. Step-by-Step solution: We start by training an internal model on a chunk of the latest data. -> This allows us to learn the new possible Concept presented in the data. Next, we use the internal model to make predictions on the reference dataset. We then estimate the model's performance on the reference dataset, assuming the model's predictions on the monitoring data as ground truth. If the estimated performance of the internal model and the actual monitored model are very different, we then say that there has been a Concept Drift. To quantify how this Concept impacts performance, we subtract the actual model's performance on reference from the estimated performance and report a delta of the performance metric. -> This is what the plot below shows. The change of the F1-score due to Concept drift! This process is repeated for every new chunk of data that we get. https://preview.redd.it/3hyu6a8jhhlc1.jpg?width=2738&format=pjpg&auto=webp&s=6898ac6ccb3675d2816870047ffd2a95f1aa53b2 submitted by /u/santiviquez [link] [comments]

- [D] Straight from Bachelor's to PhDby /u/samdav_ (Machine Learning) on February 29, 2024 at 6:17 am
I'm a 21-year-old student pursuing a BSc in Computer Science. I'm planning to pursue a PhD right after my BSc. I have research experience; I started out as an ML engineer intern in the first year of my studies at the Scientific Research Institute of the department of computer science at my uni. It's been almost a year now and I'm currently an ML research engineer. I've co-authored one research paper (local) and 3 IEEE conference papers. I work in diverse but ML/AI-related projects, funded by the government - mostly for the government. There are a few Q1-Q2 articles in the works that I'm co-authoring this year. I am fairly certain that I'll get some papers as first author before I graduate. I'm also gaining some industry experience by working remotely as an ML engineer in a Saudi company. I'm aiming for a top 50 PhD program. My goal is to be a research scientist. I love research and I know what I want to do with my career. An MSc will be a waste of my time. I'm seeking advice on steps I can take to enhance my application for a top 50 PhD program in ML/AI, particularly from those who have navigated this path. Any insights on strategies or experiences would be incredibly valuable to me. EDIT: I’m not disrespecting or condescending a masters degree in any way. I came to this decision because I’ve been advised to choose one or the other. Master’s mostly for management and similar career paths and PhD for academia, R&D, and executive career paths. I spend my days and nights in the lab with many PhDs and postdocs, so I know what to expect. submitted by /u/samdav_ [link] [comments]

- [R] How to think step-by-step: A mechanistic understanding of chain-of-thought reasoningby /u/Gaussian_Kernel (Machine Learning) on February 29, 2024 at 6:07 am
PDF: https://arxiv.org/pdf/2402.18312.pdf Findings: 1. Despite different reasoning requirements across different stages of CoT generation, the functional components of the model remain almost the same. Different neural algorithms are implemented as compositions of induction circuit-like mechanisms. Attention heads perform information movement between ontologically related (or negatively related) tokens. This information movement results in distinctly identifiable representations for such token pairs. Typically, this distinctive information movement starts from the very first layer and continues till the middle. While this phenomenon happens zero-shot, in-context examples exert pressure to quickly mix other task-specific information among tokens. Multiple different neural pathways are deployed to compute the answer, that too in parallel. Different attention heads, albeit with different probabilistic certainty, write the answer token (for each CoT subtask) to the last residual stream. These parallel answer generation pathways collect answers from different segments of the input. We found that while generating CoT, the model gathers answer tokens from the generated context, the question context, as well as the few-shot context. This provides a strong empirical answer to the open problem of whether LLMs actually use the context generated via CoT while answering questions. We observe a functional rift at the very middle of the LLM (16th decoder block in case of LLaMA-2 7B), which marks a phase shift in the content of residual streams and the functionality of the attention heads. Prior to this rift, the model primarily assigns bigram associations memorized via pretraining; it drastically starts following the in-context prior to and after the rift. It is likely that this is directly related to the token-mixing along ontological relatedness that happens only prior to the rift. Similarly, answer-writing heads appear only after the rift. Attention heads that (wrongly) collect the answer token from the few-shot examples are also bounded by the prior half of the model. Code: https://github.com/joykirat18/How-To-Think-Step-by-Step submitted by /u/Gaussian_Kernel [link] [comments]

- [P] RAG based text to code!!by /u/Delicious_Success303 (Machine Learning) on February 29, 2024 at 5:44 am
Hi all!! I am working on a project where I want to generate code from testcases written in plain english. I want code/scripts compatible with my own codebase so I want the model to have some context of my codebase. For that I was thinking RAG or finetuning. What do you think will be a better choice? And any resources if you guys can recommend for me to follow?...thanks submitted by /u/Delicious_Success303 [link] [comments]

- [D] is there a way to monitor the input prompts?by /u/Semantics777 (Machine Learning) on February 29, 2024 at 4:30 am
I have a set of metrics to detect toxicity and data leakage in the input prompts. If detected, I do not want the input prompts to even reach the LLM because the prompts are bad and it is a waste of money. But I want to log these input prompts, display the raw text as well as the metrics results. Is there a tool like this? submitted by /u/Semantics777 [link] [comments]

- Using Raspberry Pi & YOLO To Determine Weights of Irregular Objects [D] [P]by /u/Thin-Addition6686 (Machine Learning) on February 29, 2024 at 3:11 am
I am working on a system on a raspberry pi that uses a YOLO instance segmentation model to classify different foods and mask them. I then want to use the detected classes to find the weight of each food, and add it to a total counter. The camera will most likely be facing top down, so I am curious what the best way to find the depths of the food is. Currently, the code I have now just takes the 2d mask, so it just takes the mask of the object straight down and then finds the weight from the area of that mask. This isn't accurate because we are missing depth, and I need the system to be as accurate as possible. What are some possible low-cost, yet effective solutions I could use to find the volume of foods and not just the area. There could be multiple foods on one plate in the frame, and they would all have different shapes & sizes. They will most likely be breakfast foods, so scrambled eggs, tater tots, french toast, etc. submitted by /u/Thin-Addition6686 [link] [comments]

- Analysts with Weird Degrees: What is it?by /u/Itsallkosher1 (Data Science) on February 29, 2024 at 1:03 am
I have a degree in the Arts field. Have been successful in this field and peaked at where I am (mid 30s). Looking to make use of self-taught programming, Tableau, analytics, etc. to get another job/career start. If you were in a similar spot with an unrelated degree and unrelated previous career, what was it? Did you manage to find some work in data? Would love to hear your story for some inspiration. submitted by /u/Itsallkosher1 [link] [comments]

- Survival analysis on cancer patientsby /u/EmilyEmlz (Data Science) on February 29, 2024 at 12:55 am
Hi, I am doing my master’s thesis, and I would like any information on where I can get this type of dataset? I am primarily worried about lacking data variables in the dataset. Can someone offer me some guidance? submitted by /u/EmilyEmlz [link] [comments]

- [P] Speech-to-Text Benchmark: 47,638 mins transcribed per $1 on RTX3070 Ti (1000-fold cost reduction than managed services)by /u/SaladChefs (Machine Learning) on February 29, 2024 at 12:09 am
Speech-to-text benchmark with Parakeet TDT 1.1B Our previous Speech-to-text benchmarks on Whisper Large V3 benchmark (11,736 mins/$) and Whisper Large V2 benchmark (1681 mins/$) generated a healthy discussion here. Next on our list of open-source STT models is Parakeet TDT 1.1B which turned out to the winner. In this benchmark, we transcribed 17,305 hours of CommonVoice (en) audio to text from 5,209,130 audio files. Benchmark results: Parakeet TDT 1.1B on a RTX 3070 Ti delivered 47,638 minutes per $1 on a distributed cloud. For 1 Million hours of audio, that costs just $1260. Comparative cost to transcribe 1 Million hours of audio with Speech-to-text APIs/managed transcription services ranges from $200K - $1,500,000. To serve ASR inference at scale, a self-managed system built on open-source models like Parakeet/Whisper running on consumer GPUs delivers a 100-1000 fold cost reduction - a compelling alternative considering the savings. https://preview.redd.it/24xz31uuyelc1.jpg?width=2204&format=pjpg&auto=webp&s=1564868e62e56fde0909bad18d100731e785ac8c Advanced system architecture for batch jobs GPU Resource Pool: Hundreds of Salad nodes, equipped with dedicated GPUs, are utilized for tasks such as downloading/uploading, pre-processing/post-processing and transcribing. Cloud Storage: Audio files and generated assets stored in Cloudflare R2, which is AWS S3-compatible and incurs zero egress fees. Job Queue System: The Salad nodes retrieve jobs via AWS SQS, providing unique identifiers and accessible URLs for audio clips in Cloudflare R2. Direct data access without a job queue is also possible based on specific business logic. Job Recording System: Job results, including input audio URLs, audio length, processing time, output text URLs, etc., are stored in NoSQL databases like AWS DynamoDB. We aimed to keep the framework components fully managed and serverless to closely simulate the experience of using managed transcription services. Enhanced Node Implementation for High Performance and Throughput We have refined the node implementation to further enhance the system performance and throughput. Within each node in the GPU resource pool in SaladCloud, we follow best practices by utilizing two processes: One dedicated to GPU-based transcription inference and, Another focused on I/O- and CPU-bound tasks, such as downloading/uploading, pre-processing, and post-processing. https://preview.redd.it/apmgbb4sxelc1.png?width=1024&format=png&auto=webp&s=636b53f9da25068ed0748a438f7edeab4157fc06 Single-node test using JupyterLab Based on our single-node tests using JupyterLab, we found that the inference of Parakeet TDT 1.1B for audio files lasting longer than 15 minutes (or its batch inference for 15 1-minute audio files) requires approximately 7GB VRAM, excluding the process CUDA context. To optimize utilization and leverage massive and more cost-effective GPU types with 8GB of VRAM, we have decided to segment all long audio into 15-minute clips using the downloader threads. Given the high-speed performance of Parakeet TDT 1.1B and the presence of numerous small audio files in our datasets (averaging 12 seconds per audio), resulting in low network transmission efficiency, we have opted to use a minimum of 3 download threads for downloading and pre-processing audio files, based on our tests. Considering occasional downtimes from the Hugging Face website and the possibility of container instances being rebooted, we have pre-built the container image with the model, adding approximately 4GB in its size. This strategy eliminates dynamic downloads during the application’s runtime, contributing to cost reduction. Massive Transcription Tests for Parakeet TDT 1.1B on SaladCloud We created a container group with 100 replicas (2vCPU and 12 GB RAM with 20+ different GPU types) in SaladCloud. The group was operational for approximately 10 hours, from 10:00 pm to 8:00 am PST. We successfully transcribed a total of 5.2 million audio files. The cumulative length of these audio files amounted to 17,305 hours, with an average duration of 12 seconds. Inference performance comparison of Parakeet TDT 1.1B, Whisper Large V3 and Distil-Whisper Large V2 Whisper Large V3 Distil-Whisper Large V2 Parakeet TDT 1.1B Number of Transcribed Audio Files 2,364,838 3,846,559 5,209,130 Total Audio Length (s) 28,554,156 (8000 hours) 47,207,294 (13113 hours) 62,299,198 (17305 hours) Most Cost-Effective GPU Type for transcribing long audio files exceeding 30 seconds RTX 3060 196 hours per $ RTX 2080 Ti500 hours per $ RTX 3070 Ti794 hours per $ Most Cost-Effective GPU Type for transcribing short audio files lasting less than 30 seconds RTX 2080/3060/3060 Ti/3070 Ti 47 hours per $ RTX 2080/3060 Ti 90 hours per $ RTX 2080 Ti 228 hours per $ Best-Performing GPU Type for transcribing long audio files exceeding 30 seconds RTX 4080 Average real-time factor: 40,transcribing 40 seconds of audio per second RTX 4090 Average real-time factor: 93,transcribing 93 seconds of audio per second RTX 3080Ti Average real-time factor: 131,transcribing 131 seconds of audio per second Best-Performing GPU Type for transcribing short audio files lasting less than 30 seconds RTX 3080 Ti/4070 Ti/4090 Average real-time factor: 8,transcribing 8 seconds of audio per second RTX 4090 Average real-time factor: 14,transcribing 14 seconds of audio per second RTX 4070 Average real-time factor: 35,transcribing 35 seconds of audio per second You can read the full benchmark with detailed architecture & process here: https://blog.salad.com/parakeet-tdt-1-1b/ submitted by /u/SaladChefs [link] [comments]

- Can you get reprimanded for logging your hours for your job for working extra?by /u/JobIsAss (Data Science) on February 29, 2024 at 12:02 am
Got a mouthful from my manager about me working extra and that i didnt communicate that i have to work extra. Despite actually communicating this and sending the work at like 7-8 PM. For example: i get ask at 3-4 pm and they want numbers next day. So i started logging my hours as my teammates and I have to work extra just to meet the deadlines, maintain the existing jobs, and also do asks. What i got was that in my team we never had people log their hours despite the fact that everyone is overworked to the bone and my coworker literally said she wants to quit and hates it. so as a community how should i proceed as my manager wants to escalate and I dont understand what i did wrong. I literally logged the hours that i worked after communicating that i have to work extra. Am I being gaslighted? submitted by /u/JobIsAss [link] [comments]

- OMSCS or OMSA?by /u/kater543 (Data Science) on February 28, 2024 at 11:33 pm
online masters in CS or analytics? Or should I even go find a proper economics/metrics or stats program? Current DS , 7 YOE in a variety of topics spanning the DA/DE/DS-lite Spectrum wanting to upgrade my degree to look more competitive on resumes. Working for very very large company. Company is willing to pay for school. Which one should I do? Should I even think about MBAs if I’m aiming for front-line management? submitted by /u/kater543 [link] [comments]

- [R] EMO: Emote Portrait Alive - Generating Expressive Portrait Videos with Audio2Video Diffusion Model under Weak Conditionsby /u/Successful-Western27 (Machine Learning) on February 28, 2024 at 6:29 pm
Researchers have struggled to make AI-generated talking head videos that capture the nuance of human facial expressions and speech. Methods usually fail to replicate the fluidity and synchronization of real human mouths and faces. A new paper from Alibaba proposes EMO, an AI system that achieves unprecedented realism in synthesized talking head videos using a novel diffusion model approach. EMO generates videos directly from audio clips and portrait images, without 3D graphics or animation: Audio encoder analyzes tone, rhythm to generate motions Reference encoder preserves visual identity throughout video Temporal modules enable smooth frame transitions Facial mask focuses details on core face regions like mouth, eyes, etc Speed control layers stabilize pace of head movements Trained on a dataset of over 250 hours of talking head videos spanning 150 million frames, EMO learns the intricacies of human speech like enunciation, accents, and emotional affect. Quantitative evaluations show EMO substantially improves over previous state-of-the-art methods like Wav2Lip and DreamTalk on metrics including: Fréchet Inception Distance: individual frame quality Expression modeling: vividness of facial animations Lip sync: audio-visual alignment in mouth shapes Fréchet Video Distance: consistency of identity and expressions While limitations remain like slower generation and artifacts, EMO represents a major advance in replicating human facial dynamics directly from audio. As models scale up, AI-generated talking heads will become increasingly expressive and realistic. Paper here (github). Full summary here. submitted by /u/Successful-Western27 [link] [comments]

- If you are an X Analyst, what is your salary?by /u/PassiveIncome001 (Data Science) on February 28, 2024 at 6:11 pm
If you are an X Analyst, what is your salary? Curious as to what the market looks like right now. Glassdoor, Indeed, Payscale and Salary.com all have a degree of variance, and it also depends on what kind of analyst you are. I am: -Risk Analyst L1, Financial Services industry -Coming up to 2 YoE -Total current comp $66,500 a year -MCoL city, USA Personally, very curious to hear from any Data, Risk and Credit Risk analysts out there! submitted by /u/PassiveIncome001 [link] [comments]