What are some ways to increase precision or recall in machine learning?

What are some ways to increase precision or recall in machine learning?

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What are some ways to increase precision or recall in machine learning?

What are some ways to Boost Precision and Recall in Machine Learning?

Sensitivity vs Specificity?


In machine learning, recall is the ability of the model to find all relevant instances in the data while precision is the ability of the model to correctly identify only the relevant instances. A high recall means that most relevant results are returned while a high precision means that most of the returned results are relevant. Ideally, you want a model with both high recall and high precision but often there is a trade-off between the two. In this blog post, we will explore some ways to increase recall or precision in machine learning.

What are some ways to increase precision or recall in machine learning?
What are some ways to increase precision or recall in machine learning?


There are two main ways to increase recall:

by increasing the number of false positives or by decreasing the number of false negatives. To increase the number of false positives, you can lower your threshold for what constitutes a positive prediction. For example, if you are trying to predict whether or not an email is spam, you might lower the threshold for what constitutes spam so that more emails are classified as spam. This will result in more false positives (emails that are not actually spam being classified as spam) but will also increase recall (more actual spam emails being classified as spam).

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To decrease the number of false negatives,

you can increase your threshold for what constitutes a positive prediction. For example, going back to the spam email prediction example, you might raise the threshold for what constitutes spam so that fewer emails are classified as spam. This will result in fewer false negatives (actual spam emails not being classified as spam) but will also decrease recall (fewer actual spam emails being classified as spam).

What are some ways to increase precision or recall in machine learning?

There are two main ways to increase precision:

by increasing the number of true positives or by decreasing the number of true negatives. To increase the number of true positives, you can raise your threshold for what constitutes a positive prediction. For example, using the spam email prediction example again, you might raise the threshold for what constitutes spam so that fewer emails are classified as spam. This will result in more true positives (emails that are actually spam being classified as spam) but will also decrease precision (more non-spam emails being classified as spam).

To decrease the number of true negatives,

you can lower your threshold for what constitutes a positive prediction. For example, going back to the spam email prediction example once more, you might lower the threshold for what constitutes spam so that more emails are classified as spam. This will result in fewer true negatives (emails that are not actually spam not being classified as spam) but will also decrease precision (more non-spam emails being classified as spam).


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What are some ways to increase precision or recall in machine learning?

To summarize,

there are a few ways to increase precision or recall in machine learning. One way is to use a different evaluation metric. For example, if you are trying to maximize precision, you can use the F1 score, which is a combination of precision and recall. Another way to increase precision or recall is to adjust the threshold for classification. This can be done by changing the decision boundary or by using a different algorithm altogether.

What are some ways to increase precision or recall in machine learning?

Sensitivity vs Specificity

In machine learning, sensitivity and specificity are two measures of the performance of a model. Sensitivity is the proportion of true positives that are correctly predicted by the model, while specificity is the proportion of true negatives that are correctly predicted by the model.

Google Colab For Machine Learning

State of the Google Colab for ML (October 2022)

Google introduced computing units, which you can purchase just like any other cloud computing unit you can from AWS or Azure etc. With Pro you get 100, and with Pro+ you get 500 computing units. GPU, TPU and option of High-RAM effects how much computing unit you use hourly. If you don’t have any computing units, you can’t use “Premium” tier gpus (A100, V100) and even P100 is non-viable.

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Google Colab Pro+ comes with Premium tier GPU option, meanwhile in Pro if you have computing units you can randomly connect to P100 or T4. After you use all of your computing units, you can buy more or you can use T4 GPU for the half or most of the time (there can be a lot of times in the day that you can’t even use a T4 or any kinds of GPU). In free tier, offered gpus are most of the time K80 and P4, which performs similar to a 750ti (entry level gpu from 2014) with more VRAM.

For your consideration, T4 uses around 2, and A100 uses around 15 computing units hourly.
Based on the current knowledge, computing units costs for GPUs tend to fluctuate based on some unknown factor.

Considering those:

  1. For hobbyists and (under)graduate school duties, it will be better to use your own gpu if you have something with more than 4 gigs of VRAM and better than 750ti, or atleast purchase google pro to reach T4 even if you have no computing units remaining.
  2. For small research companies, and non-trivial research at universities, and probably for most of the people Colab now probably is not a good option.
  3. Colab Pro+ can be considered if you want Pro but you don’t sit in front of your computer, since it disconnects after 90 minutes of inactivity in your computer. But this can be overcomed with some scripts to some extend. So for most of the time Colab Pro+ is not a good option.

If you have anything more to say, please let me know so I can edit this post with them. Thanks!

Conclusion:


In machine learning, precision and recall trade off against each other; increasing one often decreases the other. There is no single silver bullet solution for increasing either precision or recall; it depends on your specific use case which one is more important and which methods will work best for boosting whichever metric you choose. In this blog post, we explored some methods for increasing either precision or recall; hopefully this gives you a starting point for improving your own models!

 

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Machine Learning and Data Science Breaking News 2022 – 2023

  • [D] The only DS in the team
    by /u/Dramatic_Chance9577 (Machine Learning) on April 24, 2024 at 7:25 pm

    I am currently pursuing a Master's degree in Data Science and am required to complete a practicum project in my final semester. I've been exploring internship opportunities and was recently approached by a startup that is interested in developing an AI teaching model using an existing Large Language Model. However, I would be the only data scientist at the company, which I find daunting given my limited experience. The project needs to culminate in a tangible achievement for my practicum, and I'm concerned that might not be feasible under these circumstances. I'm seeking advice on whether to proceed with this opportunity, especially given the absence of a dedicated data science team. What do you think? Should I take on this challenge? submitted by /u/Dramatic_Chance9577 [link] [comments]

  • [Discussion] Preparing for new job - looking for resources
    by /u/b0taki (Machine Learning) on April 24, 2024 at 6:49 pm

    Hello everyone! In a few weeks, I'll be starting a new role as a "Lead, Data Engineer" where I'll be responsible for a team that is building an LLM-based application/pipeline for automating processing of customer claims. My role will be a 50/50 split around managerial and technical contributions. I've been working in software engineering for more than 10 years, half of which as an IC engineering and the latest half as a lead / manager, in both big (FAANG) and small companies. My extensive experience is mostly around large-scale distributed applications and web-platforms; nothing much yet on data-processing-oriented or machine-learning-related projects. Still, I feel I have lots of relevant expertise and knowledge that is transferable and I am confident that I can successfully ramp up 🙂 I know the team I'm joining is already great at what they do. As their manager, I don't expect to be better and, naturally, I will be leveraging their expertise to deliver a good product. What I'm looking for is decreasing my entry/ramp-up barriers and increasing my odds of being a good manager and tech contributor. With a start date just a few weeks away, I want to make the most of it to prepare as best as possible and learn as much as possible. I’m looking for advice and information on how to rapidly learn & improve. Architecture & Production Best Practices: What are resources (books, articles, etc.) on best practices around designing/architecting and implementing data processing pipelines and LLM-based applications? How about how to leverage existing data for evaluating performance of LLM-based solutions? Quick Learning Resources: What are the top books, or articles that can provide an advanced crash course in LLMs? I already understand (somewhat more than just) the basics and have used OpenAI's API and playground. Still, any specific recommendations that would be useful in practical/production environment would be great! (e.g. I imagine this survey may be good?) Key Technologies: I have expertise in Java, Python, and several database technologies. Which tools or platforms should I prioritize to enhance my understanding of integrating LLMs into data pipelines? Strategic Preparations: What are some strategic moves I can make right now that would yield significant benefits once I start? How can I leverage my management and technical skills to make an immediate impact? Thank you! submitted by /u/b0taki [link] [comments]

  • Recall Score Increase [D]
    by /u/Legal_Hearing555 (Machine Learning) on April 24, 2024 at 5:38 pm

    Hello Everyone, I am trying to do a small fraud detection project and i have so imbalanced dataset. I used randomundersampling because minority class is pretty small and i also tried smote or combining with smote best recall score i got, was with only randomundersampling(0.95). I thought GridsearchCV to increase it but instead of increasing, it is decreasing although i tried to make it to focus on recall score. Why this is happening? submitted by /u/Legal_Hearing555 [link] [comments]

  • [D] Preserving spatial distribution of data during data splitting
    by /u/dr_greg_mouse (Machine Learning) on April 24, 2024 at 5:14 pm

    Hello, I am trying to model nitrate concentrations in the streams in Bavaria in Germany using Random Forest model. I am using Python and primarily sklearn for the same. I have data from 490 water quality stations. I am following the methodology in the paper from LongzhuQ.Shen et al which can be found here: https://www.nature.com/articles/s41597-020-0478-7 I want to split my dataset into training and testing set such that the spatial distribution of data in both sets is identical. The idea is that if data splitting ignores the spatial distribution, there is a risk that the training set might end up with a concentration of points from densely populated areas, leaving out sparser areas. This can skew the model's learning process, making it less accurate or generalizable across the entire area of interest. sklearn train_test_split just randomly divides the data into training and testing sets and it does not consider the spatial patterns in the data. The paper I mentioned above follows this methodology: "We split the full dataset into two sub-datasets, training and testing respectively. To consider the heterogeneity of the spatial distribution of the gauge stations, we employed the spatial density estimation technique in the data splitting step by building a density surface using Gaussian kernels with a bandwidth of 50 km (using v.kernel available in GRASS GIS33) for each species and season. The pixel values of the resultant density surface were used as weighting factors to split the data into training and testing subsets that possess identical spatial distributions." I want to follow the same methodology but instead of using grass GIS, I am just building the density surface myself in Python. I have also extracted the probability density values and the weights for the stations. (attached figure) Now the only problem I am facing is how do I use these weights to split the data into training and testing sets? I checked there is no keyword in the sklearn train_test_split function that can consider the weights. I also went back and forth with chat GPT 4 but it is also not able to give me a clear answer. Neither did I find anything concrete on the internet about this. Maybe I am missing something. Is there any other function I can use to do this? Or will I have to write my own algorithm to do the splitting? In case of the latter, can you please suggest me the approach so I can code it myself? In the attached figure you can see the location of the stations and the probability density surface generated using the kernel density estimation method (using Gaussian kernels). Also attaching a screenshot of my dataframe to give you some idea of the data structure. (all columns after longitude ('lon') column are used as features. the NO3 column is used as the target variable.) I will be grateful for any answers. ​ Probability density surface generated using the kernel density estimation method with gaussian kernels. ​ the dataset I am using to model the nitrate concentrations submitted by /u/dr_greg_mouse [link] [comments]

  • [N] Snowflake releases open (Apache 2.0) 128x3B MoE model
    by /u/topcodemangler (Machine Learning) on April 24, 2024 at 4:45 pm

    Links: ​ https://www.snowflake.com/blog/arctic-open-efficient-foundation-language-models-snowflake/ ​ https://replicate.com/snowflake/snowflake-arctic-instruct submitted by /u/topcodemangler [link] [comments]

  • [D] Why would such a simple sentence break an LLM?
    by /u/michael-relleum (Machine Learning) on April 24, 2024 at 3:59 pm

    This is a prompt I entered into MS Copilot (GPT4 Turbo). It's in german but it just means "Would there be any disadvantages if I took the full bath first?"), so this can't be another SolidGoldMagikarp or similar, because the words clearly were in both tokenizer and training vocab. Why would such a simple sentence cause this? Any guesses? (also tried with Claude Opus and LLama 3 70b, which worked fine) ​ https://preview.redd.it/9x6mva7b6gwc1.png?width=1129&format=png&auto=webp&s=bb6ac52d1c52d981161e8a864c5d1dd3794ca392 submitted by /u/michael-relleum [link] [comments]

  • [R] Speaker diarization
    by /u/anuragrawall (Machine Learning) on April 24, 2024 at 3:01 pm

    Hi All, I am working on a project where I want to create speaker-aware transcripts from audios/videos, preferably using open-source solutions. I have tried so many approaches but nothing seems to work good enough out of the box. I have tried: ​ whisperX: https://github.com/m-bain/whisperX (uses pyannote) whisper-diarization: https://github.com/MahmoudAshraf97/whisper-diarization (uses Nemo) AWS Transcribe AssemblyAI API Picovoice API I'll need to dig deeper and understand what's causing the incorrect diarization but I am looking for suggestions to improve speaker diarization. Please reach out if you have worked in this area and have had any success. Thanks! submitted by /u/anuragrawall [link] [comments]

  • [R] I made an app to predict ICML paper acceptance from reviews
    by /u/Lavishness-Mission (Machine Learning) on April 24, 2024 at 12:23 pm

    https://www.norange.io/projects/paper_scorer/ A couple of years ago, u/programmerChilli analyzed ICLR 2019 reviews data and trained a model that rather accurately predicted acceptance results for NeurIPS. I've decided to continue this analysis and trained a model (total ~6000 parameters) on newer NeurIPS reviews, which has twice as many reviews compared to ICLR 2019. Additionally, review scores system for NeurIPS has changed since 2019, and here is what I've learned: 1) Both conferences consistently reject nearly all submissions scoring <5 and accept those scoring >6. The most common score among accepted papers is 6. An average rating around 5.3 typically results in decisions that could go either way for both ICML and NeurIPS, suggesting that ~5.3 might be considered a soft threshold for acceptance. 2) Confidence scores are less impactful for borderline ratings such as 4 (borderline reject), 5 (borderline accept), and 6 (weak accept), but they can significantly affect the outcome for stronger reject or accept cases. For instance, with ratings of [3, 5, 6] and confidences of [*, 4, 4], changing the "Reject" confidence from 5 to 1 shifts the probabilities from 26.2% - 31.3% - 52.4% - 54.5% - 60.4%, indicating that lower confidence in this case increases your chances. Conversely, for ratings [3, 5, 7] with confidences [4, 4, 4], the acceptance probability is 31.3%, but it drops to 28.1% when the confidence changes to [4, 4, 5]. Although it might seem counterintuitive, a confidence score of 5 actually decreases your chances. One possible explanation is that many low-quality reviews rated 5 are often discounted by the Area Chairs (ACs). Hope this will be useful, and thanks to u/programmerChilli for the inspiration! I also discussed this topic in a series of tweets. submitted by /u/Lavishness-Mission [link] [comments]

  • [R] SpaceByte: Towards Deleting Tokenization from Large Language Modeling - Rice University 2024 - Practically the same performance as subword tokenizers without their many downsides!
    by /u/Singularian2501 (Machine Learning) on April 24, 2024 at 11:42 am

    Paper: https://arxiv.org/abs/2404.14408 Github: https://github.com/kjslag/spacebyte Abstract: Tokenization is widely used in large language models because it significantly improves performance. However, tokenization imposes several disadvantages, such as performance biases, increased adversarial vulnerability, decreased character-level modeling performance, and increased modeling complexity. To address these disadvantages without sacrificing performance, we propose SpaceByte, a novel byte-level decoder architecture that closes the performance gap between byte-level and subword autoregressive language modeling. SpaceByte consists of a byte-level Transformer model, but with extra larger transformer blocks inserted in the middle of the layers. We find that performance is significantly improved by applying these larger blocks only after certain bytes, such as space characters, which typically denote word boundaries. Our experiments show that for a fixed training and inference compute budget, SpaceByte outperforms other byte-level architectures and roughly matches the performance of tokenized Transformer architectures.Paper: https://arxiv.org/abs/2404.14408Github: https://github.com/kjslag/spacebyteAbstract:Tokenization is widely used in large language models because it significantly improves performance. However, tokenization imposes several disadvantages, such as performance biases, increased adversarial vulnerability, decreased character-level modeling performance, and increased modeling complexity. To address these disadvantages without sacrificing performance, we propose SpaceByte, a novel byte-level decoder architecture that closes the performance gap between byte-level and subword autoregressive language modeling. SpaceByte consists of a byte-level Transformer model, but with extra larger transformer blocks inserted in the middle of the layers. We find that performance is significantly improved by applying these larger blocks only after certain bytes, such as space characters, which typically denote word boundaries. Our experiments show that for a fixed training and inference compute budget, SpaceByte outperforms other byte-level architectures and roughly matches the performance of tokenized Transformer architectures. https://preview.redd.it/v1xo6g1gzewc1.jpg?width=1507&format=pjpg&auto=webp&s=f9d415307b60639fa67e8a54c8769fa5a6c10f04 https://preview.redd.it/edvqos1gzewc1.jpg?width=1654&format=pjpg&auto=webp&s=f91c8727017e1a1bc7b80bb77a8627ff99182607 https://preview.redd.it/fe6z6i1gzewc1.jpg?width=1181&format=pjpg&auto=webp&s=24d955f30b8ca3eaa7c527f3f40545ed493f789c submitted by /u/Singularian2501 [link] [comments]

  • [D] Keeping track of models and their associated metadata.
    by /u/ClearlyCylindrical (Machine Learning) on April 24, 2024 at 10:20 am

    I am starting to accumulate a large number of models for a project I am working on, many of these models are old which I am keeping for archival sake, and many are fine tuned from other models. I am wondering if there is an industry standard way of dealing with this, in particular I am looking for the following: Information about parameters used to train the model Datasets used to train the model Other metadata about the model (i.e. what objects an object detection model trained for) Model performance Model lineage (What model was it fine tuned from) Model progression (Is this model a direct upgrade from some other model, such as being fine tuned from the same model but using better hyper parameters) Model source (Not sure about this, but I'm thinking some way of linking the model to the python script which was used to train it. Not crucial but something like this would be nice) Are there any tools of services which could help be achieve some of this functionality? Also, if this is not the sub for this question could I get some pointers in the correct direction. Thanks! ​ submitted by /u/ClearlyCylindrical [link] [comments]

  • [D] Deploy the fine-tuned Mistral 7B model using the Hugging Face library
    by /u/Future-Outcome3167 (Machine Learning) on April 24, 2024 at 9:31 am

    I followed the tutorial provided at https://www.datacamp.com/tutorial/mistral-7b-tutorial and now seek methods to deploy the model for faster inference using Hugging Face and Gradio. Could anyone please share a guide notebook or article for reference? Any help would be appreciated. submitted by /u/Future-Outcome3167 [link] [comments]

  • [D] Transkribus vs Tesseract for Handwritten Text Recognition (HTR)
    by /u/Pretty_Instance4483 (Machine Learning) on April 24, 2024 at 6:15 am

    I am looking for a HTR tool with the best accuracy and preferably not pricy (obviously). From my research, it seems that Transkribus was the most mentioned platform with good reviews. As I would need to convert images to text regularly I would need to pay the subscription. So I am wondering if I could use the Tesseract and/or TensorFlow Python library to achieve the same result for free. Would using Tesseract/TensorFlow be less accurate rather than using Transkribus? I learned only the basics of Machine Learning (TensorFlow, scikit-learn, keras), so I might have not enough knowledge to see the difference between the two solutions. Or is training Tesseract/TensorFlow would be challenging? submitted by /u/Pretty_Instance4483 [link] [comments]

  • [D] How researcher think of inductive bias when thinking of creating new/improving foundational models?
    by /u/binny_sarita (Machine Learning) on April 24, 2024 at 2:36 am

    I am undergradute student learning machine learning. What I got to know while reading few papers that we try to reduce search space by imposing inductive bias in machine learning models. And the success in creating useful models comes when inductive bias matches with the underlying data. In heriarchical models like NVAE how they instilled inductive bias by specifing the way data gets computed? (I thinks it's called algorithmic bias, not sure though) But how people think such inductive bias will be helpful, what is step by step procedure they go through to insist such inductive bias. I took a lot of class in machine learning and statistics but didn't got any lectures explaing such stuff. Did I missed any course/lecture? Please provide my with papers/lectures/talks related to it if possible Thankyou submitted by /u/binny_sarita [link] [comments]

  • [R] Generalized Contrastive Learning for Multi-Modal Retrieval and Ranking
    by /u/Jesse_marqo (Machine Learning) on April 23, 2024 at 11:07 pm

    Generalization of the popular training method of CLIP to be better suited for search and recommendations. Paper: https://arxiv.org/pdf/2404.08535.pdf Github: https://github.com/marqo-ai/GCL Generalises CLIP: Use any number of text and/or images to represent documents. Better text understanding by having both inter- and intra-modal losses. Can encode rank/importance/relevance, a.k.a “rank-tune”. Works with pretrained, text, CLIP models. Can learn uni- or multi-vector representations for documents. Works with binary and Matryoshka methods. Open source 10M row multi-modal dataset with 100k queries and ~5M products. Why? The prevailing methods for training embedding models are largely disconnected from the end use-case (like search), the vector database, the requirements of users, and a lack of representative datasets for development and evaluation, particularly when multiple modalities and ranking is involved. Limitations of current embedding models for vector search Although vector search is very powerful and enables searching across just about any data, the current methods have some limitations. The prevailing methods for training embedding models are largely disconnected from the end use-case (like search), the vector database, and the requirements of users. This means that a lot of the potential of vector search is being unmet. Some of the current challenges are described below. Restricted to using a single piece of information to represent a document Current models encode and represent one piece of information with one vector. The reality is that often there are multiple pieces of pertinent information for a document that may span multiple modalities. For example, in product search there may be a title, description, reviews, and multiple images, each with its own caption. GCL generalises embedding model training to use as many pieces of information as is desired. No notion of rank when dealing with degenerate queries When there are degenerate queries - multiple results that satisfy some criteria of relevance - the ordering of the results is only ever learned indirectly from the many binary relationships. In reality, the ordering of results matters, even for first stage retrieval. GCL allows for the magnitude of query-document specific relevance to be encoded in the embeddings and improves ranking of candidate documents. Poor text understanding when using CLIP like methods For multi-modal models like CLIP, these are trained to only work from image to text (and vice versa). The text-text understanding is not as good as text only models due to the text-text relationships being learned indirectly through images. For many applications, having both inter- and intra-modality understanding is required. GCL allows for any combination of inter- and intra-modal understanding by directly optimizing for this. Lack of representative datasets to develop methods for vector search In developing GCL, it became apparent there was a disconnect with publicly available datasets for embedding model training and evaluation for real-world use cases. Existing benchmarks are typically text only or inter-modal only and focus on the 1-1 query-result paradigm. Additionally, existing datasets have limited notions of relevance, with the majority encoding it as a binary relationship while several use (up-to) a handful of discrete categorizations often on the test set only. This differs from a typical real-world use cases where relevance can be both hard binary relationships or come from continuous variables. To help with this we compiled a dataset of 10M (ranked) product-query pairs, across ~100k queries, nearly 5M products, and four evaluation splits (available here). ​ submitted by /u/Jesse_marqo [link] [comments]

  • [D] Practical uses of AI inside companies
    by /u/CJSF (Machine Learning) on April 23, 2024 at 10:25 pm

    How are people using AI inside companies (startups -> FAANG) to improve operations and processes? There is so much talk about leveraging LLM’s and GenAI but I’m struggling to find real concrete examples that are successful, beyond what comes up in a google search. The following areas come to mind first but this list isn’t exhaustive of course: Design (and handoff) Engineering Customer Support Sales Documentation Marketing What’s worked or shown promise? What hasn’t worked? submitted by /u/CJSF [link] [comments]

  • Meta does everything OpenAI should be [D]
    by /u/ReputationMindless32 (Machine Learning) on April 23, 2024 at 10:03 pm

    I'm surprised (or maybe not) to say this, but Meta (or Facebook) democratises AI/ML much more than OpenAI, which was originally founded and primarily funded for this purpose. OpenAI has largely become a commercial project for profit only. Although as far as Llama models go, they don't yet reach GPT4 capabilities for me, but I believe it's only a matter of time. What do you guys think about this? submitted by /u/ReputationMindless32 [link] [comments]

  • [D] Speech to Text Word Level Timestamps Accuracy Issue
    by /u/Mindless-Ordinary485 (Machine Learning) on April 23, 2024 at 7:18 pm

    I've had a lot of success with Whisper when it comes to transcriptions, but word level timestamps seems to be slightly inaccurate. From my understanding ("Whisper cannot provide reliable word timestamps, because the END-TO-END models like Transformer using cross-entropy training criterion are not designed for reliably estimating word timestamps." https://www.youtube.com/watch?v=H576iCWt1Co&t=192s) For my use case, I need precise word level timestamps, because I'm doing audio insertion after specific words. This becomes problematic when I do an insertion and the back part of a word ends up on the other side. Example: Given an original audio file with speech that has been transcribed, If I want to insert a clip at the end of the word "France", and according to the timestamp, the word "France" starts at 19.26 and ends at 19.85, I will insert the clip at 19.85. However, if the actual end of France is at 19.92, then when I insert the laugher at 19.85, I will here the remaining "France", likely "ce" (0.07), at the end. I'm curious if anyone has been posed with a similar problem and what they did to get around this? I've experimented with a few open source variations of whisper, but still running into that issue. submitted by /u/Mindless-Ordinary485 [link] [comments]

  • [R] Wu's Method can Boost Symbolic AI to Rival Silver Medalists and AlphaGeometry to Outperform Gold Medalists at IMO Geometry
    by /u/SeawaterFlows (Machine Learning) on April 23, 2024 at 7:11 pm

    Paper: https://arxiv.org/abs/2404.06405 Code: https://huggingface.co/datasets/bethgelab/simplegeometry Abstract: Proving geometric theorems constitutes a hallmark of visual reasoning combining both intuitive and logical skills. Therefore, automated theorem proving of Olympiad-level geometry problems is considered a notable milestone in human-level automated reasoning. The introduction of AlphaGeometry, a neuro-symbolic model trained with 100 million synthetic samples, marked a major breakthrough. It solved 25 of 30 International Mathematical Olympiad (IMO) problems whereas the reported baseline based on Wu's method solved only ten. In this note, we revisit the IMO-AG-30 Challenge introduced with AlphaGeometry, and find that Wu's method is surprisingly strong. Wu's method alone can solve 15 problems, and some of them are not solved by any of the other methods. This leads to two key findings: (i) Combining Wu's method with the classic synthetic methods of deductive databases and angle, ratio, and distance chasing solves 21 out of 30 methods by just using a CPU-only laptop with a time limit of 5 minutes per problem. Essentially, this classic method solves just 4 problems less than AlphaGeometry and establishes the first fully symbolic baseline strong enough to rival the performance of an IMO silver medalist. (ii) Wu's method even solves 2 of the 5 problems that AlphaGeometry failed to solve. Thus, by combining AlphaGeometry with Wu's method we set a new state-of-the-art for automated theorem proving on IMO-AG-30, solving 27 out of 30 problems, the first AI method which outperforms an IMO gold medalist. submitted by /u/SeawaterFlows [link] [comments]

  • [D] Method to generate shapely contributions without model object
    by /u/ozymandias_514 (Machine Learning) on April 23, 2024 at 6:08 pm

    Is there a method to generate the approximations of Shapely values (or something similar) for a data without using model object. Essentialy I input features and model predictions on benchmark data, and the same for test data, and output is contributions for each feature on test data submitted by /u/ozymandias_514 [link] [comments]

  • [N] Phi-3-mini released on HuggingFace
    by /u/topcodemangler (Machine Learning) on April 23, 2024 at 3:26 pm

    https://huggingface.co/microsoft/Phi-3-mini-128k-instruct The numbers in the technical report look really great, I guess need to be verified by 3rd parties. submitted by /u/topcodemangler [link] [comments]

Top 100 Data Science and Data Analytics and Data Engineering Interview Questions and Answers

What are some good datasets for Data Science and Machine Learning?

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List of Freely available programming books - What is the single most influential book every Programmers should read



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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

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

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

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


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