Word embeddings like Word2Vec and GloVe have revolutionized natural language processing, offering compact and dense representations of word meanings. However, these embeddings typically represent words as real-valued vectors, potentially limiting their ability to capture complex semantic relationships. In this proposal, we explore an alternative approach: representing word vectors as complex numbers. We propose converting Word2Vec or GloVe vectors into complex numbers, where the real part captures magnitude and the imaginary part encodes additional semantic information. For instance, consider the word vector Vecword=[0.2,−0.3,0.5,0.1,−0.2]. We can convert this vector into a complex number zz as follows: z=Vecword[0]+i×Vecword[1] Here, ii is the imaginary unit. The real part of the complex number represents the magnitude of the word's meaning (0.2), while the imaginary part (-0.3i) captures additional semantic nuances. This approach offers several potential advantages: Enhanced Semantic Representation: Complex numbers can capture both magnitude and phase, allowing for richer semantic representations compared to real-valued vectors. Contextual Information: By encoding semantic information in the imaginary part, we can capture contextual nuances that may be missed by traditional embeddings. Compatibility: Complex number representations can be seamlessly integrated into existing models and frameworks, offering a straightforward extension to current NLP pipelines. Exploring complex number representations for word vectors presents an exciting avenue for enhancing semantic understanding in natural language processing tasks. By leveraging the unique properties of complex numbers, we can potentially unlock deeper insights into the structure and meaning of language. This proposal aims to spark further research and experimentation in this promising direction. Join us as we delve into the fascinating world of complex semantics! submitted by /u/_mayuk [link] [comments]

Hi, does anyone know of a French L2 GEC dataset (that was published at a conference)? submitted by /u/R-e-v-e-r-i-e- [link] [comments]

My primary expertise is audio processing, but i believe this task happens in other domains too: running a model on chunks of infinitely long input. while for some architectures it is straightforward, it can get tedious for convolutional nets. I put together a comprehensive tutorial how to build a streaming ML applications: https://balacoon.com/blog/streaming_inference/. would be curious to learn wether its a common problem and how do people usually deal with it. because generally resources on the topic are surprisingly scarce. submitted by /u/clementruhm [link] [comments]

​ https://preview.redd.it/jpiyt4b9yhwc1.png?width=1165&format=png&auto=webp&s=95d80f8f9c9241d722717ad25215be4077d541ca Based on the MSE looks good right? But why is my R^2 starting off so negative and approaching 0? Could it be a bug in how i am calculating it? This happened after i min maxed the labels before training. This is an LSTM that is predicting runs scored for baseball games. submitted by /u/Cloverdover1 [link] [comments]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]