Hi. I am not sure whether this is the right subreddit, but I want to write it here. I am a student and starting my research project. I just want to use any two pre-trained models that is existing like ClinicalBert, Biobert and to showcase the comparison. Hence, I don't want or need to do any fine tuning. Only want to implement into my system, run the chatbot and have a conversation with it. it would really be helpful if someone can help me on suggesting any good pre-trained medical chatbot usable models submitted by /u/Apstyles_17 [link] [comments]

I got a Reject (1), Borderline (3) and Accept (5), with confidence (3,3,4)! Quite stochastic I'd say!! But the Reject reviewer is not quite bad actually. submitted by /u/ElPelana [link] [comments]

Hi All, I’ve always been fascinated by the story of Deep Blue, IBM’s legendary chess computer, and its iconic matches against Garry Kasparov in the 90s. The intersection of chess and technology is a story that resonates deeply with me, and I wanted to create something that captures that magic for others. I’ve put together a google doc that collects and organizes some of the best long-form resources on the topic. It’s designed to serve as a comprehensive guide for anyone interested in exploring this moment in artificial intelligence history. If this way of exploring the Deep Blue story resonates with you, I’d love to hear your thoughts in the comments. Thank you for taking the time to read this post. Cheers! Link to the Google doc: https://docs.google.com/spreadsheets/d/1bZGQWR7zBPAyGVPlw6tu37FYF60w33m6gRsSlPNT5u0/edit?usp=sharing submitted by /u/bestovius [link] [comments]

What methods are ypu using for search algorithms? If you know good repositories / sources I'd be more than happy! Thanks submitted by /u/Emotional_Print_7068 [link] [comments]

The decisions seem to have appeared. What are your thoughts? submitted by /u/ApamNapat [link] [comments]

submitted by /u/moschles [link] [comments]

I am trying to build a image search app, like how Google's image search works. I am considering SigLip for this task (maybe Jina CLIP if this succeeds). It's a domain specific data, and I have the image, description and a label for classification. How do I fine-tune the model? What should be the approach here? submitted by /u/Extension-Tap-7488 [link] [comments]

Hi all, I'm working on a little personal project, quantifying what technologies are most asked for in Data Science JDs. Really I'm more using it to work on my Python chops. I'm hitting a slightly perplexing error and I think ChatGPT has taken me as far as it possibly can on this one. When I attempt to crawl my spider I get this error: TypeError: Cannot create a consistent method resolution order (MRO) for bases Injectable, Generic Previously the code was attempting to import Injectable from scrap_poet until I eventually inspected the package and saw that Injectable doesn't exist. So I attempted to avoid using that entirely and omitted all references to Injectable in my code. Yet I'm still getting this error. Any thoughts? Here's what the spider looks like: import scrapy import csv from scrapy_autoextract import request_raw class JobSpider(scrapy.Spider): name = "job_spider" custom_settings = { "DOWNLOADER_MIDDLEWARES": { "scrapy_autoextract.AutoExtractMiddleware": 543, }, } # Read URLs from links.csv and start requests def start_requests(self): with open("/adzuna_links.csv", "r") as file: reader = csv.reader(file) for row in reader: url = row[0] yield request_raw(url=url, page_type="jobposting", callback=self.parse) def parse(self, response): try: # Extract job details directly from the response JSON data returned by AutoExtract job_data = response.json().get("job_posting", {}) if job_data: yield { "title": job_data.get("title"), "description": job_data.get("description"), "company": job_data.get("hiringOrganization", {}).get("name"), "location": job_data.get("jobLocation", {}).get("address"), "datePosted": job_data.get("datePosted"), } else: self.logger.error(f"No job data extracted from {response.url}") except Exception as e: self.logger.error(f"Error parsing job data from {response.url}: {e}") submitted by /u/Tamalelulu [link] [comments]

Reviews should be out in less than 24 hours (Jan 23 '25 01:59 AM CST). Good luck everyone. submitted by /u/Some-Landscape-4763 [link] [comments]

This paper presents an approach for learning world knowledge directly from unlabeled video data through a self-supervised framework called VideoWorld. The core technical contribution is a multi-stage architecture that processes videos to extract both visual and temporal relationships without requiring manual annotation. Key technical points: - Uses contrastive learning between video segments to capture temporal dynamics - Implements cross-modal alignment between visual and motion features - Employs temporal consistency learning to understand event sequences - Introduces a hierarchical attention mechanism for long-range dependencies Results demonstrate improvement over existing methods: - 12% increase in video QA performance on HowTo100M - 15% better temporal relationship understanding on ActivityNet - 8% improvement in next-frame prediction tasks - Effective zero-shot transfer to unseen video domains I think this approach could significantly change how we train video understanding models. By removing the need for expensive manual labeling, we could potentially train on much larger and more diverse video datasets. I'm particularly interested in how this could improve robotic learning systems that need to understand physical interactions and causality. The results suggest good potential for real-world applications, though I think there are still important challenges around computational efficiency and handling of abstract concepts that need to be addressed. TLDR: New self-supervised framework learns world knowledge from unlabeled videos, showing strong improvements on video understanding tasks without requiring manual annotations. Full summary is here. Paper here. submitted by /u/Successful-Western27 [link] [comments]

I joined the thread to learn about Data Science. Something like 75 percent of the posts are peoples resumes and requests for career advice. I thought these were supposed to go into a weekly thread or something - I'm getting a warning about the weekly thread even as I'm posting this comment. Can anyone suggest alternative subs with more educational content? submitted by /u/meevis_kahuna [link] [comments]

submitted by /u/GiovannaDio [link] [comments]

Hey everyone, We built Jaqpot at our research lab because we needed a simple way to deploy machine learning models through APIs without dealing with the deployment headaches. Basically, you train your model (sklearn, pytorch geometric), and with a few lines of code it gets wrapped in an API you can call from your applications or visit on our dashboard. Example in Python: from jaqpotpy.models import SklearnModel from jaqpotpy import Jaqpot # Train your model as usual model = SklearnModel(dataset=dataset, model=LogisticRegression()) model.fit() # Deploy it jaqpot = Jaqpot() jaqpot.login() model.deploy_on_jaqpot( name="My Model", description="Simple classifier" ) Then you can call it from Python: prediction = jaqpot.predict_sync(model_id=model_id, dataset=input_data) or Java: Dataset prediction = jaqpotApiClient.predictSync(modelId, inputData); Some key points: Free to use (research project funded through our lab) Supports scikit-learn models and pytorch geometric Python and Java/Kotlin SDKs available Handles all the ONNX conversion and API setup Private or public model deployment options Includes specialized features for predictive modeling in chemistry and materials science We're making this available to researchers and developers while we have funding to run the infrastructure. We want to see how people actually use it and what features they need. Long term we'll probably need usage tiers to keep the servers running, but for now it's completely free and open-source. Check out our documentation or try it at app.jaqpot.org if you're interested. Happy to answer any questions! https://preview.redd.it/685xnheuljee1.png?width=1582&format=png&auto=webp&s=957a61576ca14a52cee33a1877331c61bb214905 https://preview.redd.it/tho37heuljee1.png?width=1289&format=png&auto=webp&s=4704e95d03e70c8f02c01d228a445c96f0f81c37 Edit: Since people are asking about the tech stack - it uses ONNX for model conversion and runs on AWS. Models are versioned and you can track usage through the web interface. submitted by /u/SignificanceUsual606 [link] [comments]

Hello. Could someone here who works with fraud models in production answer a few questions, please? - How would you describe a typical day of work? - You do mostly transactional models? - How often do you retrain your models? - how often do you retrain the same model? What is the average time window of your training base? And when do you know you need to train a new model? What changes between them? Audience, variables? (Because they are much more frequent than in credit). - how do you test the performance of a new model in production? - how much do you need to know about MLEng/architecture/infrastructure? For example, say I work as a data scientist for a fintech (I actually do). They already have fraud detection models running. When should I retrain the current model and when should I retrain a new one? I am exploring new opportunities in the area, but I find difficult to collect information on actual experiences of work. Thank you very much. submitted by /u/Lock-and-load [link] [comments]

Hi community, I’m primarily DS with quite a number of years in DS and DE. I’ve mostly worked with on-site infrastructure. My stack is currently Python, Julia, R… and my field of interest is numerical computing, OpenMP, MPI and GPU parallel computing (down the line) I’m curious as to how best to align my current work with high level languages with my interest in lower level languages. If I were deciding based on work alone, Fortran will be the best language for me to learn as there’s a lot of legacy code we’d have to port in the next years. However, I’d like to develop in a language that’ll complement the skill set of a DS. My current view is Julia, C and Fortran. However, I’m not completely sure of how useful these are outside of my very-specific field. Are there any other DS that have gone through this? How did you decide? What would you recommend? What factors did you consider. submitted by /u/NoteClassic [link] [comments]

Great talk by Fei-Fei Li on Visual Intelligence and what the future holds for AI. Wanted to share it here in case anyone wants to check it out on their website. submitted by /u/hiskuu [link] [comments]

Youtube video Timestamps 00:01:38 : 3 character context ( 272727 = 19683 ) . Too much possibilities. \ Introduce Multi-Layer Perception model. 00:02:09 - 00:09:00 : 00-02-03-bengio-2003-paper.md submitted by /u/yogimankk [link] [comments]

Hello everybody! My name is Skye and I am the first author of this work! This paper demonstrates that forecasting and event prediction can be enhanced by taking inspiration from the brain, specifically predictive coding theory. I am posting the abstract, code, and arXiv link for anybody curious! Please feel free to leave any comments below, as this is my first full-length paper and I would appreciate any feedback! Abstract: Accurate time-series forecasting is crucial in various scientific and industrial domains, yet deep learning models often struggle to capture long-term dependencies and adapt to data distribution drifts over time. We introduce Future-Guided Learning, an approach that enhances time-series event forecasting through a dynamic feedback mechanism inspired by predictive coding. Our method involves two models: a detection model that analyzes future data to identify critical events and a forecasting model that predicts these events based on current data. When discrepancies occur between the forecasting and detection models, a more significant update is applied to the forecasting model, effectively minimizing surprise and adapting to shifts in the data distribution by aligning its predictions with actual future outcomes. This feedback loop allows the forecasting model to dynamically adjust its parameters, focusing on persistent features despite changes in the data. We validate our approach on a variety of tasks, demonstrating a 44.8% increase in AUC-ROC for seizure prediction using EEG data, and a 48.7% reduction in MSE for forecasting in nonlinear dynamical systems. By incorporating a predictive feedback mechanism adaptable to data drift, Future-Guided Learning advances how deep learning is applied to time-series forecasting. Our code is publicly available at: https://github.com/SkyeGunasekaran/FutureGuidedLearning. arXiv: https://arxiv.org/pdf/2410.15217 submitted by /u/Skye7821 [link] [comments]

Scaling language models to handle longer input sequences typically necessitates large key-value (KV) caches, resulting in substantial memory overhead during inference. In this paper, we propose Tensor Product Attention (TPA), a novel attention mechanism that uses tensor decompositions to represent queries, keys, and values compactly, significantly shrinking KV cache size at inference time. By factorizing these representations into contextual low-rank components (contextual factorization) and seamlessly integrating with RoPE, TPA achieves improved model quality alongside memory efficiency. Based on TPA, we introduce the Tensor ProducT ATTenTion Transformer (T6), a new model architecture for sequence modeling. Through extensive empirical evaluation of language modeling tasks, we demonstrate that T6 exceeds the performance of standard Transformer baselines including MHA, MQA, GQA, and MLA across various metrics, including perplexity and a range of renowned evaluation benchmarks. Notably, TPAs memory efficiency enables the processing of significantly longer sequences under fixed resource constraints, addressing a critical scalability challenge in modern language models. The code is available submitted by /u/RajonRondoIsTurtle [link] [comments]

article single-head attention multi-head attention cross-attention explanations included. submitted by /u/yogimankk [link] [comments]