I have some data - twitter description of an event in text and the event itself. If I have 100000 tweets in column X and a category in Y - e.g sporting event review, movie review, news, etc what is the best algorithm to match them. Should I make the description a bag of words and depending on the word frequency I can train a ML model (random forest,svm,etc.) or can the algorithm take into account the order. submitted by /u/AnyJello605 [link] [comments]

Hello, The Mistral 7b AI model beats LLaMA 2 7b on all benchmarks and LLaMA 2 13b in many benchmarks. It is actually even on par with the LLaMA 1 34b model. So I made a quick video about how to deploy this model on an A10 GPU on an AWS EC2 g5.4xlarge instance: https://nlpcloud.com/deploy-mistral-7b-on-a10-gpu-on-aws.html I hope it will be useful. If you have recommendations about how to improve this video please don't hesitate to let me know, that will be very much appreciated! Julien submitted by /u/juliensalinas [link] [comments]

I am reading PaLI: A Jointly-Scaled Multilingual Language-Image Model . In their table 2 (page 6), it's reported that Thapliyal et al. (2022) (0.8B) model got 57.6 of CIDEr on XM 3600 for English. Thapliyal et al. (2022) is Crossmodal-3600: A Massively Multilingual Multimodal Evaluation Dataset. However in this paper, the CIDEr values are reported less than 1. For example, the largest model got 0.584 of CIDEr on XM 3600 for English. Could someone explain to me why those values have great differences? submitted by /u/KingsmanVince [link] [comments]

Hey everyone. I am very passionate about getting in ML research and was wondering what the learning pathway was, particularly with regards to the theoretical Math and Statistics involved. For context: I am a second year undergraduate. By the end of this year, I will have taken and finished A Multivariable Calculus with Proofs course, so that is my current starting point. I have been working with ML for the last 3 years and am proficient in Python and frameworks like PyTorch. I have also made my own implementation of several research papers (LSTMs, GRUs, Transformers, ELMo, BERT, GPT, as well as a few computer vision papers). I have a good general intuition of how deep learning works, but I want to formalise this knowledge with the adequate mathematical background so that I can eventually pursue a career in research. I understand that I have plenty of time until I reach there, and I am willing to dedicate it to grinding out the math and statistical knowledge required. I have done my research on this sub and other forums, and here are a few resources that stood out: Mathematics for Machine Learning by Deisenroth, Faisal and Ong Advanced Calculus of Several Variables by C. H. Edwards Jr. Mathematical Methods Lecture Notes from Imperial College by Deisenroth and Cheraghchi The original information theory paper by Shannon The Elements of Statistical Learning by Hastie, Tibshirani and Friedman Pattern Recognition and Machine Learning by Bishop The Probabalistic Machine Learning Series by Kevin P. Murphy Deep Learning by Goodfellow, Bengio and Courville Mathematics of Machine Learning on MIT OCW (here) My question is, what order should I start self-learning in, given the (somewhat limited) background knowledge I have? Also, are there any other resources that would help? submitted by /u/Far_Clothes_5054 [link] [comments]

Hi everyone, With the different data points, such as phi-1.5 performance being as good as 7b models on some tasks, it seems to be plausible that small models can be quite capable on specific tasks. I am working on BlindChat, an open-source and private solution to run small LLMs on your browser and I am interested in fine-tuning a phi-1.5 on some domain specific data. I am thinking of having an approach similar to the researchers of the phi paper, which is creating a high quality dataset using GPT3.5 / GPT4. Do you know good open-source frameworks that make it easy to create a high quality data for a specific task using an existing large model, like GPT3.5/4 or Llama 2 70b? submitted by /u/Separate-Still3770 [link] [comments]

I want to tune Facebook's LLaMA or any available LLM model to be able to answer questions about my business. The idea is to provide a prompt of the business and some Q&As, then based on the provided information, the AI chatbot will answer customers who ask questions about the business. If the answers to the questions are not known or the question is not relevant, the bot should say "I dont know". submitted by /u/the_aceix [link] [comments]

Paper - https://arxiv.org/abs/2307.07162 submitted by /u/MysteryInc152 [link] [comments]

Made a repo for you all to try using a collaborative AI tool which includes 100+ papers on LLM-Based-Agents. You can try out the repo here: https://www.collama.ai/varun/llm-based-agents submitted by /u/_llama2 [link] [comments]

In this analysis, we use a K-nearest neighbors (KNN) model to conduct crop segmentation, and we compare these results with ground truth imagery on an agricultural region. Our results reveal that the classification from the KNN model is more accurately representative of the state of the current crop field in 2017 than the ground truth classification data from 2015. These results are a testament to the power of Planet’s high-cadence geospatial imagery. Agricultural fields change often, sometimes multiple times a season, and having high-frequency satellite imagery available to observe and analyze this land can provide immense value to our understanding of agricultural land and quickly-changing environments.

Project Page Paper Code In this paper, we present Gaussian Splatting based text-to-3D generation (GSGEN), a novel approach for generating high-quality 3D objects. Previous methods suffer from inaccurate geometry and limited fidelity due to the absence of 3D prior and proper representation. We leverage 3D Gaussian Splatting, a recent state-of-the-art representation, to address existing shortcomings by exploiting the explicit nature that enables the incorporation of 3D prior. Specifically, our method adopts a progressive optimization strategy, which includes a geometry optimization stage and an appearance refinement stage. In geometry optimization, a coarse representation is established under a 3D geometry prior along with the ordinary 2D SDS loss, ensuring a sensible and 3D-consistent rough shape. Subsequently, the obtained Gaussians undergo an iterative refinement to enrich details. In this stage, we increase the number of Gaussians by compactness-based densification to enhance continuity and improve fidelity. With these designs, our approach can generate 3D content with delicate details and more accurate geometry. Extensive evaluations demonstrate the effectiveness of our method, especially for capturing high-frequency components. submitted by /u/Sirisian [link] [comments]

Hi! I just open-sourced a project that I've been working on for a while and wanted to see what you think! The goal of Carton (https://carton.run) is to let you use a single interface to run any machine learning model from any programming language. It’s currently difficult to integrate models that use different technologies (e.g. TensorRT, Ludwig, TorchScript, JAX, GGML, etc) into your application, especially if you’re not using Python. Even if you learn the details of integrating each of these frameworks, running multiple frameworks in one process can cause hard-to-debug crashes. Ideally, the ML framework a model was developed in should just be an implementation detail. Carton lets you decouple your application from specific ML frameworks so you can focus on the problem you actually want to solve. At a high level, the way Carton works is by running models in their own processes and using an IPC system to communicate back and forth with low overhead. Carton is primarily implemented in Rust, with bindings to other languages. There are lots more details linked in the architecture doc below. Importantly, Carton uses your model’s original underlying framework (e.g. PyTorch) under the hood to actually execute the model. This is meaningful because it makes Carton composable with other technologies. For example, it’s easy to use custom ops, TensorRT, etc without changes. This lets you keep up with cutting-edge advances, but decouples them from your application. I’ve been working on Carton for almost a year now and I open sourced it on Wednesday! Some useful links: Website, docs, quickstart - https://carton.run Explore existing models - https://carton.pub Repo - https://github.com/VivekPanyam/carton Architecture - https://github.com/VivekPanyam/carton/blob/main/ARCHITECTURE.md Please let me know what you think! submitted by /u/vpanyam [link] [comments]

Hi Everyone, I’m doing a project where I’m crowdsourcing a lot of location data for a set of location labels and then trying to cluster it for each and using the centroid of the cluster as the most accurate location for that location label. The data keeps coming in everyday. I’m not sure when to stop computation. Initially I thought I’ll check the delta between each days centroid computed and if the delta falls under a threshold then stop computing. But now I’m thinking if my daily data collected gets marked as outliers, subsequent days centroids won’t have much of a delta and it will pass my convergence condition. Any suggestions? submitted by /u/Longjumping-Song4958 [link] [comments]

Suppose we want to use a deep learning model on a gpu within our app. We want this model to coexist on the gpu with other processes, effectively limit it's possible usage of resources. As cuDNN/cuBLAS routines are nondeterministic and possibly dynamically allocate variable amount of memory, how do people manage this problem? Is it a problem at all? Estimating memory usage of deep learning models on gpu is notoriously hard. There is a research paper from Microsoft tackling this problem and they mispredict the usage of memory by 15% on average. Some cpu BLAS libraries like openBLAS or MKL also dynamically allocate the memory, but there are alternatives - LAPACK as far as I know uses only the memory provided by the caller, making it viable option for applications in embedded. In safety criticall tasks like autonomous driving, it seems to be especially important to have deterministic and clear bounds on memory usage of the process and not get spontaneously hit by CUDA OOM error. I can imagine that for autonomous vehicles, the prediction pipeline usually is the only process occupying the GPU, making the problem less visible or go away completely. In case of desktop applications only running the inference, the problem is also less visible as the memory requirements for forward pass only are comparatively low (we can reuse allocated memory blocks efficiently). However, I am looking on this subject through the problem of training/finetuning deep models on the edge devices, being increasingly available thing to do. Looking at tflite, alibaba's MNN, mit-han-lab's tinyengine etc.. To summarize: 1. Do nondeterministic memory allocations pose a problem for deploying deep models in the wild and if so, what strategies do people employ to mitigate this problem? 2. Do you think it would be beneficial to have a deep learning library with worse performance but with fine graned controll over the memory allocations? (If such library doesn't already exist. If it does, please tell me.) Such a library could possibly enable you to choose from a list of possible computation routines, providing you with required memory before the call is made and choose suitable perf/memory tradeoff routine for a given state of the machine per function call. Eg: if os.free_mem>matmul(x,y,fast).mem_cost: matmul(x,y,fast).compute() else: matmul(x,y,economic).compute() submitted by /u/jasio1909 [link] [comments]

Which are currently the best Sentence Embedding pre-trained models out there? submitted by /u/Uilxitora [link] [comments]

Blog about AI "black box" nature and how it can be explained and become engaging to users using gamification. Explained with example from open-appsec an open-source machine learning-based Web Application & API Security product. https://www.openappsec.io/post/using-gamification-to-demystify-the-ai-black-box-in-a-waf-product https://github.com/openappsec/openappsec submitted by /u/onirisapp [link] [comments]

Hey all! I'm in the throws of doing a startup and looking for someone to help build a legal tech platform. I can discuss more in person, but it is intended to be a human/lawyer in the loop workflow tool for complex contract and deal analysis. Base product is built and deployed. I'm a former developer turned lawyer with 15 years corporate experiences, and need help/talent/co-founder to help take things to the next level. Ideally you have a mixture of NLP and regular software engineering background and just a very practical approach. If you've played with LLM's all the better. Options for cash, equity, larger roles are all on the table. Just looking for the right talent. DM me if you are interested and lets talk about experience, etc.! And it seems that tags are turned off in here, so not sure how to tag something as [Project] but I put it in the title. submitted by /u/pudgyplacater [link] [comments]

Project page: https://realfill.github.io/ Paper: https://arxiv.org/abs/2309.16668 RealFill is able to complete the image with what should have been there. Abstract Recent advances in generative imagery have brought forth outpainting and inpainting models that can produce high-quality, plausible image content in unknown regions, but the content these models hallucinate is necessarily inauthentic, since the models lack sufficient context about the true scene. In this work, we propose RealFill, a novel generative approach for image completion that fills in missing regions of an image with the content that should have been there. RealFill is a generative inpainting model that is personalized using only a few reference images of a scene. These reference images do not have to be aligned with the target image, and can be taken with drastically varying viewpoints, lighting conditions, camera apertures, or image styles. Once personalized, RealFill is able to complete a target image with visually compelling contents that are faithful to the original scene. We evaluate RealFill on a new image completion benchmark that covers a set of diverse and challenging scenarios, and find that it outperforms existing approaches by a large margin. ​ submitted by /u/StrawberryNumberNine [link] [comments]

https://www.youtube.com/watch?v=aNJWCwG-H_U submitted by /u/Snoo63916 [link] [comments]

Paper : https://arxiv.org/abs/2309.07416 Demo : https://anton-jeran.github.io/MAD/ Code : https://github.com/anton-jeran/MULTI-AUDIODEC submitted by /u/Snoo63916 [link] [comments]

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

I have a CNN model, call it model M. It was trained on dataset A for object pose estimation. After training for 100 epochs, it resulted in these losses: Train: 0.06 Val: 0.08 As dataset A is somewhat limited, I wonder if I can incorporate additional data via a different, but related task: object segmentation for similar objects. Model M is a UNet, so I can incorporate this task simply with an additional output channel in the last layer. I add dataset B for object segmentation. During training, M learns on both datasets quite well, which suggests to me that the tasks are well-aligned. After 100 epochs, I get these losses on dataset A: Train: 0.06 Val: 0.16 This is surprising to me. If I get the same training loss on dataset A, while training on additional data. I'd expect the validation loss to be lower, since I'm training on 2x the data. Yet the validation performance is consistently higher when I train on both datasets. The only explanation I can think of is the double descent phenomenon. Perhaps when I trained only on dataset A, I was significantly over-parameterized, but past the interpolation threshold. So perhaps adding more data brought me closer to the interpolation threshold, leading to worse generalization. Does this explanation seem likely? Has anyone had similar experiences? submitted by /u/murrdpirate [link] [comments]

Hello, denoising autoencoders is when you train something to reverse x+n -> x. This seems to be basically the same as a diffusion model, more so if you see the U-Net diffusion model, which is effectively an information bottleneck. submitted by /u/windoze [link] [comments]

For a quick disclaimer, I know people on here think the sub is being flooded by people who arent ml engineers/researchers. I have worked at two FAANGS on ml research teams/platforms. My opinion is that GPT-4 Vision/Image processing is out of science fiction. I fed chatgpt an image of a complex sql data base schema, and it converted it to code, then optimized the schema. It understood the arrows pointing between table boxes on the image as relations, and even understand many to one/many to many. I took a picture of random writing on a page, and it did OCR better than has ever been possible. I was able to ask questions that required OCR and a geometrical understanding of the page layout. Where is the hype on here? This is an astounding human breakthrough. I cannot believe how much ML is now obsolete as a result. I cannot believe how many computer science breakthroughs have occurred with this simple model update. Where is the uproar on this sub? Why am I not seeing 500 comments on posts about what you can do with this now? Why are there even post submissions about anything else? submitted by /u/corporate_autist [link] [comments]

Hey all - vllm==0.2.0 got released a couple of hours ago and I put together some code to get it running with the new Mistral 7B model. Also included are some benchmarks for different input batch sizes with the model (output capped at 200 tokens): Batch size Tokens /s 1 46 10 400 60 1.8k Hope it's useful, let me know if you'd like any more info! Here's the link: https://docs.mystic.ai/docs/mistral-ai-7b-vllm-fast-inference-guide submitted by /u/paulcjh [link] [comments]

This post is co-written with Ilan Geller and Shuyu Yang from Accenture. Enterprises today face major challenges when it comes to using their information and knowledge bases for both internal and external business operations. With constantly evolving operations, processes, policies, and compliance requirements, it can be extremely difficult for employees and customers to stay up

I've recently started working with ML and NLP, so I'm sorry if this sounds Naive. Unlike Llama 2 or other open source, we don't have access to the model weights for GPT-4, Claude or Bard, so Benchmark Evals are being run through either APIs or the chat Interface. So how do we know that the model isn't being Boosted by custom web-searching abilities or RAG? While GPT-4 might have a turnoff option, I'm pretty sure Bard is always online, being built by google. So how do we trust benchmarks? Also, have any opensource been tested after Websearch/RAG? submitted by /u/vatsadev [link] [comments]

We’re excited to announce that Amazon SageMaker Canvas now offers a quicker and more user-friendly way to create machine learning models for time-series forecasting. SageMaker Canvas is a visual point-and-click service that enables business analysts to generate accurate machine learning (ML) models without requiring any machine learning experience or having to write a single line of code. SageMaker

In the world of data-driven decision-making, time series forecasting is key in enabling businesses to use historical data patterns to anticipate future outcomes. Whether you are working in asset risk management, trading, weather prediction, energy demand forecasting, vital sign monitoring, or traffic analysis, the ability to forecast accurately is crucial for success. In these applications,

Greg Diamos, the CTO of startup Lamini, was an early CUDA architect at NVIDIA and later cofounded MLPerf. He asserts that AMD's ROCM has "achieved software parity" with CUDA for LLMs. Lamini, focused on tuning LLM's for corporate and institutional users, has decided to go all-in with AMD Instict GPU's. https://www.crn.com/news/components-peripherals/llm-startup-embraces-amd-gpus-says-rocm-has-parity-with-nvidia-s-cuda-platform submitted by /u/makmanred [link] [comments]

In the rapidly evolving world of AI and machine learning (ML), foundation models (FMs) have shown tremendous potential for driving innovation and unlocking new use cases. However, as organizations increasingly harness the power of FMs, concerns surrounding data privacy, security, added cost, and compliance have become paramount. Regulated and compliance-oriented industries, such as financial services,

Companies use time series forecasting to make core planning decisions that help them navigate through uncertain futures. This post is meant to address supply chain stakeholders, who share a common need of determining how many finished goods are needed over a mixed variety of planning time horizons. In addition to planning how many units of

From startups to enterprises, organizations of all sizes are getting started with generative AI. They want to capitalize on generative AI and translate the momentum from betas, prototypes, and demos into real-world productivity gains and innovations. But what do organizations need to bring generative AI into the enterprise and make it real? When we talk

The Amazon EU Design and Construction (Amazon D&C) team is the engineering team designing and constructing Amazon Warehouses across Europe and the MENA region. The design and deployment processes of projects involve many types of Requests for Information (RFIs) about engineering requirements regarding Amazon and project-specific guidelines. These requests range from simple retrieval of baseline

MDaudit provides a cloud-based billing compliance and revenue integrity software as a service (SaaS) platform to more than 70,000 healthcare providers and 1,500 healthcare facilities, ensuring healthcare customers maintain regulatory compliance and retain revenue. Working with the top 60+ US healthcare networks, MDaudit needs to be able to scale its artificial intelligence (AI) capabilities to

As machine learning (ML) goes mainstream and gains wider adoption, ML-powered inference applications are becoming increasingly common to solve a range of complex business problems. The solution to these complex business problems often requires using multiple ML models and steps. This post shows you how to build and host an ML application with custom containers

This post was co-authored with Daniele Chiappalupi, participant of the AWS student Hackathon team at ETH Zürich. Everyone can easily get started with machine learning (ML) using Amazon SageMaker JumpStart. In this post, we show you how a university Hackathon team used SageMaker JumpStart to quickly build an application that helps users identify and remove

We’re at an exciting inflection point in the widespread adoption of machine learning (ML), and we believe most customer experiences and applications will be reinvented with generative AI. Generative AI can create new content and ideas, including conversations, stories, images, videos, and music. Like most AI, generative AI is powered by ML models—very large models

Please post your questions here instead of creating a new thread. Encourage others who create new posts for questions to post here instead! Thread will stay alive until next one so keep posting after the date in the title. Thanks to everyone for answering questions in the previous thread! submitted by /u/AutoModerator [link] [comments]

In this blog post, we illustrate how RLHF can be performed on Amazon SageMaker by conducting an experiment with the popular, open-sourced RLHF repo Trlx. Through our experiment, we demonstrate how RLHF can be used to increase the helpfulness or harmlessness of a large language model using the publicly available Helpfulness and Harmlessness (HH) dataset provided by Anthropic. Using this dataset, we conduct our experiment with Amazon SageMaker Studio notebook that is running on an ml.p4d.24xlarge instance. Finally, we provide a Jupyter notebook to replicate our experiments.

In this post, we discuss how United Airlines, in collaboration with the Amazon Machine Learning Solutions Lab, build an active learning framework on AWS to automate the processing of passenger documents. “In order to deliver the best flying experience for our passengers and make our internal business process as efficient as possible, we have developed

To add to our guidance for optimizing deep learning workloads for sustainability on AWS, this post provides recommendations that are specific to generative AI workloads. In particular, we provide practical best practices for different customization scenarios, including training models from scratch, fine-tuning with additional data using full or parameter-efficient techniques, Retrieval Augmented Generation (RAG), and prompt engineering.

In this post, we demonstrate one of the many options that you have to take advantage of AWS’s broadest and deepest set of AI/ML capabilities in a multicloud environment. We show how you can build and train an ML model in AWS and deploy the model in another platform. We train the model using Amazon SageMaker, store the model artifacts in Amazon Simple Storage Service (Amazon S3), and deploy and run the model in Azure.

Multi-modal data is a valuable component of the financial industry, encompassing market, economic, customer, news and social media, and risk data. Financial organizations generate, collect, and use this data to gain insights into financial operations, make better decisions, and improve performance. However, there are challenges associated with multi-modal data due to the complexity and lack

This post is written in collaboration with Dima Zadorozhny and Fuad Babaev from VirtuSwap. VirtuSwap is a startup company developing innovative technology for decentralized exchange of assets on blockchains. VirtuSwap’s technology provides more efficient trading for assets that don’t have a direct pair between them. The absence of a direct pair leads to costly indirect trading,

Amazon SageMaker Feature Store provides an end-to-end solution to automate feature engineering for machine learning (ML). For many ML use cases, raw data like log files, sensor readings, or transaction records need to be transformed into meaningful features that are optimized for model training. Feature quality is critical to ensure a highly accurate ML model.

Machine learning (ML) is becoming increasingly complex as customers try to solve more and more challenging problems. This complexity often leads to the need for distributed ML, where multiple machines are used to train a single model. Although this enables parallelization of tasks across multiple nodes, leading to accelerated training times, enhanced scalability, and improved