[D] Hey, It's kind of a simple one but just putting it out for opinions: when passing a graph through graph neural networks to obtain vectors for all the nodes. Is the info in the node required because all we care is about the position of certain node in context to the whole graph and that's how gnn outputs the vectors of each node. Sorry if that was messy. submitted by /u/ab_11nav [link] [comments]

https://youtu.be/ktdUeqzzhiA what text to speech does he use? he's been popping up on my yt feed lately and i can see he has different voices in his videos and most of them sound robotic, what do you think it's being used here? submitted by /u/candidhorse4 [link] [comments]

Say I wanted to do a KNN similarity search, using a text embedding from a block of text from a PDF. But I also want to find these documents written at a similar time, and with similar title and author. Would it make sense to prepend a written form of this metadata ahead of the document text, and sending that to the embedding? Like “this document titled ABC was written by XYZ on January 1, 2020”. Or would it be better to create a separate embedding for the metadata, and merge the embeddings afterwards? submitted by /u/DeadPukka [link] [comments]

Looking for ideas to start an NLP project, I'd like to explore something not too mainstream or novel to some extent, any ideas or datasets I should check out? submitted by /u/mems_m [link] [comments]

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I have performed below steps and require guidance to proceed further I have extracted and preprocessed the text from PDFs. Performed NER on the extracted text and created a data frame of entities. Created a function to preprocess the query and identified the entities in the question. Now I need guidance or any reference to perform the below steps. Match the entities from the question with the entities in the PDF text and retrieve the paragraph ? Calculate the similarity score for each paragraph and display the relevant paragraph Generate answer from the identified paragraph ? Please also guide me if the approach followed is correct or not ? submitted by /u/sasi_0212 [link] [comments]

It seems like Singular Value Decomposition is only used for unsupervised learning when trying to reduce the number of features in a high dimensional dataset, but I was wondering why I don't see any articles or literature on using SVD for supervised learning. I know that using a regularization function like Lasso (L1) can get rid of irrelevant features, but I don't see why SVD wouldn't be helpful too. submitted by /u/TemperatureOk6810 [link] [comments]

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I have been working on information extraction from documents, but what I got to know is there are not enough free tools available for labelling data for these kind of tasks. Are there any free tools available for labelling data for LayoutLM models? submitted by /u/TensorDudee [link] [comments]

Hi, I've done some programming type things before and I'm interested in learning about ML, to be able to make some basic projects with ML, how good does my math need to be. I get As at school in math but I know that what I'm learning now is pretty basic. I'm just wondering whether I should try learn about ML or wait a few more years for my math skills to improve submitted by /u/Daniel_C_____ [link] [comments]

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Hi everyone, I'm currently knees-deep in a ML project with a friend (~4 months of development) and my free compute units on Colab finally ran out. After searching for alternatives, and finding none that work as smoothly as Colab, we've considered to buy a Pro subscription. My question is: How can I share the compute units I'll get from Colab Pro with said friend? Don't want to make the purchase and later realize that I'm the only person with access to those compute units. submitted by /u/RaphDaPingu [link] [comments]

Maybe not a Machine Learning question, but I'm searching for good books about information retrieval. The two primary ones I can find are: - Introduction to Information Retrieval (2008) - Information Retrieval - Implementing and Evaluating Search Engines (2016) ​ They seem a bit old for 2023, but they may still be useful? Do you have any good book recommendations? submitted by /u/Ggronne [link] [comments]

Hi guys! I read a lot of offline RL papers in last Fall semester and choose it as my course project. Offline RL seems to be a very hot topic in recent years, I believe that the major challenge for offline RL are (i) distribution shift and (ii) overestimation. The second challenge is caused by (i), because the learners/agents will never allow to interact with the true environment and they will too optimistic for unseen state-actions. Hence, there are many papers to address such challenges, e.g., CQL and MOPO. However can these methods handle misleading datasets? Consider the following example. Suppose we have only one state (MAB) and two arms. The reward of the first arm will return 2/3 with probability 1 and the reward model of second arm is Bernoulli distribution with p=1/2. Clearly, choosing the first arm is the best choice. Now, for the dataset, unfortunately, all samples on the second arm received reward 1. Because the agent only can access this misleading dataset, if we use Bayesian methods, then the posterior will give a high score for the second arm. If we use Lower Confidence Bound, we need to count the occurrence of each arm. Then, this is very hard to extend this method to MDPs with arbitrary large state and action space. So, does anyone know a function can capture this uncertainty (caused by the dataset) or can any methods to tell the learner that you’re in a very misleading situation? submitted by /u/AndyMeowMeow [link] [comments]

From the Financial Times: https://www.ft.com/content/583ead66-467c-4bd5-84d0-ed5df7b5bf9c Unpaywalled: https://archive.is/ciZPV I guess I'm a little surprised, this feels like Google backing a competitor to 1) their own Google Brain teams, and 2) Deepmind. The cynical take might be that they're trying to lock in Anthropic; the same way Microsoft locked in OpenAI. submitted by /u/bikeskata [link] [comments]

I’ve been developing this idea since I first thought of it in mid December last year. Here’s the elevator pitch (skip to how for technical details): Why? Existing models and learning algorithms are extremely static and unable to generalize across tasks as well as humans or to adapt well to new / changing business requirements. This even applies to the final solutions in recent AutoML (see An Empirical Review of Automated Machine Learning, AutoML: A survey of the state-of-the-art). Beyond being static, most suffer from a need for high-performance systems with large amounts of compute and/or memory. This static and bloated nature not only limits the reusability of code, pipelines and all the computations that went into previous versions of a model architecture upon finding a better one. It also forces our preconceptions of what type of learning is best for the task and which degrees of freedom are needed onto the solution. Instead of perpetuating all these assumptions, I want to create a sort of AutoML capable, under the right conditions, of even developing a learning algorithm / model combination that can dynamically add or remove inputs and outputs subsequently incorporating them into the network with adaptive online self-directed learning. ​ How? Basically, the idea in a nutshell is to use some form of NEAT (neuro evolution of augmenting topologies) and have special nodes in the network that will be activated based on different criteria (depending on the node’s allele for that gene). When activated, however, these special nodes would not send any input forward but instead apply some property change(s) to their connected nodes and/or edges (yes they can connect to an edge and they could choose a subset of their connections or just apply the change(s) to all or use a maximum number of connection hops, etc). It could also create and destroy nodes depending on the effects defined by the allele. There would also be different firing policies (like the normal always fire or thresholding with or without decay, etc.) for all nodes to allow for better leveraging of temporal dynamics. Basically every property of all these policies, including the policy template itself is a potential target for modification by the special neuromodulatory nodes along with the normal properties of a “neuron” like bias, input weights, activation function, aggregation function, etc. The fitness function would either be abstracted away by using rtNEAT in a simulated environment or just be a combined score over a set of simulated tasks. This should add a regularizing force if the tasks are similar enough to help enforce generalization of the evolved algorithms. There should be no limitation placed on cycles in the graph, in fact I would expect cycles to be part of the evolved solutions, which would make them dynamical systems. To reduce the computational complexity of finding a viable solution, the initial population should also be implementations of existing algorithms in the form of the self-modifying neural networks mentioned. It might even be possible to generate a computational graph from open-source implementations as a starting point for the initial population. All of this together should also allow for different parts of the network to use different learning strategies. Theoretically, this can even allow for the evolution of and incorporation of self-organizing criticality and percolation. This could even evolve something that can dynamically add or remove inputs and outputs then incorporate them into the network with adaptive online learning. The network could literally change the learning paradigm for different portions of itself on the fly in different ways depending on the situation. ​ For further clarity, I'm also attaching this mock up of a design I've started working on for an analysis tool Thoughts? Please feel free to chime in. Science should be a public discussion. submitted by /u/Feeling_Card_4162 [link] [comments]

Paper: https://arxiv.org/abs/2302.00923 Github: https://github.com/amazon-science/mm-cot Twitter: https://paperswithcode.com/top-social Abstract: Large language models (LLMs) have shown impressive performance on complex reasoning by leveraging chain-of-thought (CoT) prompting to generate intermediate reasoning chains as the rationale to infer the answer. However, existing CoT studies are mostly isolated in the language modality with LLMs, where LLMs are hard to deploy. To elicit CoT reasoning in multimodality, a possible solution is to fine-tune small language models by fusing the vision and language features to perform CoT reasoning. The key challenge is that those language models tend to generate hallucinated reasoning chains that mislead the answer inference. To mitigate the effect of such mistakes, we propose Multimodal-CoT that incorporates vision features in a decoupled training framework. The framework separates the rationale generation and answer inference into two stages. By incorporating the vision features in both stages, the model is able to generate effective rationales that contribute to answer inference. With Multimodal-CoT, our model under 1 billion parameters outperforms the previous state-of-the-art LLM (GPT-3.5) by 16% (75.17%->91.68%) on the ScienceQA benchmark and even surpasses human performance. https://preview.redd.it/g9eo0f94k1ga1.jpg?width=1331&format=pjpg&auto=webp&s=9b5fc84b424aff7160b69ff7c7a5fad071cbb7d2 https://preview.redd.it/fgboci94k1ga1.jpg?width=1323&format=pjpg&auto=webp&s=35215544d9e0a74881c42503d04b62ab09081af1 https://preview.redd.it/2ojfym94k1ga1.jpg?width=1660&format=pjpg&auto=webp&s=cf040c4f422f6c323e8c4d75474a5881f45a41d1 https://preview.redd.it/k7huem94k1ga1.jpg?width=1326&format=pjpg&auto=webp&s=f4326a5088744d3856e5c5c23311be6348fab924 https://preview.redd.it/05m8rf94k1ga1.jpg?width=658&format=pjpg&auto=webp&s=ac4110e57a49fcea6f8c03571edd391ff71bd13d submitted by /u/Singularian2501 [link] [comments]

Hey ML Reddit! I just shipped a project I’ve been working on called Maroofy: https://maroofy.com You can search for any song, and it’ll use the song’s audio to find other similar-sounding music. Demo: https://twitter.com/subby_tech/status/1621293770779287554 How does it work? I’ve indexed ~120M+ songs from the iTunes catalog with a custom AI audio model that I built for understanding music. My model analyzes raw music audio as input and produces embedding vectors as output. I then store the embedding vectors for all songs into a vector database, and use semantic search to find similar music! Here are some examples you can try: Fetish (Selena Gomez feat. Gucci Mane) — https://maroofy.com/songs/1563859943 The Medallion Calls (Pirates of the Caribbean) — https://maroofy.com/songs/1440649752 Hope you like it! This is an early work in progress, so would love to hear any questions/feedback/comments! 😀 submitted by /u/BullyMaguireJr [link] [comments]

Github: https://github.com/google/vizier Google AI Blog: https://ai.googleblog.com/2023/02/open-source-vizier-towards-reliable-and.html Tweet from Zoubin Ghahramani: https://twitter.com/ZoubinGhahrama1/status/1621321675936768000?s=20&t=ZEuz9oSc_GWYxixtXDskqA submitted by /u/enderlayer [link] [comments]

Hello everyone, I'm interested in diving into the field of computer vision and I recently came across the concept of Vision Transformer (ViT). I want to understand this concept in depth but I'm not sure what prerequisites I need to have in order to grasp the concept fully. Do I need to have a strong background in Recurrent Neural Networks (RNNs) and Transformer (Attention Is All You Need) to understand ViT, or can I get by just knowing the basics of deep learning and Convolutional Neural Networks (CNNs)? I would really appreciate if someone could shed some light on this and provide some guidance. Thank you in advance! submitted by /u/SAbdusSamad [link] [comments]

Today, the NFL is continuing their journey to increase the number of statistics provided by the Next Gen Stats Platform to all 32 teams and fans alike. With advanced analytics derived from machine learning (ML), the NFL is creating new ways to quantify football, and to provide fans with the tools needed to increase their

The National Football League (NFL) is one of the most popular sports leagues in the United States and is the most valuable sports league in the world. The NFL, BioCore, and AWS are committed to advancing human understanding around the diagnosis, prevention, and treatment of sports-related injuries to make the game of football safer. More

Almost 80% of today’s web content is user-generated, creating a deluge of content that organizations struggle to analyze with human-only processes. The availability of consumer information helps them make decisions, from buying a new pair of jeans to securing home loans. In a recent survey, 79% of consumers stated they rely on user videos, comments,

Recent developments in deep learning have led to increasingly large models such as GPT-3, BLOOM, and OPT, some of which are already in excess of 100 billion parameters. Although larger models tend to be more powerful, training such models requires significant computational resources. Even with the use of advanced distributed training libraries like FSDP and

Amazon SageMaker provides a suite of built-in algorithms, pre-trained models, and pre-built solution templates to help data scientists and machine learning (ML) practitioners get started on training and deploying ML models quickly. You can use these algorithms and models for both supervised and unsupervised learning. They can process various types of input data, including tabular,

Amazon Forecast is a fully managed service that uses machine learning (ML) to generate highly accurate forecasts, without requiring any prior ML experience. Forecast is applicable in a wide variety of use cases, including estimating supply and demand for inventory management, travel demand forecasting, workforce planning, and computing cloud infrastructure usage. You can use Forecast

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]

This post is co-authored by Tristan Miller from Best Egg. Best Egg is a leading financial confidence platform that provides lending products and resources focused on helping people feel more confident as they manage their everyday finances. Since March 2014, Best Egg has delivered $22 billion in consumer personal loans with strong credit performance, welcomed

Today, gaining customer loyalty cannot be a one-off thing. A brand needs a focused and integrated plan to retain its best customers—put simply, it needs a customer loyalty program. Earn and burn programs are one of the main paradigms. A typical earn and burn program rewards customers after a certain number of visits or spend.

Model explainability refers to the process of relating the prediction of a machine learning (ML) model to the input feature values of an instance in humanly understandable terms. This field is often referred to as explainable artificial intelligence (XAI). Amazon SageMaker Clarify is a feature of Amazon SageMaker that enables data scientists and ML engineers

In November 2022, we announced that AWS customers can generate images from text with Stable Diffusion models in Amazon SageMaker JumpStart. Today, we announce a new feature that lets you upscale images (resize images without losing quality) with Stable Diffusion models in JumpStart. An image that is low resolution, blurry, and pixelated can be converted

It’s an exciting day for the development community. Cohere’s state-of-the-art language AI is now available through Amazon SageMaker. This makes it easier for developers to deploy Cohere’s pre-trained generation language model to Amazon SageMaker, an end-to-end machine learning (ML) service. Developers, data scientists, and business analysts use Amazon SageMaker to build, train, and deploy ML models quickly and easily using its fully managed infrastructure, tools, and workflows.

This post is co-written by Christopher Diaz, Sam Kinard, Jaime Hidalgo and Daniel Suarez  from CCC Intelligent Solutions. In this post, we discuss how CCC Intelligent Solutions (CCC) combined Amazon SageMaker with other AWS services to create a custom solution capable of hosting the types of complex artificial intelligence (AI) models envisioned. CCC is a

Amazon SageMaker Studio is a fully integrated development environment (IDE) for machine learning (ML) partly based on JupyterLab 3. Studio provides a web-based interface to interactively perform ML development tasks required to prepare data and build, train, and deploy ML models. In Studio, you can load data, adjust ML models, move in between steps to adjust experiments,

Amazon SageMaker JumpStart is the Machine Learning (ML) hub of SageMaker providing pre-trained, publicly available models for a wide range of problem types to help you get started with machine learning. Understanding customer behavior is top of mind for every business today. Gaining insights into why and how customers buy can help grow revenue. Customer churn is

This post is co-written with Jennifer Bergstrom, Sr. Technical Director, ParsonsX. Parsons Corporation (NYSE:PSN) is a leading disruptive technology company in critical infrastructure, national defense, space, intelligence, and security markets providing solutions across the globe to help make the world safer, healthier, and more connected. Parsons provides services and capabilities across cybersecurity, missile defense, space ground

This post is co-written by Ramdev Wudali and Kiran Mantripragada from Thomson Reuters. In 1992, Thomson Reuters (TR) released its first AI legal research service, WIN (Westlaw Is Natural), an innovation at the time, as most search engines only supported Boolean terms and connectors. Since then, TR has achieved many more milestones as its AI

This blog post is co-written with Chaoyang He and Salman Avestimehr from FedML. Analyzing real-world healthcare and life sciences (HCLS) data poses several practical challenges, such as distributed data silos, lack of sufficient data at a single site for rare events, regulatory guidelines that prohibit data sharing, infrastructure requirement, and cost incurred in creating a

This blog post is co-written with Chaoyang He and Salman Avestimehr from FedML. Analyzing real-world healthcare and life sciences (HCLS) data poses several practical challenges, such as distributed data silos, lack of sufficient data at any single site for rare events, regulatory guidelines that prohibit data sharing, infrastructure requirement, and cost incurred in creating a

This post was co-authored with Mark Lott, Distinguished Technical Architect, Salesforce, Inc. Enterprises that operate globally are experiencing challenges sourcing customer support professionals with multi-lingual experience. This process can be cost-prohibitive and difficult to scale, leading many enterprises to only support English for chats. Using human interpreters for translation support is expensive, and infeasible since

This is guest post by Andy Whittle, Principal Platform Engineer – Application & Reliability Frameworks at The Very Group. At The Very Group, which operates digital retailer Very, security is a top priority in handling data for millions of customers. Part of how The Very Group secures and tracks business operations is through activity logging