Hello guys, I've been working on a sequential labelling using DNA sequences as inputs. Lately there have been 2 foundation models released HyenaDNA (Based on Hyena operator) and Caduceus (based on mamba), I used both pretrained and from scratch models and performances are terrible even with pretrained ones. Does anyone have experience with this type of models, and what are the potential causes for performance drop ? I am literally getting zero performance for the minority class ? Does mamba deal poorly with class imbalance ? submitted by /u/blooming17 [link] [comments]

This is a small drug toxicity prediction GNN model I wrote/trained repo: https://github.com/Null-byte-00/toxicity-prediction-gnn submitted by /u/Soroush_ra [link] [comments]

I have a use case to use function calling within my application, I am confused whether to choose OpenAI function calling or use Bedrock Agents coupled with Lambda functions for this, which is the best approach? Or help me to choose between these two. submitted by /u/raman_boom [link] [comments]

ML is very good at solving a niche set of problems, but most of the technical nuances are lost on tech bros and managers. What are some problems you have been told to solve which would be impossible (no data, useless data, unrealistic expectations) or a misapplication of ML (can you have this LLM do all of out accounting). submitted by /u/LanchestersLaw [link] [comments]

Does anyone know what datasets are out there for causal inference? I’d like to explore methods in the doubly robust ML literature, and I’d like to compensate my learning by working on some datasets and learn the econML software. Does anyone know of any datasets, specifically in the context of marketing/pricing/advertising that would be good sources to apply causal inference techniques? I’m open to other datasets as well. submitted by /u/Direct-Touch469 [link] [comments]

Dreambooth the MusicGen model suite on small consumer GPUs, in a matter of minutes, using this repository: https://github.com/ylacombe/musicgen-dreamboothing The aim of this project is to provide tools to easily fine-tune and dreambooth the MusicGen model suite, with little data and to leverage a series of optimizations and tricks to reduce resource consumption, thanks to LoRA adaptors. For example, the model can be fine-tuned on a particular music genre or artist to give a checkpoint that generates in that given style. The aim is also to easily share and build on these trained checkpoints, Specifically, this involves: using as few data and resources as possible. We're talking fine-tuning with as little as 15mn on an A100 and as little as 10GB to 16GB of GPU utilization. easily share and build models thanks to the Hugging Face Hub. optionally, generate automatic music descriptions optionally, training MusicGen in a Dreambooth-like fashion, where one key-word triggers generation in a particular style Wandb example of what the training run looks like here. submitted by /u/Sufficient-Tennis189 [link] [comments]

Speaker diarization, an essential process in audio analysis, segments an audio file based on speaker identity. This post delves into integrating Hugging Face’s PyAnnote for speaker diarization with Amazon SageMaker asynchronous endpoints. We provide a comprehensive guide on how to deploy speaker segmentation and clustering solutions using SageMaker on the AWS Cloud.

Organizations across industries are using automatic text summarization to more efficiently handle vast amounts of information and make better decisions. In the financial sector, investment banks condense earnings reports down to key takeaways to rapidly analyze quarterly performance. Media companies use summarization to monitor news and social media so journalists can quickly write stories on

Paper: https://arxiv.org/abs/2402.11131 Abstract: Speculative decoding is a prominent technique to speed up the inference of a large target language model based on predictions of an auxiliary draft model. While effective, in application-specific settings, it often involves fine-tuning both draft and target models to achieve high acceptance rates. As the number of downstream tasks grows, these draft models add significant complexity to inference systems. We propose Speculative Streaming, a single-model speculative decoding method that fuses drafting into the target model by changing the fine-tuning objective from next token prediction to future n-gram prediction. Speculative Streaming speeds up decoding by 1.8 - 3.1X in a diverse set of tasks, such as Summarization, Structured Queries, and Meaning Representation, without sacrificing generation quality. Additionally, Speculative Streaming is parameter-efficient. It achieves on-par/higher speed-ups than Medusa-style architectures while using ~10000X fewer extra parameters, making it well-suited for resource-constrained devices. submitted by /u/SeawaterFlows [link] [comments]

Paper: https://arxiv.org/abs/2404.08698 Abstract: While Large Language Models (LLMs) have shown remarkable abilities, they are hindered by significant resource consumption and considerable latency due to autoregressive processing. In this study, we introduce Adaptive N-gram Parallel Decoding (ANPD), an innovative and lossless approach that accelerates inference by allowing the simultaneous generation of multiple tokens. ANPD incorporates a two-stage approach: it begins with a rapid drafting phase that employs an N-gram module, which adapts based on the current interactive context, followed by a verification phase, during which the original LLM assesses and confirms the proposed tokens. Consequently, ANPD preserves the integrity of the LLM's original output while enhancing processing speed. We further leverage a multi-level architecture for the N-gram module to enhance the precision of the initial draft, consequently reducing inference latency. ANPD eliminates the need for retraining or extra GPU memory, making it an efficient and plug-and-play enhancement. In our experiments, models such as LLaMA and its fine-tuned variants have shown speed improvements up to 3.67x, validating the effectiveness of our proposed ANPD. submitted by /u/SeawaterFlows [link] [comments]

I just wanted to remind or introduce newcomers to this paper. I think this discussion should be re-opened since many people here actually do influence the trends of the field. https://arxiv.org/pdf/1807.03341 On a personal note (feel free to skip): Specifically, I want to point out the issue of "Mathiness", as it seems like this problem got way out of hand and most best papers of conferences suffer from it (one of the most important ML papers tried to be mathy and introduced a big mistake, I believe other papers have bigger issues but no one bothers to check it). So here are my personal points to academics and researchers: We (I think most will relate), practitioners, do not need equations to know what recall is and clearly don't want to read difficult-to-understand versions of what linear regression is, it just makes your paper unuseful. If you don't want to waste our time, please put it in the appendix or completely remove it. Reviewers, please don't get impressed by unnecessary math, if it's complicated and does nothing useful, who cares? Also, it might be flawed anyway and you will probably not catch it. submitted by /u/pyepyepie [link] [comments]

In this video about Times Series, https://www.youtube.com/watch?v=0zpg9ODE6Ww, does anyone have an idea about the Python package used to create the animated plots showed at the 34th minute of the video ? Thank for your help. submitted by /u/SatieGonzales [link] [comments]

Following the review phase(old post), creating a thread for others like me waiting for the decision. All the best! submitted by /u/PaganPasta [link] [comments]

It seems to me that thanks to the residual path the gradient that flows to each layer is the same regardless of the transformer layer/block. Example: ProjectionAndCost(X + L1(X) + L2(X + L1(X)) + L3(X + L1(X) + L2(X + L1(X))) ...) Since the input to ProjectionAndCost is just sum of outputs from all layers and initial embeddings then the gradient that comes to the layer L1 is the same as the gradient that comes to L2 or L3. So we could: first train only L1: ProjectionAndCost(X + L1(X)) freeze L1, include L2 and train: ProjectionAndCost(X + L1(X) + L2(X + L1(X))) freeze L1 and L2, include L3 and train: ProjectionAndCost(X + L1(X) + L2(X + L1(X)) + L3(X + L1(X) + L2(X + L1(X)))) .. and so on We can't train first L2 then L1, because the input to L2 depends on L1, but we could train lower layers first then gradually add and train deeper layers. Is there any problem with that approach? submitted by /u/kiockete [link] [comments]

So there's this relatively new "BigDL" project" (https://bigdl.readthedocs.io/en/latest/ ), which is for Intel CPUs and Intel GPUs, but there's no mention anywhere of it working for NVIDIA GPUs. Is there any equivalent library for NVIDIA GPUs on a spark cluster? submitted by /u/PepperGrind [link] [comments]

Hi. Here is the new's question. The biggest concern is the rate of generation. I want to generate about 5 seconds of voice in about 100ms. I want to know which model performs best(SOTA) under those conditions. Which model is best for me? I think "styletts2" is best. If you have any relevant experience or know any other information, I would really appreciate your help. Thank you ! submitted by /u/hwk06023 [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 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, 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]

Conversational artificial intelligence (AI) assistants are engineered to provide precise, real-time responses through intelligent routing of queries to the most suitable AI functions. With AWS generative AI services like Amazon Bedrock, developers can create systems that expertly manage and respond to user requests. Amazon Bedrock is a fully managed service that offers a choice of

The Amazon EU Design and Construction (Amazon D&C) team is the engineering team designing and constructing Amazon warehouses. The team navigates a large volume of documents and locates the right information to make sure the warehouse design meets the highest standards. In the post A generative AI-powered solution on Amazon SageMaker to help Amazon EU

In various industries, such as financial services, telecommunications, and healthcare, customers use a digital identity process, which usually involves several steps to verify end-users during online onboarding or step-up authentication. An example of one step that can be used is face search, which can help determine whether a new end-user’s face matches those associated with

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'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]

We are excited to announce two new capabilities in Amazon SageMaker Studio that will accelerate iterative development for machine learning (ML) practitioners: Local Mode and Docker support. ML model development often involves slow iteration cycles as developers switch between coding, training, and deployment. Each step requires waiting for remote compute resources to start up, which

We introduced Amazon Bedrock to the world a little over a year ago, delivering an entirely new way to build generative artificial intelligence (AI) applications. With the broadest selection of first- and third-party foundation models (FMs) as well as user-friendly capabilities, Amazon Bedrock is the fastest and easiest way to build and scale secure generative

This post explores the new enterprise-grade features for Knowledge Bases on Amazon Bedrock and how they align with the AWS Well-Architected Framework. With Knowledge Bases for Amazon Bedrock, you can quickly build applications using Retrieval Augmented Generation (RAG) for use cases like question answering, contextual chatbots, and personalized search.

Amazon SageMaker HyperPod is purpose-built to accelerate foundation model (FM) training, removing the undifferentiated heavy lifting involved in managing and optimizing a large training compute cluster. With SageMaker HyperPod, you can train FMs for weeks and months without disruption. Typically, HyperPod clusters are used by multiple users: machine learning (ML) researchers, software engineers, data scientists,

Organizations are facing ever-increasing requirements for sustainability goals alongside environmental, social, and governance (ESG) practices. A Gartner, Inc. survey revealed that 87 percent of business leaders expect to increase their organization’s investment in sustainability over the next years. This post serves as a starting point for any executive seeking to navigate the intersection of generative

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]

Amazon Personalize is excited to announce automatic training for solutions. Solution training is fundamental to maintain the effectiveness of a model and make sure recommendations align with users’ evolving behaviors and preferences. As data patterns and trends change over time, retraining the solution with the latest relevant data enables the model to learn and adapt,

We are excited to announce a new version of the Amazon SageMaker Operators for Kubernetes using the AWS Controllers for Kubernetes (ACK). ACK is a framework for building Kubernetes custom controllers, where each controller communicates with an AWS service API. These controllers allow Kubernetes users to provision AWS resources like buckets, databases, or message queues

In Part 1 of this series, we presented a solution that used the Amazon Titan Multimodal Embeddings model to convert individual slides from a slide deck into embeddings. We stored the embeddings in a vector database and then used the Large Language-and-Vision Assistant (LLaVA 1.5-7b) model to generate text responses to user questions based on

This is a guest post co-written with the leadership team of Iambic Therapeutics. Iambic Therapeutics is a drug discovery startup with a mission to create innovative AI-driven technologies to bring better medicines to cancer patients, faster. Our advanced generative and predictive artificial intelligence (AI) tools enable us to search the vast space of possible drug

As you navigate the complexities of cloud migration, the need for a structured, secure, and compliant environment is paramount. AWS Landing Zone addresses this need by offering a standardized approach to deploying AWS resources. This makes sure your cloud foundation is built according to AWS best practices from the start. With AWS Landing Zone, you eliminate the guesswork in security configurations, resource provisioning, and account management. It’s particularly beneficial for organizations looking to scale without compromising on governance or control, providing a clear path to a robust and efficient cloud setup. In this post, we show you how to generate customized, compliant IaC scripts for AWS Landing Zone using Amazon Bedrock.

You’ve likely experienced the challenge of taking notes during a meeting while trying to pay attention to the conversation. You’ve probably also experienced the need to quickly fact-check something that’s been said, or look up information to answer a question that’s just been asked in the call. Or maybe you have a team member that always joins meetings late, and expects you to send them a quick summary over chat to catch them up. Then there are the times that others are talking in a language that’s not your first language, and you’d love to have a live translation of what people are saying to make sure you understand correctly. And after the call is over, you usually want to capture a summary for your records, or to send to the participants, with a list of all the action items, owners, and due dates. All of this, and more, is now possible with our newest sample solution, Live Meeting Assistant (LMA).

Today, we are excited to announce that Meta Llama 3 foundation models are available through Amazon SageMaker JumpStart to deploy and run inference. The Llama 3 models are a collection of pre-trained and fine-tuned generative text models. In this post, we walk through how to discover and deploy Llama 3 models via SageMaker JumpStart. What is

We are excited to announce that Slack, a Salesforce company, has collaborated with Amazon SageMaker JumpStart to power Slack AI’s initial search and summarization features and provide safeguards for Slack to use large language models (LLMs) more securely. Slack worked with SageMaker JumpStart to host industry-leading third-party LLMs so that data is not shared with the infrastructure owned by third party model providers. This keeps customer data in Slack at all times and upholds the same security practices and compliance standards that customers expect from Slack itself.

In asset management, portfolio managers need to closely monitor companies in their investment universe to identify risks and opportunities, and guide investment decisions. Tracking direct events like earnings reports or credit downgrades is straightforward—you can set up alerts to notify managers of news containing company names. However, detecting second and third-order impacts arising from events

This post walks you through the Open Source Observability pattern for AWS Inferentia, which shows you how to monitor the performance of ML chips, used in an Amazon Elastic Kubernetes Service (Amazon EKS) cluster, with data plane nodes based on Amazon Elastic Compute Cloud (Amazon EC2) instances of type Inf1 and Inf2.