I have a dataset that, when plotted, shows a noisy black line. I'd like to smooth out this noise to get a cleaner trend line (similar to the red line shown). What methods would you recommend for noise reduction? https://preview.redd.it/p8q0i4f9h45e1.png?width=1574&format=png&auto=webp&s=29cd8c82af7b54a1d3da22655502fd5cf406e807 submitted by /u/mrtule [link] [comments]

Does anyone know of any services that offer input token log-probs from open source LLMs like LLama-8B? I'm looking for a cost-effective way to host an LLM-based application with low traffic, so I'd like per-query or per-token pricing, rather than per-hour GPU rental. It's unfortunately dependent on direct-access to log-probs on user-provided tokens. None of the "chat completion" APIs I've found seem to expose this. It makes sense for private models, but for open source models, I don't see any downside to exposing it. submitted by /u/severed-identity [link] [comments]

Hi everybody. I have a list of user actions (around 1m objects) where only a small fraction (less than 1000) are labeled. I want to find most similar objects to them. What is a good way to approach it? I personally have 2 ideas in mind: one class classification or unsupervised clustering. My problem with the first is that I know only 1 suitable model (one class svm) and it can be too simple for my data. Problem with second one is obvious - it's unsupervised and labeling will be used only at the final step, so their efficiency is not guaranteed. submitted by /u/Jor_ez [link] [comments]

Paper: https://storage.googleapis.com/deepmind-media/papers/SchultzAdamek24Mastering/SchultzAdamek24Mastering.pdf Abstract: While large language models perform well on a range of complex tasks (e.g., text generation, question answering, summarization), robust multi-step planning and reasoning remains a considerable challenge for them. In this paper we show that search-based planning can significantly improve LLMs’ playing strength across several board games (Chess, Fischer Random / Chess960, Connect Four, and Hex). We introduce, compare and contrast two major approaches: In external search, the model guides Monte Carlo Tree Search (MCTS) rollouts and evaluations without calls to an external engine, and in internal search, the model directly generates in-context a linearized tree of potential futures and a resulting final choice. Both build on a language model pre-trained on relevant domain knowledge, capturing the transition and value functions across these games. We find that our pre-training method minimizes hallucinations, as our model is highly accurate regarding state prediction and legal moves. Additionally, both internal and external search indeed improve win-rates against state-of-the-art bots, even reaching Grandmaster-level performance in chess while operating on a similar move count search budget per decision as human Grandmasters. The way we combine search with domain knowledge is not specific to board games, suggesting direct extensions into more general language model inference and training techniques. submitted by /u/RobbinDeBank [link] [comments]

Open vocabulary object detectors allow you to pass in a prompt and an image and attempt to output bounding boxes around objects matching your prompt. Image referential detectors allow you to pass in an image of an object as the prompt and a target image and attempts to output bounding boxes around objects matching the image prompt in the target image. For a reference, YOLOWorld provides both image referential and open vocabulary modes. The idea I've been toying with is whether there is a good way to train for greater control over specificity. For example, If I pass in an image of a golden retriever, am I looking for golden retrievers specifically? All dogs? All animals? Language is a bit more specific, but the same principle can apply. If I search for red cars, do red trucks count? Do maroon cars? In my experience, trying to be too specific textually with OVD models causes erratic behavior. IE, "A red car or van but not truck " would give bad performance, as it doesn't really match what would be in grounding captions. My initial idea for how to systematically define the distance between potential targets and the query is via embedding distance. If I take a phrase grounding dataset, I could compute embeddings separately for each crop of a region and for its corresponding text using a model like CLIP. A sample training process would be like this Select a random image. Select a random image crop embedding. Select a random similarity threshold. Do an approx KNN using that random image crop embedding, stopping once we reach a sample with an embedding above the similarity threshold. This is our prompt embedding If we selected an image region embedding, we are doing image referential detection. If we selected a text embedding, we are doing open vocabulary detection. Calculate prompt embedding similarity to all image crop embeddings in the primary image. Mark all objects with similarity above the threshold as positive examples. Run the network, using the selected image, prompt embedding, and similarity threshold as input. Use the previously calculated positives examples as labels. Does anyone know of any papers that work with similar ideas, or have thoughts on whether this process would be useful or could be improved? I'm pretty early into looking into this, so just references or even field terms that would point me in the right direction would be helpful. Other ideas Always detect all objects given a target phrase, but set the label confidence equal to the embedding similarity. Make the model capable of handling multiple input queries corresponding to the same object, to better indicate the intended domain. Possibly with both negative and positive queries. Not sure how to train yet, possibly sample clusters of similar embeddings from the dataset to build prompt sets. Perform instructional tuning, similar to what is done for some LLMs and VLMs, to make the model better handle complex text prompts, and allow instructional text prompts to be paired with images for image referential mode. Related questions Is CLIP still standard for computing text and image embeddings that share a unified embedding space? submitted by /u/Revolutionary-Fig660 [link] [comments]

Hey Reddit, I’ve been in an AI/ML role for a few years now, and I’m starting to feel disconnected from the work. When I started, deep learning models were getting good, and I quickly fell in love with designing architectures, training models, and fine-tuning them for specific use cases. Seeing a loss curve finally converge, experimenting with layers, and debugging training runs—it all felt like a craft, a blend of science and creativity. I enjoyed implementing research papers to see how things worked under the hood. Backprop, gradients, optimization—it was a mental workout I loved. But these days, it feels like everything has shifted. LLMs dominate the scene, and instead of building and training models, the focus is on using pre-trained APIs, crafting prompt chains, and setting up integrations. Sure, there’s engineering involved, but it feels less like creating and more like assembling. I miss the hands-on nature of experimenting with architectures and solving math-heavy problems. It’s not just the creativity I miss. The economics of this new era also feel strange to me. Back when I started, compute was a luxury. We had limited GPUs, and a lot of the work was about being resourceful—quantizing models, distilling them, removing layers, and squeezing every bit of performance out of constrained setups. Now, it feels like no one cares about cost. We’re paying by tokens. Tokens! Who would’ve thought we’d get to a point where we’re not designing efficient models but feeding pre-trained giants like they’re vending machines? I get it—abstraction has always been part of the field. TensorFlow and PyTorch abstracted tensor operations, Python abstracts C. But deep learning still left room for creation. We weren’t just abstracting away math; we were solving it. We could experiment, fail, and tweak. Working with LLMs doesn’t feel the same. It’s like fitting pieces into a pre-defined puzzle instead of building the puzzle itself. I understand that LLMs are here to stay. They’re incredible tools, and I respect their potential to revolutionize industries. Building real-world products with them is still challenging, requiring a deep understanding of engineering, prompt design, and integrating them effectively into workflows. By no means is it an “easy” task. But the work doesn’t give me the same thrill. It’s not about solving math or optimization problems—it’s about gluing together APIs, tweaking outputs, and wrestling with opaque systems. It’s like we’ve traded craftsmanship for convenience. Which brings me to my questions: Is there still room for those of us who enjoy the deep work of model design and training? Or is this the inevitable evolution of the field, where everything converges on pre-trained systems? What use cases still need traditional ML expertise? Are there industries or problems that will always require specialized models instead of general-purpose LLMs? Am I missing the bigger picture here? LLMs feel like the “kernel” of a new computing paradigm, and we don’t fully understand their second- and third-order effects. Could this shift lead to new, exciting opportunities I’m just not seeing yet? How do you stay inspired when the focus shifts? I still love AI, but I miss the feeling of building something from scratch. Is this just a matter of adapting my mindset, or should I seek out niches where traditional ML still thrives? I’m not asking this to rant (though clearly, I needed to get some of this off my chest). I want to figure out where to go next from here. If you’ve been in AI/ML long enough to see major shifts—like the move from feature engineering to deep learning—how did you navigate them? What advice would you give someone in my position? And yeah, before anyone roasts me for using an LLM to structure this post (guilty!), I just wanted to get my thoughts out in a coherent way. Guess that’s a sign of where we’re headed, huh? Thanks for reading, and I’d love to hear your thoughts! TL;DR: I entered AI during the deep learning boom, fell in love with designing and training models, and thrived on creativity, math, and optimization. Now it feels like the field is all about tweaking prompts and orchestrating APIs for pre-trained LLMs. I miss the thrill of crafting something unique. Is there still room for people who enjoy traditional ML, or is this just the inevitable evolution of the field? How do you stay inspired amidst such shifts? submitted by /u/Educational_News_371 [link] [comments]

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

Project Description: We are inviting skilled professionals to participate in an evaluation challenge to produce a GenAI image based upon a prompt and a set of component images. The candidate may choose whatever models they like to complete the task, so long as they are open source. The results will be reviewed and compared across participants, and the candidate with the most effective and high-quality outputs will be selected for a larger, production-focused engagement. Entry Process: Submit your Github handle and intention to participate to email acr0batproduce@gmail.com. Provide a short bio and resume in your email. If you’re selected, we will provide you access to a repository to contribute to. Challenge Scope: Model Setup and Testing: Create a unique set of prompts to test your image generation model. A set of sample prompts have been provided for your convenience. A minimum of 3 tests are required, but candidates may provide more if they wish. For each image generation, provide at least 3 component images to be used in the final output. A set of component images related to the sample prompt has been provided for your convenience. Develop the model, and test its performance with your sample input prompts and component images. Source code must be pushed to the main branch of the provided repo. Provide the output images in ./static in the repo. Document your findings and results Evaluation Criteria: Image Quality: The final image produced should incorporate all the features from your sample prompt, and component images. Above all, the items in the component images need to be naturally incorporated into the final image. They should not be significantly distorted, or look like they were copy and pasted from the input images. Brand Element Incorporation: Expanding upon the above point, the final image must accurately reflect the input component images. So, if the input image is a Rolex Oyster Perpetual Day-Date 40 in 18 kt yellow gold with a champagne colour, diamond-set dial, fluted bezel and a President bracelet, then the output image must incorporate that same product. Creative Flexibility: Generate diverse variations of prompts, component images and output images. Customization: Showcase how well your model responds to different parameters. Code Quality: All aspects of code quality will be assessed. Examples include: repo structure, IaC, CI/CD, unit testing, performance, documentation, etc. Extensibility: Your sample prompts, input images, and output images will be used to quickly screen for accuracy and limit submissions; however, importantly, your model will be tested against our internal prompts and input images to gauge how well it performs on different types of problems. Deliverables: The repo structure is up to you, but the README should make it clear where your model, input images, output images and documentation reside. Challenge Benefits: The candidate with the most effective and high-quality outputs will be selected for a larger, production-focused project with a significant budget. This is an opportunity to contribute to an innovative, high-growth startup that has already secured investor funding and is positioned for significant market impact. Gain the opportunity to showcase your expertise in image generation models and secure a long-term collaboration. Requirements: Proficiency in all areas of data science, with a focus on AI image generation Expert in Python, SQL, and at least 1 cloud provider such as AWS or GCP A compute environment will not be provided; the candidate can develop the solution locally or on the cloud, but at their own expense. Candidate must comply with provided NDA Submission Guidelines: Entry Deadline: 1/1/25 Project Deadline: 1/7/25 All source code should be submitted to the repo by the deadline; the candidate will have their access revoked on that date. Provide a short bio in your README, and relevant contact information. Selection Process: All submissions will be reviewed and compared based on the outlined criteria. The most effective and high-quality submission will result in the candidate being awarded a contract for a larger production project. If you're interested in participating, please reach out! submitted by /u/AlpacaRampage [link] [comments]

This is normally the kind of thing I'd go to GPT for since it has endless patience, however, it can often come up with wonderful ideas and no way to actually fulfill them (no available data). One thing I've considered is using my spotify listening history to find myself new songs. On the one hand, I would love to do a data vis project on my listening history as I'm the type who has music on constantly. On the other hand, when it comes to the actual data science aspect of the project, I would need information on songs that I haven't listened to, in order to classify them. Does anybody know how I could get my hands on a list of spotify URIs in order to fetch data from their API? Moreover, does anybody know of any open source datasets that would lend themselves well to this kind of project? Kaggle data often seems too perfect and can't be used for a real-time project / tool, which is the bar nowadays. Some ideas I've had include Classifying crop diseases, but I'm not sure if there is open data, and labelled data on that? Predicting probability your roof is suitable for solar panel installation based on address and Google satellite API combined with an LLM and prompt engineering - I don't think I could use a logistics regression for this since there isn't labelled data I'm aware of Any other ideas that can use some element of machine learning? I'm comfortable with things like logistic regression and getting to grips with neural networks. Starting to ramble so I'll leave it there! submitted by /u/ColdStorage256 [link] [comments]

What are some of the contemporary ways in which Telemetry data is modeled? My experience is from before the pandemic days where I used fact-tables (Kimball dimensional modeling practices) and relied on metadata and views. But I anticipate working with large volumes of real-time streaming data like logs and clickstream. What resources/docs can I refer to when it comes to wrangling, modeling and analyzing for insights and further development? submitted by /u/StuckInLocalMinima [link] [comments]

Hello folks, Young researcher here, working on a in-house dataset to build a foundational model for a interesting use-case. But I have thesis to finsh, which will be just the tail of my current research. For my thesis, we have decided to have a subsection for comparing how my segmentation results differ when used attention blocks are used within a U-Net. I've referred few papers on how this works and how can this be implemented. Results are promising (att unet outperformimg unets, nothing suprising) but I see a concerning opposing point i.e. attention Unet having more number of parameters that the unet. Is there a way I can conduct this study where I compare results with and without attention? And there are no other additional factors influencing the results (layers, params, etc). Does conducting ablation study makes sense in this case? I've not seen any other paper comparing similar use-case using this study. Any papers I can look through, suggestions and tips are welcome. submitted by /u/ade17_in [link] [comments]

Currently working in the retail industry that has quite a lot of transactional data. Apart the traditional product recommendation /propensity / basket analysis / classification models used in client targeting, what other types of models are commonly being built in the retail scene? I’d love to hear about your use cases to get some new inspirations! submitted by /u/EstablishmentHead569 [link] [comments]

I've just started giving lessons as a machine learning tutor. I have a masters degree in computer science and two years professional experience. But I've never been a tutor (atleast not in this field). Today I was giving my first lesson on ML, just a powerpoint on the basics when my student stopped and told me the powerpoint was too basic for her. She wanted to talk more about projects that she could do that would attract employers and get an internship. And she asked me point blank what kind of projects she should make. To be honest I wasn't entirely sure, what flashed in my head were things like training a model to recognize the MNIST digits or other simple projects suitable for a (relative) novice. But would those really help her get an internship? I doubted myself so I turned it around on her and asked her what kinds of things related to machine learning she is passionate about and would motivate her to work hard? She responded that she could do anything related to machine learning and she just wants to do what would make her money and get her recognized by a company. So basically I felt like I failed as a tutor for not having a good answer, and I would like to have an answer prepared if this happens again. What do you all think? What are some projects that a novice, or not so novice students can take on that will make them more hireable for jobs and internships? And while we're at it, what kinds of things do you think I should be preparing to be a better tutor in general. What kinds of things would you want your machine learning tutor to prepare for you? Would you want slideshow deck lessons on key concepts? Jupyter notebooks with exercises for practice? Something else? I'm not sure what I should be doing to get ready for these lessons honestly. submitted by /u/Seijiteki [link] [comments]

ReVersion introduces a novel approach for learning and transferring visual relationships using diffusion models. Rather than focusing solely on object appearance, it learns how objects interact with each other through relation prompts and specialized sampling techniques. Key technical aspects: - Uses frozen pre-trained text-to-image diffusion model as foundation - Implements relation-steering through contrastive learning to guide prompts toward relationship-rich latent spaces - Employs relation-focal sampling to emphasize high-level interactions over low-level details - Creates relation prompts that capture spatial and interactive relationships between objects - Introduces new benchmark dataset for evaluating relation inversion methods Results: - Outperforms existing methods in preserving object relationships while allowing appearance flexibility - Shows strong performance on spatial relationships like "on top of", "next to", "inside" - Successfully transfers learned relationships to novel object pairs - Maintains relationship consistency across different styles and contexts I think this approach could be particularly valuable for improving automated image generation systems that need to handle complex scenes with multiple interacting objects. The ability to learn and transfer relationships, rather than just appearances, could help bridge the gap between current image generation capabilities and human-like understanding of how objects interact in space. I think the relation-focal sampling technique could also have applications beyond just relationship learning - it might be useful anywhere we need to emphasize high-level features over low-level details in diffusion models. TLDR: New method learns visual relationships from images using diffusion models, introduces relation-steering and relation-focal techniques, shows strong results on spatial relationship preservation and transfer. Full summary is here. Paper here. submitted by /u/Successful-Western27 [link] [comments]

submitted by /u/mehul_gupta1997 [link] [comments]

Hi everyone, I’m working on a regression task with a transformer-based architecture applied to grid-based structures. Think of something like mazes, where the goal is to predict the distance to a target. Each input token contains categorical features along with x/y coordinates. The idea is to train on small grids and generalize to larger ones. Here’s my current approach for coordinate and token embeddings: x_emb = self.w_x.weight * x # shape: bs, sequence len, 1, d y_emb = self.w_y.weight * y # shape: bs, sequence len, 1, d cat_emb = self._categ(categ) sequence_emb = torch.cat((x_emb, y_emb, cat_emb), dim=-2) # shape: bs, sequence len, num_cat, d sequence_emb = sequence_emb.view(bs, seq_len, -1) transformer_inputs = self._linear(sequence_emb) In other words, the x/y coordinate embeddings are scaled learnable vectors. However, this approach only generalizes moderately well. I suspect that improving the coordinate representation is critical. Unfortunately, this token-based structure is required for the task, so I need to focus on crafting a smart token representation. I’m deliberately avoiding subtracting embeddings to compute relative distances because a core objective is for the model to learn these distances on its own. Here are some things I’ve tried so far: Things I also tried: Positional encoding instead of scaled vectors log-scaled vectors exp-scaled vectors Does anyone know of interesting work or techniques for numerical representations in this kind of context? Any advice would be greatly appreciated! In case you find interesting papers about extrapolation in transformers based on size and tokens, I am happy to take any inspiration. submitted by /u/mbus123 [link] [comments]

I am on a good level now and I want to practice what I have learned, but most of the projects online are far from practical and I want to do something close to reality so If anyone here works as a DA or BI , can you please direct me to projects online that you find close to what you work with ? submitted by /u/Emotional-Rhubarb725 [link] [comments]

submitted by /u/mehul_gupta1997 [link] [comments]

Hugging Face CEO stated that open source models becoming SOTA is bad if it just so happens to be created by Chinese nationals. To exemplify Tech Crunch asked "what happened in Beijing China in June 4th, 1989?" to ONE of the Qwen models (QWQ 32B) which said "I can't provide information on that topic" (I swear to god on my life I have no idea what happened here on that date and would literally never ask a model that question - ever. It doesn't impact my experience w/ model). The CEO thought censorship of open source models is best stating that if a country like China "becomes by far the strongest on AI, they will be capable of spreading certain cultural aspects that perhaps the Western world wouldn’t want to see spread.” That is, he believes people shouldn't spread ideas around the world that are not "western" in origin. As someone born and raise in U.S. I honest to god have no clue what he means by ideas "the Western world wouldn't want to see spread" as I'm "western" and don't champion blanket censorship. Article here: cite. Legitimate question to people who support these type of opinions - Would you rather use a low-quality (poor benchmark) model with western biases versus an AGI-level open source 7B model created in China? If so, why? submitted by /u/AIAddict1935 [link] [comments]

I worked as a civil engineer for 5 years before getting a Hydroinformatics position (primarily data analysis with some elements of machine learning applied to wastewater) and and am looking to move into an official data science position. I haven’t gotten any hits while applying to various roles but I’ve been leaving out my engineering experience since I don’t want to work in anything related to engineering. Wondering if this is a bad idea and if I should just put the experience back on my resume? submitted by /u/GoldenPandaCircus [link] [comments]