We are university students and we're conducting a quick survey on students’ motivation to learn Artificial Intelligence and Modeling. The survey will take less than 10 minutes to complete. Here's the link to the survey: https://docs.google.com/forms/d/e/1FAIpQLSdS-xy53N9lDRlC_835A_E59VMjCPql0_HuihPYqaQ_nINSsw/viewform?usp=sf_link Your input would mean a lot to us! Thank you so much for your support and time. submitted by /u/ExamSensitive3076 [link] [comments]

annotateai automatically annotates papers using Large Language Models (LLMs). While LLMs can summarize papers, search papers and build generative text about papers, this project focuses on providing human readers with context as they read. Project: https://github.com/neuml/annotateai See the examples below. Annotate paper "Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks" https://preview.redd.it/2hb8gp679q6e1.png?width=1920&format=png&auto=webp&s=d3379dd478c3e9fdc941ff8fcb614000fb812d31 Source: https://arxiv.org/pdf/2005.11401 Annotate paper "HunyuanVideo: A Systematic Framework For Large Video Generative Models" https://preview.redd.it/epxsryr99q6e1.png?width=1920&format=png&auto=webp&s=c2b9dc08b31d14d621fc46dfe7658bf49d548f7d Source: https://arxiv.org/pdf/2412.03603v2 Annotate paper "OpenDebateEvidence: A Massive-Scale Argument Mining and Summarization Dataset" https://preview.redd.it/slwx6hta9q6e1.png?width=1920&format=png&auto=webp&s=1e0494f84c1d3004671afd00ab3bc567fbaadbd8 Source: https://arxiv.org/pdf/2406.14657 submitted by /u/davidmezzetti [link] [comments]

I'm dealing with a clustering over time issue. Our company is a sort of PayPal. We are trying to implement an antifraud process to trigger alerts when a client makes excessive payments compared to its historical behavior. To do so, I've come up with seven clustering features which are all 365-day-long moving averages of different KPIs (payment frequency, payment amount, etc.). So it goes without saying that, from one day to another, these indicators evolve very slowly. I have about 15k clients, several years of data. I get rid of outliers (99-percentile of each date, basically) and put them in a cluster-0 by default. Then, the idea is, for each date, to come up with 8 clusters. I've used a Gaussian Mixture clustering (GMM) but, weirdly enough, the clusters of my clients vary wildly from one day to another. I have tried to plant the previous mean of my centroids, using the previous day centroid of a client to sort of seed the next day's clustering of a client, but the results still vary a lot. I've read a bit about DynamicC and it seemed like the way to address the issue, but it doesn't help. submitted by /u/LaBaguette-FR [link] [comments]

In AI and professional workstations, NVIDIA's dominance feels like a suffocating monopoly. Their segmented product lines widen the gap between consumer and professional GPUs, particularly in VRAM, performance, and price. AI enthusiasts struggle with prohibitive costs for GPUs equipped with sufficient VRAM. The reliance on CUDA cores—a proprietary standard—further locks developers into NVIDIA’s ecosystem, stifling competition and innovation. NVIDIA’s control extends beyond hardware, as their CUDA platform discourages adoption of open, competitive solutions. This feeds a cyberpunk dystopia where corporations consolidate power, leaving consumers and developers with few choices. Why does the tech world remain complicit? Why aren’t we pursuing alternative hardware architectures or broader software compatibility beyond CUDA? AMD’s ROCm is a start, but more aggressive development and policy interventions are needed to challenge NVIDIA’s grip. Until when will this continue? Who will stand up for the end consumer? submitted by /u/SevenShivas [link] [comments]

This paper introduces a framework for analyzing and visualizing the branching decisions language models make during text generation. The key methodology involves tracking probability distributions across different sampling paths to understand how early choices affect downstream generation. Main technical points: - Developed metrics to quantify uncertainty at each generation step - Created visualization tools for mapping decision trees in generation - Analyzed how different sampling methods affect path divergence - Measured correlation between model confidence and generation quality - Identified clustering patterns in generation trajectories Key results: - Found that paths tend to cluster into 2-3 distinct trajectory groups - Early sampling decisions have outsized impact on final outputs - Uncertainty patterns vary significantly between sampling methods - Similar prompts can lead to dramatically different generation paths - Model confidence doesn't consistently predict output quality I think this work provides important insights into how we might better control text generation. The ability to map and understand generation paths could help develop more reliable sampling methods and better uncertainty estimates. I think the clustering of generation paths is particularly interesting - it suggests there may be ways to guide generation toward desired trajectory groups. This could be valuable for applications needing more predictable outputs. The methodology also reveals some concerning aspects about current sampling methods. The strong dependence on early decisions suggests we may need new approaches that better preserve generation flexibility throughout the sequence. TLDR: New framework for analyzing how language models make text generation choices. Shows that generation paths cluster into distinct groups and early decisions heavily influence outcomes. Could help develop better sampling methods and uncertainty estimates. Full summary is here. Paper here. submitted by /u/Successful-Western27 [link] [comments]

About a year ago, research papers talked about model collapse when dealing with synthetic data. Recently I’ve been hearing about some progress in this regard. I am not expert and would welcome your views on what’s going on. Thank you and have a fantastic day. submitted by /u/BubblyOption7980 [link] [comments]

I was looking into design patterns for Agentic AI and I could need some help to grasp the concepts. I read about ReAct and ReWOO. From ReWOO, I really liked the idea of having a planner that creates a blueprint of the work that needs to be done. I can imagine that this works well for a lot of tasks, and it optimizes token usage compared to ReAct. From ReAct, I like that it has a reflection/observation LLM, to decide whether the output is good enough or needs another pass through the agents. What I don't understand: Why does ReWOO not have a reflection component?? Wouldn't it be the best of both worlds to have the planner and the reflection? This was the first draft for my agentic AI prototype, and I think it has pretty obvious advantages. I think I am missing something here. submitted by /u/Mindless_Copy_7487 [link] [comments]

I’ve noticed that the time spent on hyperparameter optimization vary significantly, not just between industry and academia but also across different fields like NLP, computer vision, or reinforcement learning. I’m curious—what’s your experience? Is tuning something you prioritize heavily, or do you often settle for “good enough” configurations to move faster? What field / model type / applications do you think experience most(or least) bottleneck in workflow due to HPO? Are there any industry dependency around choosing HPO tools? For example, everyone in xx industry would pick Optuna as a go-to or everyone running xx experiments would use Sigopt. Would love to hear your experiences! Thanks submitted by /u/Maleficent_Ad5541 [link] [comments]

i am having an issue with evaluating my model because model.evaluate() returns an okay overall score in accuracy but the confusion matrix and classification report return 100% for one class and 0% for another, i am using cifar10 but only 2 classes from it. anyone know why this happens? is this overfitting i am not sure because i am getting a similar score as model.evaluate(0 in my training accuracy and same for loss (which is almost as high as the accuracy) submitted by /u/Affectionate_Pen6368 [link] [comments]

For a project I am currently trying to integrate an Autoencoder for feature extraction and an LSTM for classification of the reduced feature space. The problem I am encountering is on how to train the LSTM network. The AE produces 5 datapoints which is fed into the LSTM network. The trick now comes in on the training of the LSTM network and how the LSTM works. I want the LSTM to take into account the 5 parameters from the AE at time t as well as the parameters at t-1 and t-2. As far as I understand the LSTM does this automatically, or should it then be that the LSTM takes in a total of 15 parameters with each pair of 5 corresponding to one timestep of the AE? Any advice on LSTM would be great or how such training can be done in an efficient way. The AE is processing a time-series signal. submitted by /u/Sea_Onion41 [link] [comments]

We recently had a paper accepted to a conference (AAAI). We found out that the conference does not publish appendices so they recommend we upload the full paper (with appendix) to arXiv. This is something we were considering doing anyway since the paper would be available before the conference proceedings come out. My concern is that if someone decides to cite our work, they may either become confused or cite the arXiv rather than AAAI "version". Is there a "correct" or common way to handle this? Do arXiv uploads with the same title get indexed to "one manuscript" on google scholar? Also, are we allowed to use the conference template to upload? (This part might be conference dependent I suppose). I know it is common these days to upload to arXiv before hearing back from a conference (usually with a different title) but I think this is a slightly different situation as the paper is accepted and the uploaded version will be identical to the conference paper (though with an Appendix). Thanks in advance! submitted by /u/baghalipolo [link] [comments]

Hello, I have a project in which I detect anomalies on transactions data from ethereum blockchain. I have performed aggregated calculations on each wallet address (ex. minimum, maximum, median, sum, mode of transactions' values) and created seperated datafile with it. I have joined the data on all the transactions. Now I have to standardize data (I have chosen robust scalling) before machine learning but I have following questions regarding this topic: Should I actually standardize each feature based on its unique mean and iqr? Or perform scalling on the column that the calculations come from - value column and than use its mean and iqr to scale the calculated columns? If each feature was scaled based on its own mean and iqr should I do it before joining calculated data or after? submitted by /u/Wikar [link] [comments]

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

Presumably, the winner of the NeurIPS 2024 Best Paper Award (a guy from ByteDance, the creators of Tiktok) sabotaged the other teams to derail their research and redirect their resources to his own. Plus he was at meetings debugging his colleagues' code, so he was always one step ahead. There's a call to withdraw his paper. https://var-integrity-report.github.io/ I have not checked the facts themselves, so if you can verify what is asserted and if this is true this would be nice to confirm. submitted by /u/LelouchZer12 [link] [comments]

i can't find this information and if both are open source it make sense a compatibility layer , any of the two is already ported to the other platform?, if you can share info about nvidia too will be cool submitted by /u/mrnothing- [link] [comments]

Hi, I am sharing my recent work which allows arbitrary images to be positive pairs. Our finding is quite astonishing that two disparate images, e.g., a snake and a lamp, can be positive. Our work potentially broadens the applications of contrastive learning to deal with the "false positive" in which two views are not similar. We challenge the common sense in contrastive learning, that is, the positive pair design is critical. Our results prove that the feature selection is the key! Paper: https://arxiv.org/abs/2410.18200 submitted by /u/Miserable-Gene-308 [link] [comments]

General Discussion - now that they are about to be banned in the US, I'm becoming fascinated by the strength of their For You recommendations. To try and put some guard rails on what I mean, TikTok has shown itself to be able to match content to relevant audience at greater frequency and scale than any other app (YouTube included). Many creators can join the platform, post a single video, and have millions of views in 24 hours. This does happen on other apps, but TikTok seems to be the most consistent at scaling audience incredibly fast. What models might they be basing their system on? What about their models creates their competitive advantage? submitted by /u/No_Collection_5509 [link] [comments]

Hi, I am learning ML and this is my first project. I did a simple 100 LoC implementation of the Neural Style Transfer paper by Gatys et al. See https://github.com/TAOGenna/pytorch-neural-style-transfer https://preview.redd.it/x2udi76n2g6e1.jpg?width=939&format=pjpg&auto=webp&s=437bdda1683e9fd580a6b3d1d4dc2598b25079ff submitted by /u/TAO_genna [link] [comments]

I’ve been exploring the architecture of ResNet and its ability to train very deep neural networks effectively. While I understand that residual connections help mitigate issues like vanishing gradients and make training deeper networks feasible, I’m curious about the limitations of this approach when scaling to extremely deep networks, such as those with 1000 layers or more. From my understanding, a ResNet with, say, 100 layers might effectively function like a much smaller network due to the residual connections, which essentially "skip" layers and add outputs. However, wouldn’t this also mean that if a regular MLP struggles to scale beyond 15 layers, a ResNet might just shift this limit proportionally (e.g., struggling beyond 150 layers)? In other words, does ResNet fundamentally solve the problem of training extremely deep networks, or does it merely extend the depth at which issues start to reappear? I’d appreciate any insights you might have! TYSM! submitted by /u/Time_Celebration6058 [link] [comments]

This paper presents a grounded theory study of how red-teaming is conducted on Large Language Models (LLMs), based on interviews with practitioners. The researchers systematically analyzed practitioner approaches to identify common patterns, strategies and motivations in LLM red-teaming. Key technical points: - Used qualitative coding of interviews to develop taxonomy of red-teaming approaches - Identified 12 distinct attack strategies and 35 specific techniques - Found red-teaming requires manual effort rather than automation - Demonstrated importance of team collaboration over individual attempts - Established red-teaming as distinct from malicious attacks - Mapped common patterns in tester motivations and goals Main results: - Red-teaming strategies fall into categories like prompt manipulation, psychology-based attacks, and system limit testing - Successful testers adopt an "alchemist" mindset of systematic experimentation - Most practitioners are motivated by curiosity and safety concerns - Testing requires deep understanding of both technical and psychological aspects - Manual testing currently more effective than automated approaches I think this work provides an important foundation for developing more structured approaches to LLM safety testing. The taxonomy they've developed could help standardize how we evaluate and secure these systems. Their finding that manual testing remains superior to automation suggests we need much more work on automated testing approaches. I think the emphasis on non-malicious intent and safety motivations is particularly relevant as these systems become more widely deployed. Understanding how and why people conduct these tests helps distinguish legitimate security research from attacks. TLDR: First systematic study of LLM red-teaming practices, providing taxonomy of strategies and techniques based on practitioner interviews. Shows importance of manual testing and team collaboration, while establishing red-teaming as legitimate security research. Full summary is here. Paper here. submitted by /u/Successful-Western27 [link] [comments]