Search:
Match:
11 results
Research#llm📝 BlogAnalyzed: Jan 3, 2026 06:29

Pruning Large Language Models: A Beginner's Question

Published:Jan 2, 2026 09:15
1 min read
r/MachineLearning

Analysis

The article is a brief discussion starter from a Reddit user in the r/MachineLearning subreddit. The user, with limited pruning knowledge, seeks guidance on pruning Very Large Models (VLMs) or Large Language Models (LLMs). It highlights a common challenge in the field: applying established techniques to increasingly complex models. The article's value lies in its representation of a user's need for information and resources on a specific, practical topic within AI.
Reference

I know basics of pruning for deep learning models. However, I don't know how to do it for larger models. Sharing your knowledge and resources will guide me, thanks

Research#llm📝 BlogAnalyzed: Dec 28, 2025 18:59

AI/ML Researchers: Staying Current with New Papers and Repositories

Published:Dec 28, 2025 18:55
1 min read
r/MachineLearning

Analysis

This Reddit post from r/MachineLearning highlights a common challenge for AI/ML researchers and engineers: staying up-to-date with the rapidly evolving field. The post seeks insights into how individuals discover and track new research, the most frustrating aspects of their research workflow, and the time commitment involved in staying current. The open-ended nature of the questions invites diverse perspectives and practical strategies from the community. The value lies in the shared experiences and potential solutions offered by fellow researchers, which can help others optimize their research processes and manage the overwhelming influx of new information. It's a valuable resource for anyone looking to improve their efficiency in navigating the AI/ML research landscape.
Reference

How do you currently discover and track new research?

Research#llm📝 BlogAnalyzed: Dec 28, 2025 04:00

Thoughts on Safe Counterfactuals

Published:Dec 28, 2025 03:58
1 min read
r/MachineLearning

Analysis

This article, sourced from r/MachineLearning, outlines a multi-layered approach to ensuring the safety of AI systems capable of counterfactual reasoning. It emphasizes transparency, accountability, and controlled agency. The proposed invariants and principles aim to prevent unintended consequences and misuse of advanced AI. The framework is structured into three layers: Transparency, Structure, and Governance, each addressing specific risks associated with counterfactual AI. The core idea is to limit the scope of AI influence and ensure that objectives are explicitly defined and contained, preventing the propagation of unintended goals.
Reference

Hidden imagination is where unacknowledged harm incubates.

Research#llm📝 BlogAnalyzed: Dec 27, 2025 21:32

AI Hypothesis Testing Framework Inquiry

Published:Dec 27, 2025 20:30
1 min read
r/MachineLearning

Analysis

This Reddit post from r/MachineLearning highlights a common challenge faced by AI enthusiasts and researchers: the desire to experiment with AI architectures and training algorithms locally. The user is seeking a framework or tool that allows for easy modification and testing of AI models, along with guidance on the minimum dataset size required for training an LLM with limited VRAM. This reflects the growing interest in democratizing AI research and development, but also underscores the resource constraints and technical hurdles that individuals often encounter. The question about dataset size is particularly relevant, as it directly impacts the feasibility of training LLMs on personal hardware.
Reference

"...allows me to edit AI architecture or the learning/ training algorithm locally to test these hypotheses work?"

Research#llm📝 BlogAnalyzed: Dec 27, 2025 20:31

What tools do ML engineers actually use day-to-day (besides training models)?

Published:Dec 27, 2025 20:00
1 min read
r/MachineLearning

Analysis

This Reddit post from r/MachineLearning asks about the essential tools and libraries for ML engineers beyond model training. It highlights the importance of data cleaning, feature pipelines, deployment, monitoring, and maintenance. The user mentions pandas and SQL for data cleaning, and Kubernetes, AWS, FastAPI/Flask for deployment, seeking validation and additional suggestions. The question reflects a common understanding that a significant portion of an ML engineer's work involves tasks beyond model building itself. The responses to this post would likely provide valuable insights into the practical skills and tools needed in the field.
Reference

So I’ve been hearing that most of your job as an ML engineer isn't model building but rather data cleaning, feature pipelines, deployment, monitoring, maintenance, etc.

Research#llm📝 BlogAnalyzed: Dec 27, 2025 16:32

[D] r/MachineLearning - A Year in Review

Published:Dec 27, 2025 16:04
1 min read
r/MachineLearning

Analysis

This article summarizes the most popular discussions on the r/MachineLearning subreddit in 2025. Key themes include the rise of open-source large language models (LLMs) and concerns about the increasing scale and lottery-like nature of academic conferences like NeurIPS. The open-sourcing of models like DeepSeek R1, despite its impressive training efficiency, sparked debate about monetization strategies and the trade-offs between full-scale and distilled versions. The replication of DeepSeek's RL recipe on a smaller model for a low cost also raised questions about data leakage and the true nature of advancements. The article highlights the community's focus on accessibility, efficiency, and the challenges of navigating the rapidly evolving landscape of machine learning research.
Reference

"acceptance becoming increasingly lottery-like."

Research#llm📝 BlogAnalyzed: Dec 27, 2025 05:00

Seeking Real-World ML/AI Production Results and Experiences

Published:Dec 26, 2025 08:04
1 min read
r/MachineLearning

Analysis

This post from r/MachineLearning highlights a common frustration in the AI community: the lack of publicly shared, real-world production results for ML/AI models. While benchmarks are readily available, practical experiences and lessons learned from deploying these models in real-world scenarios are often scarce. The author questions whether this is due to a lack of willingness to share or if there are underlying concerns preventing such disclosures. This lack of transparency hinders the ability of practitioners to make informed decisions about model selection, deployment strategies, and potential challenges they might face. More open sharing of production experiences would greatly benefit the AI community.
Reference

'we tried it in production and here's what we see...' discussions

Research#llm📝 BlogAnalyzed: Dec 25, 2025 22:11

Best survey papers of 2025?

Published:Dec 25, 2025 21:00
1 min read
r/MachineLearning

Analysis

This Reddit post on r/MachineLearning seeks recommendations for comprehensive survey papers covering various aspects of AI published in 2025. The post is inspired by a similar thread from the previous year, suggesting a recurring interest within the machine learning community for broad overviews of the field. The user, /u/al3arabcoreleone, hopes to find more survey papers this year, indicating a desire for accessible and consolidated knowledge on diverse AI topics. This highlights the importance of survey papers in helping researchers and practitioners stay updated with the rapidly evolving landscape of artificial intelligence and identify key trends and challenges.
Reference

Inspired by this post from last year, hopefully there are more broad survey papers of different aspect of AI this year.

Research#llm📝 BlogAnalyzed: Dec 25, 2025 22:26

[P] The Story Of Topcat (So Far)

Published:Dec 24, 2025 16:41
1 min read
r/MachineLearning

Analysis

This post from r/MachineLearning details a personal journey in AI research, specifically focusing on alternative activation functions to softmax. The author shares experiences with LSTM modifications and the impact of the Golden Ratio on tanh activation. While the findings are presented as somewhat unreliable and not consistently beneficial, the author seeks feedback on the potential merit of publishing or continuing the project. The post highlights the challenges of AI research, where many ideas don't pan out or lack consistent performance improvements. It also touches on the evolving landscape of AI, with transformers superseding LSTMs.
Reference

A story about my long-running attempt to develop an output activation function better than softmax.

Research#llm📝 BlogAnalyzed: Dec 25, 2025 22:23

Any success with literature review tools?

Published:Dec 24, 2025 13:42
1 min read
r/MachineLearning

Analysis

This post from r/MachineLearning highlights a common pain point in academic research: the inefficiency of traditional literature review methods. The user expresses frustration with the back-and-forth between Google Scholar and ChatGPT, seeking more streamlined solutions. This indicates a demand for better tools that can efficiently assess paper relevance and summarize key findings. The reliance on ChatGPT, while helpful, also suggests a need for more specialized AI-powered tools designed specifically for literature review, potentially incorporating features like automated citation analysis, topic modeling, and relationship mapping between papers. The post underscores the potential for AI to significantly improve the research process.
Reference

I’m still doing it the old-fashioned way - going back and forth between google scholar, with some help from chatGPT to speed up things

Community#General📝 BlogAnalyzed: Dec 25, 2025 22:08

Self-Promotion Thread on r/MachineLearning

Published:Dec 2, 2025 03:15
1 min read
r/MachineLearning

Analysis

This is a self-promotion thread on the r/MachineLearning subreddit. It's designed to allow users to share their personal projects, startups, products, and collaboration requests without spamming the main subreddit. The thread explicitly requests users to mention payment and pricing requirements and prohibits link shorteners and auto-subscribe links. The moderators are experimenting with this thread and will cancel it if the community dislikes it. The goal is to encourage self-promotion in a controlled environment. Abuse of trust will result in bans. Users are encouraged to direct those who create new posts with self-promotion questions to this thread.
Reference

Please post your personal projects, startups, product placements, collaboration needs, blogs etc.