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Hot data science related posts every hour. Chat: https://telegram.me/r_channels Contacts: @lgyanf
D Is non-DL related research a poor fit for ICLR?
I was one of the lucky people rejected from NEURIPS with 6444 scores but cranky AC, so looking to resubmit now. Since it got good reviews at NEURIPS, I'm considering submitting to ICLR incorporating suggested changes.
However, my paper proposes a linear dimensionality reduction technique, based on information geometry. It is my understanding that ICLR is very focused on neural networks and Deep Learning, so I am worried that my paper is not a good fit, so also considering AISTATS.
Is a novel linear dimensionality reduction technique too out of scope for ICLR? I am an outsider to the field, so would very much appreciate opinions.
/r/MachineLearning
https://redd.it/1nn56yu
D Monthly Who's Hiring and Who wants to be Hired?
For Job Postings please use this template
>Hiring: [Location\], Salary:[\], [Remote | Relocation\], [Full Time | Contract | Part Time\] and [Brief overview, what you're looking for\]
For Those looking for jobs please use this template
>Want to be Hired: [Location\], Salary Expectation:[\], [Remote | Relocation\], [Full Time | Contract | Part Time\] Resume: [Link to resume\] and [Brief overview, what you're looking for\]
​
Please remember that this community is geared towards those with experience.
/r/MachineLearning
https://redd.it/1n4jdo7
Tried building an explainable Vision-Language Model with CLIP to spot and explain product defects!
/r/deeplearning
https://redd.it/1n6lpte
AI research is drowning in papers that can’t be reproduced. What’s your biggest reproducibility challenge?
Curious — what’s been your hardest challenge recently? Sharing your own outputs, reusing others’ work?
We’re exploring new tools to make reproducibility proofs verifiable and permanent (with web3 tools, i.e. ipfs), and would love to hear your inputs.
The post sounds a little formal, as we are reaching a bunch of different subreddits, but please share your experiences if you have any, I’d love to hear your perspective.
Mods, if I'm breaking some rules, I apologize, I read the subreddit rules, and I didn't see any clear violations, but if I am, delete my post and don't ban me please :c.
/r/LanguageTechnology
https://redd.it/1mzxfkz
The best tools I’ve found for evaluating AI voice agents
I’ve been working on a voice agent project recently and quickly realized that building the pipeline (STT → LLM → TTS) is the easy part. The real challenge is evaluation, making sure the system performs reliably across accents, contexts, and multi-turn conversations.
I went down the rabbit hole of voice eval tools and here are the ones I found most useful:
1. **Deepgram Eval**
* Strong for transcription accuracy testing.
* Provides detailed WER (word error rate) metrics and error breakdowns.
2. **Speechmatics**
* I used this mainly for multilingual evaluation.
* Handles accents/dialects better than most engines I tested.
3. **Voiceflow Testing**
* Focused on evaluating conversation flows end-to-end.
* Helpful when testing dialogue design beyond just turn-level accuracy.
4. **Play.ht Voice QA**
* More on the TTS side, quality and naturalness of synthetic voices.
* Useful if you care about voice fidelity as much as the NLP part.
5. **Maxim AI**
* This stood out because it let me run *structured evals on the whole voice pipeline*.
* Latency checks, persona-based stress tests, and pre/post-release evaluation of agents.
* Felt much closer to “real user” testing than just measuring WER.
I’d love to hear if anyone here has explored other approaches to **systematic evaluation of voice agents,** especially for multi-turn robustness or human-likeness metrics.
/r/LanguageTechnology
https://redd.it/1mufzbv
Open Sourced Research Repos Mostly Garbage
Im doing my MSc thesis rn. So Im going through a lot of paper reading and if lucky enough find some implementations too. However most of them look like a the guy was coding for the first time, lots of unanswered pretty fundamental issues about repo(env setup, reproduction problems, crashes…). I saw a latent diffusion repo that requires seperate env setups for vae and diffusion model, how is this even possible(they’re not saving latents to be read by diffusion module later)?! Or the results reported in paper and repo differs. At some point I start to doubt that most of these work especially ones from not well known research groups are kind of bloated/dishonest. Because how can you not have a functioning piece software for a method you published?
What do you guys think?
/r/deeplearning
https://redd.it/1mt9osc
The AI Spam has been overwhelming - conversations with ChatGPT and psuedo-research are now bannable offences. Please help the sub by reporting the spam!
Psuedo-research AI conversations about prompt engineering and recursion have been testing all of our patience, and I know we've seen a massive dip in legitimate activity because of it.
Effective today, AI-generated posts & psuedo-research will be a bannable offense.
I'm trying to keep up with post removals with automod rules, but the bots are constantly adjusting to it and the human offenders are constantly trying to appeal post removals.
Please report any rule breakers, which will flag the post for removal and mod review.
/r/LanguageTechnology
https://redd.it/1mf7igt
Estimating Distance of Ships from PTZ Camera (Only Bounding Box + PTZ Params)
/r/computervision
https://redd.it/1mgp2gr
Is it possible to do something like this with Nvidia Jetson?
/r/computervision
https://redd.it/1ma4gvj
Computational linguistic
Hello everyone,
I'm a student from West Africa currently studying English with a focus on Linguistics. Alongside that, I’ve completed a professional certification in Software Engineering.
I’m really interested in Computational Linguistics because I want to work on language technologies especially tools that can help preserve, process, and support African languages using NLP and AI. At the same time, I’d also like to be qualified for general software development roles, especially since that’s where most of the job market is.
Unfortunately, degrees in Computational Linguistics aren't offered in my country. I'm considering applying abroad or finding some alternative paths.
So I have a few questions:
Is a degree in Computational Linguistics a good fit for both my goals (language tech + software dev)?
Would it still allow me to work in regular software development jobs if needed?
What are alternative paths to get into the field if I can’t afford to go abroad right away?
I’d love to hear from anyone who’s gone into this field from a linguistics or software background—especially from underrepresented regions.
Thanks in advance!
/r/LanguageTechnology
https://redd.it/1m3ikgl
D Self-Promotion Thread
Please post your personal projects, startups, product placements, collaboration needs, blogs etc.
Please mention the payment and pricing requirements for products and services.
Please do not post link shorteners, link aggregator websites , or auto-subscribe links.
\--
Any abuse of trust will lead to bans.
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.
\--
Meta: This is an experiment. If the community doesnt like this, we will cancel it. This is to encourage those in the community to promote their work by not spamming the main threads.
/r/MachineLearning
https://redd.it/1lpk8ib
PhotoshopAPI: 20× Faster Headless PSD Automation & Full Smart Object Control (No Photoshop Required)
Hello everyone! :wave:
I’m excited to share[ PhotoshopAPI](https://github.com/EmilDohne/PhotoshopAPI), an open-source C++20 library and Python Library for reading, writing and editing Photoshop documents (\*.psd & \*.psb) **without installing Photoshop** or requiring any Adobe license. It’s the only library that treats Smart Objects as first-class citizens and scales to fully automated pipelines.
**Key Benefits**
* **No Photoshop Installation** Operate directly on .psd/.psb files—*no Adobe Photoshop installation or license required*. Ideal for CI/CD pipelines, cloud functions or embedded devices without any GUI or manual intervention.
* **Native Smart Object Handling** Programmatically create, replace, extract and warp Smart Objects. Gain unparalleled control over both embedded and linked smart layers in your automation scripts.
* **Comprehensive Bit-Depth & Color Support** Full fidelity across 8-, 16- and 32-bit channels; RGB, CMYK and Grayscale modes; and every Photoshop compression format—meeting the demands of professional image workflows.
* **Enterprise-Grade Performance**
* 5–10× faster reads and 20× faster writes compared to Adobe Photoshop
* 20–50% smaller file sizes by stripping legacy compatibility data
* Fully multithreaded with SIMD (AVX2) acceleration for maximum throughput
**Python Bindings:**
pip install PhotoshopAPI
**What the Project Does:Supported Features**:
* Read and write of \*.psd and \*.psb files
* Creating and modifying simple and complex nested layer structures
* Smart Objects (replacing, warping, extracting)
* Pixel Masks
* Modifying layer attributes (name, blend mode etc.)
* Setting the Display ICC Profile
* 8-, 16- and 32-bit files
* RGB, CMYK and Grayscale color modes
* All compression modes known to Photoshop
**Planned Features**:
* Support for Adjustment Layers
* Support for Vector Masks
* Support for Text Layers
* Indexed, Duotone Color Modes
# See examples in[ **https://photoshopapi.readthedocs.io/en/latest/examples/index.html**](https://photoshopapi.readthedocs.io/en/latest/examples/index.html)
# 📊 Benchmarks & Docs (Comparison):
https://preview.redd.it/kqz7hmqtptaf1.png?width=1350&format=png&auto=webp&s=932a6e742e2d36650a66b801fda90156c89b0ecf
Detailed benchmarks, build instructions, CI badges, and full API reference are on Read the Docs:👉 [https://photoshopapi.readthedocs.io](https://photoshopapi.readthedocs.io/)
# Get Involved!
If you…
* Can help with ARM builds, CI, docs, or tests
* Want a faster PSD pipeline in C++ or Python
* Spot a bug (or a crash!)
* Have ideas for new features
…please star ⭐️, f, and open an issue or PR on the GitHub repo:
👉 [https://github.com/EmilDohne/PhotoshopAPI](https://github.com/EmilDohne/PhotoshopAPI)
Target Audience
* Production WorkflowsTeams building automated build pipelines, serverless functions or CI/CD jobs that manipulate PSDs at scale.
* DevOps & Cloud EngineersAnyone needing headless, scriptable image transforms without manual Photoshop steps.
* C++ & Python DevelopersEngineers looking for a drop-in library to integrate PSD editing into applications or automation scripts.
/r/computervision
https://redd.it/1lre73s
D Self-Promotion Thread
Please post your personal projects, startups, product placements, collaboration needs, blogs etc.
Please mention the payment and pricing requirements for products and services.
Please do not post link shorteners, link aggregator websites , or auto-subscribe links.
\--
Any abuse of trust will lead to bans.
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.
\--
Meta: This is an experiment. If the community doesnt like this, we will cancel it. This is to encourage those in the community to promote their work by not spamming the main threads.
/r/MachineLearning
https://redd.it/1l16j5k
Free Course Hero Unlocker 2025: What’s Actually Working Right Now?
Unlock Course Hero Docs Without Paying – Safe & Tested Methods
Hey friends 👋
If you’ve been scouring the internet for a working Course Hero unlocker, you’re not alone. I’ve been deep in the trenches trying different tools, reading Reddit threads, and testing what actually works in 2025 to get free Course Hero unlocks.
Some methods are outdated, others are sketchy—but a few are still solid, and I wanted to share what I found (and hear from others too!).
🔍 Top Working Methods to Unlock Course Hero in 2025:
1. 📥 Course Hero Unlocker via Discord
This is the one that stood out the most. A Discord server where you can get free unlocks for Course Hero, Chegg, Scribd, Brainly, Numerade, etc. No payment, just follow the instructions (usually involves upvoting or interacting).
✅ Free unlocks
✅ Fast response
✅ Covers multiple platforms
✅ Active community
2. 📤 Upload Docs to Course Hero
If you’ve got notes or study guides from past classes, upload 8 original files and get 5 unlocks free. You also get a shot at their $3,000 scholarship.
Good if you’ve already got files saved. Not instant, but legit.
3. ⭐ Rate Other Course Hero Docs
This is a low-effort option:
Rate 5 documents → Get 1 unlock
Repeat as needed. It works fine, but isn’t great if you need more than 1 or 2 unlocks quickly.
💬 Still Wondering:
Has anyone used the Discord Course Hero unlocker recently?
Are there any Course Hero downloader tools that are real (and not just fake popups)?
What’s the safest way to view or download a Course Hero PDF for free?
Any risks I should watch for when using third-party tools?
💡 Final Thoughts:
If you’re looking for the fastest and easiest Course Hero unlocker in 2025, I’d say check out the Discord server above. It’s free, responsive, and works for a bunch of sites. If you prefer official methods, uploading docs or rating content still works—but can be slow.
Let’s crowdsource the best options. Share what’s worked for you 👇 so we can all study smarter (and cheaper) this year 🙌
/r/deeplearning
https://redd.it/1lhpjqs
[P] Research Scientists + Engineers for Generative AI at NVIDIA
We’re hiring senior and principal research scientists to shape the future of generative AI at NVIDIA.
We're looking for builders with deep experience in LLMs and/or multimodal models. You’ll work on **training and deploying frontier-scale models**, designing next-gen model architectures, optimizing training stacks, and helping us **push the frontier of AI performance**.
We’re a tight-knit team with high standards, strong research instincts, and a bias for shipping.
Open roles:
* [**Senior Software Engineer, GenAI**](https://nvidia.wd5.myworkdayjobs.com/en-US/NVIDIAExternalCareerSite/job/Senior-Software-Engineer--Generative-AI_JR1997674)
* [**Principal GenAI Software Engineer**](https://nvidia.wd5.myworkdayjobs.com/en-US/NVIDIAExternalCareerSite/job/Principal-Generative-AI-Software-Engineer_JR1997454)
What we value:
* Deep understanding of transformer architectures, distributed training and optimization
* Using the scientific method for conducting methodical training experiments
* Data curation for pre-training and post-training
* Experience working with LLMs and/or large multimodal models
* A builder mindset — clean code, fast iterations, deep thinking
This is a rare opportunity to **help shape NVIDIA’s genAI stack from the ground up**. We work closely with software, optimization, deployment, and many other research teams, and have massive scale and resources behind us.
Feel free apply directly through the links.
/r/MachineLearning
https://redd.it/1lcmxeb
R NeurIPS rejected paper resubmission
My paper just got rejected (scores: 4, 4, 3, 3). I’m considering resubmitting it to IEEE SatML. What’s your opinion on SatML? Would it be better to aim for a journal like IEEE TIFS instead? Any other recommendations? I’m not really interested in ICLR since I feel it might get rejected there too. Field: AI Security.
/r/MachineLearning
https://redd.it/1nkrmzr
D Self-Promotion Thread
Please post your personal projects, startups, product placements, collaboration needs, blogs etc.
Please mention the payment and pricing requirements for products and services.
Please do not post link shorteners, link aggregator websites , or auto-subscribe links.
\--
Any abuse of trust will lead to bans.
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.
\--
Meta: This is an experiment. If the community doesnt like this, we will cancel it. This is to encourage those in the community to promote their work by not spamming the main threads.
/r/MachineLearning
https://redd.it/1n67lft
M4 Mac Mini for real time inference
Nvidia Jetson nanos are 4X costlier than they are in the United States so I was thinking of dealing with some edge deployments using a M4 mini mac which is 50% cheaper with double the VRAM and all the plug and play benefits, though lacking the NVIDIA accelerator ecosystem.
I use a M1 Air for development (with heavier work happening in cloud notebooks) and can run RFDETR Small at 8fps atits native resolution of 512x512 on my laptop. This was fairly unoptimized
I was wondering if anyone has had the chance of running it or any other YOLO or Detection Transformer model on an M4 Mini Mac and experienced a better performance -- 40-50fps would be totally worth it overall.
Also, my current setup just included calling the model.predict function, what is the way ahead for optimized MPS deployments? Do I convert my model to mlx? Will that give me a performance boost? A lazy question I admit, but I will be reporting the outcomes in comments later when I try it out after affirmations.
Thank you for your attention.
/r/computervision
https://redd.it/1n563nj
I built a tool to benchmark tokenizers across 100+ languages and found some wild disparities R
TL;DR: Created tokka-bench to compare tokenizers across languages. Turns out your fine-tune's multilingual performance might suck because of tokenization, not architecture. Also explains why proprietary models (Claude, GPT, Gemini) are so much better at non-English tasks.
Links:
[Live dashboard](https://tokka-bench.streamlit.app/)
Full blog post
[GitHub repo](https://github.com/bgub/tokka-bench)
https://preview.redd.it/7i03jela9elf1.png?width=1724&format=png&auto=webp&s=95378457970e6337b147e71d7a8f0ab2dd67cb91
# The Problem Nobody Talks About
I started this as a side quest while pretraining a multilingual model, but tokenization turned out to be way more important than expected. There are two hidden layers creating massive efficiency gaps:
UTF-8 encoding differences:
English: \~1 byte per character
Arabic: 2+ bytes per character
Chinese: 3+ bytes per character
Tokenization bias: Most tokenizers are trained on English-heavy data, so they allocate way more vocabulary to English patterns. These compound into serious problems.
# Why This Affects Performance
During training: If you allocate tokens proportionally (10M English, 1M Khmer), the Khmer text has WAY less semantic content because it needs more tokens per word. Plus Khmer tokens end up being character-level instead of semantic units, making concept storage much harder.
During inference: Low-resource languages need 2-3x more tokens per sentence:
Slower throughput (costs more to serve)
Context windows fill up faster
More chances to mess up during generation
# What I Built
tokka-bench measures four key things:
1. Efficiency \- bytes per token (compression quality)
2. Coverage \- unique tokens used (script representation)
3. Word splitting \- how often semantic units get fragmented
4. Subword fertility \- average tokens per semantic unit
# Interesting Findings
You can actually reverse-engineer training data from tokenizer performance:
Kimi K2: Exceptional Mandarin coverage (obviously Chinese-trained)
Gemma 3: Strong Urdu/Hindi performance
gpt-oss: Good Arabic/Gujarati coverage
Weirdest finding: Programming languages show almost identical efficiency across all tokenizers. Probably because everyone trains on GitHub with similar language distributions.
# Technical Details
Built on high-quality datasets (FineWeb, FineWeb-2, StarCoder). Samples 2MB per language and calculates per-language metrics. Has some limitations around cross-linguistic comparison due to UTF-8 differences, but great for comparing tokenizers on the same language.
Shoutout to Judit Ács for the original subword fertility metrics and Rust et al's ACL paper that laid the groundwork.
PS: if you're from an AI lab and want to contribute your tokenizer's metrics (even if proprietary), please reach out! The community would benefit a lot from understanding how SOTA systems handle this stuff.
Posted this on LinkedIn/Twitter already but figured r/MachineLearning would appreciate the technical details. Happy to answer questions about methodology or findings!
/r/MachineLearning
https://redd.it/1n0r8b7
D Conferences need to find better venues
Better = venues that are virtually accessible for any researcher/author to go to.
Just this morning, I'm denied the U.S. B1 visa. I'm supposed to present my work at ICCV 2025 in Hawaii. And during my in-person interview, the Visa Officer did not even bother to ask for the invitation letter.
This really blows cause it's supposed to be my first time and I was so excited about attending it. Would love to hear your thoughts about this.
/r/MachineLearning
https://redd.it/1mtfikh
HR have been using AI against you for years
HR scan, rank, and reject your CV without a single human glance.
They filter, using Ai all automatically.
Most decisions are made before an actual recruiter even sees your name.
---
For this reason I built **Laboro** to flip the game.
It applies to jobs for you:
scrapes listings from 70k+ company career pages, matches them to your real experience,
Then Laboro AI Agent opens the browser, finds the forms, understands the fields, and fills them out with your CV.
---
So now it’s AI vs AI.
Their algorithm decides who gets in, mine makes sure you get there.
That’s not cheating that’s leveling the field.
When both sides have the same tech power, the only thing left to decide is merit, no insider referrals, no gatekeeping, no “friend of the hiring manager”, Just skills vs the system.
---
If HR hates that, maybe it’s because for the first time… they don’t get to choose who plays.
/r/deeplearning
https://redd.it/1mops78
[D] Is modern academic published zero-sum?
It seems the current state of publishing in A* venues (CVPR, NeurIPS, ICML, ICCV/ECCV) is zero-sum. One person’s rejection is another person’s acceptance. Reviewers seem to reject papers just for the sake of rejection. There’s a sense that some reviewers reject papers not on substantive grounds, but out of an implicit obligation to limit acceptance rates. Rebuttals appear to be pointless as reviewers take stubborn positions and not acknowledge their misunderstandings during this period. Good science just doesn’t appear to be as valued as the next flashiest LLM/VLM that gets pretty results.
/r/MachineLearning
https://redd.it/1miq2y4
D - NeurIPS'2025 Reviews
Hey everyone,
NeurIPS 2025 reviews should be dropping soon (July 24th AoE), and I thought it might be a good idea to start a thread where we can share our thoughts, experiences, and reactions.
Feel free to post your initial impressions, any surprises (good or bad), questions about rebuttals, or just how you’re feeling about the process this year. Whether it’s your first submission or your tenth, you’re not alone in the rollercoaster.
Let’s keep things constructive and supportive. Good luck to all!
/r/MachineLearning
https://redd.it/1m74ugv
D Monthly Who's Hiring and Who wants to be Hired?
For Job Postings please use this template
>Hiring: [Location\], Salary:[\], [Remote | Relocation\], [Full Time | Contract | Part Time\] and [Brief overview, what you're looking for\]
For Those looking for jobs please use this template
>Want to be Hired: [Location\], Salary Expectation:[\], [Remote | Relocation\], [Full Time | Contract | Part Time\] Resume: [Link to resume\] and [Brief overview, what you're looking for\]
​
Please remember that this community is geared towards those with experience.
/r/MachineLearning
https://redd.it/1loqe5e
A few questions for those of you with Careers in NLP
I'm finishing a bachelor's in computer science with a linguistics minor in around 2 years, and am considering a master's in computational linguistics afterwords.
Ideally I want to work in the NLP space, and I have a few specific interests within NLP that I may even want to make a career of applied research, including machine translation and text-to-speech development for low-resource languages.
I would appreciate getting the perspectives of people who currently work in the industry, especially if you specialize in MT or TTS. I would love to hear from those with all levels of education and experience, in both engineering and research positions.
1. What are your top 3 job duties during a regular work day?
2. What type of degree do you have? How helpful was your education in both getting hired for your current position, as well as doing your actual work on a daily basis?
3. What are your favorite and least favorite things about your job? Why?
4. What is your normal work schedule like?
5. Are you remote, hybrid, or on-sight?
Thanks in advance!
/r/LanguageTechnology
https://redd.it/1m0unrj
object detection on edge in 2025
hi there,
what object detection models are you currently using on edge devices? i need to run real time on hardware like hailo 8l and we use models yolo and nanodet. has anyone used something like RF-Detr or D-fine on such hardware?
/r/computervision
https://redd.it/1luwkey
Made a Handwriting->LaTex app that also does natural language editing of equations
/r/computervision
https://redd.it/1lonr7c
The Company Banned By LinkedIn For Being Too Good At Getting Jobs
/r/deeplearning
https://redd.it/1liyd5d
Is applied NLP expertise still relevant in LLM Era?
In the era of LLM, does your company still train NLP models from scratch? Fine-tuning the pre-trained models (e.g: BERT) still counted as from scratch.
Or most of the use cases already can be solved by just calling LLM APIAI Agent/MCP/host your LLM by yourself?
Given the accuracy, I believe LLM already give you good baseline for common NLP use cases. You can tailor the needs by giving a good prompts based on your needs.
However, the current LLM solutions still far away from the perfect due to model hallucinations, system reliability (e.g: high latency), and the cost of using this tech still considered as high.
For the cost, it's still debatable as the business owners can choose whether to hire NLP experts or subscribe to these LLM APIs and let software engineer to integrate the solutions.
Assuming the LLM is getting better overtime, does applied NLP expertise still relevant in industries/markets?
NB: NLP expertise here as someone who can train the NLP model from scratch
/r/LanguageTechnology
https://redd.it/1lcps10
D The effectiveness of single latent parameter autoencoders: an interesting observation
During one of my experiments, I reduced the latent dimension of my autoencoder to 1, which yielded surprisingly good reconstructions of the input data. (See example below)
Reconstruction \(blue\) of input data \(orange\) with dim\(Z\) = 1
I was surprised by this. The first suspicion was that the autoencoder had entered one of its failure modes: ie, it was indexing data and "memorizing" it somehow. But a quick sweep across the latent space reveals that the singular latent parameter was capturing features in the data in a smooth and meaningful way. (See gif below) I thought this was a somewhat interesting observation!
Reconstructed data with latent parameter z taking values from -10 to 4. The real\/encoded values of z have mean = -0.59 and std = 0.30.
/r/MachineLearning
https://redd.it/1la6plp
