标签：#learn

Hello I am curious about the election criteria for ICML financial aid. If anyone have been granted financial aid would you mind sharing your profile. Somehow being a black woman ( 2 underrepresented groups) with one paper accepted at the main conference and two papers accepted at different workshops is not enough to get financial aid. Are they solely for oral papers perhaps ? Last year a colleague (white man) with one spotlight paper did not get it neither. Maybe you need to belong to all minority groups ( half black half Latina Native American woman lgbtq+ and so on so far and what have you) with at least 3 oral papers 💀💀 to get any sort of help? The selection process and criteria should have more transparency cause chances are people in those committees are just giving the money to their own student and postdocs. submitted by /u/DazzlingPin3965 [link] [留言]

I'm running into an issue with an ablation study for a paper I'm preparing. I trained a model. The model achieved my best result, and I saved the trained checkpoint ( .pth file). Now my supervisor wants me to perform an ablation study by removing components and how it impacts the accuracy. My concern is that if I retrain from scratch, the accuracies will not exactly match the original run due to randomness, different seeds, etc. is there any way i can do the ablation study without retraining? I'd appreciate hearing how others have handled this situation in publications or thesis work. please help me out submitted by /u/Plane_Stick8394 [link] [留言]

Initially, I developed this so I can easily switch between different Attention mechanisms for my Small Language Model (SLM) experiments and benchmarking. However, I also realized that these implementations can be applicable in Computer Vision, modernize Vision Encoders, RL, and others. I hope this helps researchers, students, or educators in general. I also included MiniMax M3's sparse attention. This can be integrated with Andrej Karpathy's autoresearch framework. For contributing: I encourage you to please open a PR. I would like to see and learn implementations of other attention mechanisms I haven't covered in this repo. Thank you! GitHub Link: https://github.com/egmaminta/attnhut submitted by /u/AnyIce3007 [link] [留言]

The chatbot is tricking me!!! 💬📜⌛ When you text a chatbot, it doesn’t actually remember who you are or what you said two minutes ago. The exact millisecond it finishes typing a response, its brain completely wipes clean. To pull off the illusion of a continuous, flowing conversation, the web application secretly copy-pastes the entire past chat history, bundles it up, and blasts that whole massive block of text back into the processor every single time you hit send. Your "chat session" is an illusion maintained entirely by an ever-growing stateless prompt wrapper. You aren't interacting with a growing, adapting mind; you are repeatedly gas-lighting a brand-new entity into believing it has been talking to you for an hour. Wait, I am the one training it ??? 🚦🚸🚲 AI models are inherently blind to context; a computer doesn't instinctively know that a specific cluster of raw pixel values represents a real-world object. It requires billions of examples to be manually labeled by a human mind before the math can understand it. Every time you click on squares containing "traffic lights," "crosswalks," or "bicycles" to unlock a website, you are acting as an unpaid data annotator. You are manually labeling complex, messy real-world data points that feed directly into the computer vision systems of autonomous vehicles. The grand paradox of modern cyber security is that we force humans to act like mechanical data annotators to prove they are not computers, all so that computers can learn how to perfectly impersonate humans. The supercomputer is stupider than a toddler... 🍓👶🏻🖥️ We assume AI read letters and words the same way human eyes scan a page. It doesn't—it is entirely alphabet-blind. Before text hits the AI's brain, a parser chops strings of text into numerical blocks called "tokens." For example, the word "strawberry" isn't seen by the model as ten distinct letters; it is compressed into numerical IDs representing chunked pieces like "straw" and "berry". Because it never s

ok so the model's been up on HF now (apache 2.0, ~12B BF16, any-to-any multimodal). community has already shipped a pile of quants: - GGUF: unsloth, bartowski, ggml-org, lmstudio-community - MLX: mlx-community has 4bit / 8bit / bf16 / nvfp4 - official: google/gemma-4-12B-it (BF16) and the -assistant variant still trying to figure out which combo is actually worth downloading. the "12B runs on your laptop" hype is loud but i haven't seen many concrete numbers. if you've got it running, drop: - hardware (chip / RAM / GPU) - which quant + which repo (e.g. unsloth Q4_K_M, mlx-community 4bit, etc.) - runtime (llama.cpp / ollama / lm studio / mlx-lm / vllm / transformers …) - tokens/sec - context length you've actually used in practice - what you're using it for (chat / code / OCR / vision / agent …) - one thing it does well + one thing it falls apart on genuinely curious where the floor is — does it actually work on 16gb or only 32gb+? is mlx noticeably faster than gguf on apple silicon in 2026? anyone using the multimodal side seriously, or is it text-mostly in practice? submitted by /u/Individual_Soil4641 [link] [留言]

Hey everyone! Welcome back to my Google Summer of Code (GSoC) journey. In my last post, I shared the story of how I got into open source and was selected for GSoC with NumFOCUS to work on the Neural Network Builder API Refactor project for sbi (Simulation-Based Inference). Since the official announcement, the past three weeks have been dedicated to the Community Bonding Period . It is designed to help contributors get to know their mentors, understand the community culture, and familiarize themselves with the codebase and tools. Here is exactly what I did during these past three weeks to get ready for the main coding phase! The Kickoff Meeting We started the bonding period with a great kickoff call on Google Meet. It was a joint meeting that included the mentors for both of the selected sbi projects, the selected GSoC candidates. We were also joined by the mentee who successfully completed the GSoC project for sbi last year! Everyone introduced themselves, and it was incredibly inspiring to meet the team face-to-face (virtually!) and hear about everyone's backgrounds. Having a former GSoC student there was a huge bonus, as they shared some great insights into what to expect in the coming months. Setting Up the Machine A big part of getting started is making sure the development environment is properly configured. During our meetings, we discussed the machine setup in detail to ensure both candidates had everything required to run and test the sbi codebase locally without any hiccups. Embracing AI Coding Assistants One of the most interesting discussions we had was about using AI coding assistants. In the modern development world, tools like these are becoming standard, and our mentors actually encouraged us to use them! However, they emphasized using them carefully and strictly following project guidelines. To help us get the most out of these tools without compromising code quality, the mentors shared some excellent Claude code tutorials and provided us with resour

A lot of recent progress in ML appears to come from scaling existing architectures rather than introducing fundamentally new ones. At the same time, there’s increasing emphasis on dataset quality, curation, and synthetic data pipelines. In practice, I’m trying to understand how this tradeoff looks in real systems: How much effort is typically spent on data cleaning and filtering vs model design?? Whether dataset quality improvements still yield larger gains compared to architectural changes?? How synthetic data is affecting training stability and generalization in practice?? In many applied settings, it seems like data constraints become the limiting factor before architecture does, but I’m not sure if that’s broadly true across domains. submitted by /u/Electrical_Mine1912 [link] [留言]

Last week, Toronto hosted Tech Week. A city-wide celebration filled with events and workshops...

For example, suppose I have a one-hour video and I provide it to ChatGPT or another AI model. If I ask complex reasoning questions about the video, which models are best suited for long-horizon video understanding and reasoning? Which models can produce the most reliable answers in this scenario? submitted by /u/Alternative_Art2984 [link] [留言]

I didn’t expect to care this much about language tools. I started messing around with two different projects, Linguaboard and Parley , mostly out of curiosity. What I got was a surprisingly clear look at two very different ways we interact with language as developers and builders. Linguaboard: translation as exploration, not just output Linguaboard isn’t trying to give you the translation. Instead, it feels more like it’s saying: “Here are several valid ways this could be expressed, pick what fits your intent.” That shift is subtle but important. Most translation tools optimize for a single “correct” answer. Linguaboard leans into ambiguity in a way that actually helps you understand nuance instead of hiding it. I found myself thinking less like: “What does this mean?” and more like: “How should this sound in context?” Parley: learning through interaction, not memorization Parley takes a completely different angle. Instead of treating language as something to decode, it treats it as something to use. You’re not just passively consuming translations, you’re engaging with patterns, context, and recall in a more active loop. What stood out to me is how quickly it shifts you out of “study mode” and into “usage mode.” It feels closer to building intuition than studying rules. The interesting contrast What I didn’t expect is how well these two complement each other: Linguaboard → helps you understand nuance and meaning Parley → helps you internalize and use language One is about interpretation, the other about retention through interaction. Put together, they highlight something a lot of dev tools miss: Language work isn’t one problem. It’s at least two: understanding, and using. Why this matters (especially for devs) If you’re building anything with multilingual UX, AI translation, or global audiences, you’ve probably hit this wall: Translation APIs give you “correct” text But correctness ≠ clarity, tone, or intent These tools made that gap feel very obvious to me. And o

A while back, when I was still job hunting, building mini-projects, and trying to figure out what I...

It’s confusing because they said applicants will be notified on 3rd June but also said you’ll be notified 2-4 weeks after the deadline (29th may) submitted by /u/Smol_pp001 [link] [留言]

I just finished my first year of undergrad, and I got my first first-author paper accepted to an ICML workshop! Super stoked, especially since I was lowk a crashout in high school I wanted to know if it is worth it for me to go? It's quite expensive, and I will be the only one in my lab in attendance, so I will be on my own. If I do attend, how would I best maximize this opportunity? I got an email saying main conference tickets would also be made available for accepted authors, so I would likely be able to attend that as well. What are the best ways to network, meet people, and make sure it's worth it? Also, I am applying for transfer for this next cycle, so any advice relevant to that is also appreciated. submitted by /u/YukiOnnaLake [link] [留言]

As the title says. I am not a reciprocal reviewer but I just noticed a clever prompt injection like they did in ICML for our submission. submitted by /u/Massive-Bobcat-5363 [link] [留言]

In deployed ML systems, data distribution drift seems unavoidable over longer time horizons. I’m trying to understand what approaches are commonly used in practice: Continuous retraining pipelines (fixed intervals vs trigger-based) Online monitoring for feature or prediction drift Use of shadow models or fallback models in production Human-in-the-loop review for edge cases In most real deployments I’ve seen discussed, retraining strategy seems more operationally constrained than model-related. Curious what approaches are actually working reliably in production environments and what tends to fail first. submitted by /u/Electrical_Mine1912 [link] [留言]

I recently had a submission desk-rejected from the NeurIPS 2026 Position Paper Track for an alleged AI-policy violation. After corresponding with the track leadership and reading their public blog post, I think the broader methodological issue is worth discussing here. The track used Pangram, a proprietary AI-text detector, as part of the desk-rejection process. I was told that the materials considered for desk rejection were: the detector output the authors’ AI-use attestation This creates a potential circularity problem. If a high detector score is used to judge the author’s attestation as inconsistent, and that inconsistency is then used to justify desk rejection, the detector is not just an aid. It becomes a decisive part of the adjudication process. The bigger issue is validation. The NeurIPS blog describes tests using Pangram audits, older ACM FAccT papers, synthetic AI-generated position papers, and manually edited samples. But the target population was NeurIPS 2026 Position Paper submissions, whose ground-truth authorship process is unknown. So the key question is: What is the false-positive rate of the final decision procedure on the actual target distribution? A false-positive rate measured on one distribution does not automatically transfer to another. If the actual submission pool produced a "surprisingly high flagged rate" (citation from NeurIPS blog post), that could indicate distribution shift / miscalibration. To sanity-check the detector’s behavior, I also ran Pangram on recent 2026 papers authored by NeurIPS Position Paper Track Chairs. Pangram returned scores including: 69% AI 45% AI 36% AI 24% AI I am not claiming those papers were AI-written. For me, Pangram’s outputs alone does not permit such a conclusion. And that is exactly the point. UPD: Here is NeurIPS original blogpost And here is the blogpost with the detailed critics submitted by /u/Asleep-Requirement13 [link] [留言]

I am trying to train an AlphaZero model for Othello on a 6x6-board. Having been warned that too little exploration during data generation can lead to models being overconfident and trapped in some tight region of the search tree, I started with the value c_puct = 4.0, and then reduced this to 3.5 after a few generations. Also, I added fairly peaked Dirichlet noise (alpha = 0.15) to the prior predictions at the root of each tree search, with the proportion epsilon = 0.25. The temperature was initially set to 1.0, and then reduced to 0.8 after 20 generations. Now, the models do improve in the sense that later models consistently beat earlier ones, but there is no significant improvement against the two benchmarks I use: classical MCTS, and a greedy agent. Against the latter, the models have a deplorably low win rate of less than 10%. As can be seen from the curve for the value loss on the validation data, the models don't seem to learn to predict values (which is why I have been hesitant to reduce c_puct further), but the prediction loss seems to behave more or less as it should. https://preview.redd.it/gjby4omfp35h1.png?width=640&format=png&auto=webp&s=4d2ba4716ade6ec4ce9b7f16605a2e6bd74c6baf I decided to test if the prediction targets become strongly peaked early on. For this, I compute the normalized entropies of these predictions, meaning that I divide the entropy by the log of the number of legal moves at the given game state. The plot below shows the mean values of these normalized entropies for the data sets created by the different generations of agents. https://preview.redd.it/5yk216zjp35h1.png?width=640&format=png&auto=webp&s=538f59f5da3671a20c0ef2e1afc1ec96da237107 Finally, I tested how the policy predictions of a fixed set of random game states vary with the models. Here, I have set the second model as a benchmark, and I compute the average Kullback-Leibler divergence between the predictions by the benchmark model and those by later models. This is display

I have been thinking about an alternative tokenization and representation scheme for language models and would be interested in hearing whether similar ideas have been explored before, as well as potential advantages or flaws. The core observation is that modern tokenizers (BPE, SentencePiece, etc.) primarily capture statistical structure in text. While this is highly effective, the resulting token assignments are not explicitly organized according to semantic relationships. Concepts that are semantically related may end up with completely unrelated token identifiers, and semantic structure is learned later through embeddings and training. The idea is to construct a tokenization scheme in which the symbolic representation itself carries semantic information. For example, instead of assigning arbitrary identifiers to concepts, we could learn a mapping from concepts to short character strings such that semantically similar concepts receive similar codes. A concept like “dog” might receive a code close to those assigned to “wolf” and “fox”, while more distant concepts such as “car” would receive codes that are farther away in the code space. One possible implementation would be: 1) Build a semantic graph using resources such as WordNet, embedding similarity, or a combination of both. 2) Learn a compact symbolic encoding for concepts. 3) Optimize the encoding so that distances between codes correlate with semantic distances in the graph. 4) Train language models directly on these codes. An extension of the idea is to treat a standard keyboard layout as a fixed geometric space. The keyboard itself is not semantically meaningful, but it provides a globally agreed-upon metric structure. The learned encoding could exploit distances between characters and positions when constructing semantic codes. For example, if two concepts are semantically close, their symbolic representations would differ only slightly. Ambiguous concepts could potentially occupy positions that reflect

I built a C++ implementation of Meta’s EnCodec using Eigen . Github: https://github.com/pfeatherstone/encodec.cpp Motivation: - A lightweight implementation of EnCodec with no runtime dependencies, in C++ - No ML runtime - Easy integration in CMake project - Maximum performance on single-thread What it supports: - State-of-the-art audio codec - Audio tokenizer - Performance comparable to or exceeding onnxruntime (in my tests) - Dynamic sizes (no batches though) - Weights are compiled into the binary. No need to worry about weights files I'm looking for some feedback. Thank you very much. submitted by /u/Competitive_Act5981 [link] [留言]

Hello everyone, I've been working on a PyTorch library for solving Differential Algebraic Equations (DAEs) that supports vectorized execution and GPU acceleration. The library implements several algorithms that are not currently available in the Python ecosystem, including Generalized-Alpha integration, Dummy Derivatives index reduction, and adjoint sensitivity methods for DAEs. My motivation was to enable differentiable DAE simulation workflows in PyTorch for applications such as system identification, scientific machine learning, and physics-informed modeling. I'd be very interested in feedback on the numerical methods, API design, and potential ML use cases. GitHub: https://github.com/yousef-rafat/torchdae submitted by /u/Otaku_7nfy [link] [留言]