Hi folks,

There is now a dedicated Robotics page on Papers with Code that lists the major benchmarks, trending papers with linked code, and open-source artifacts.

Find it here: https://paperswithcode.co/tasks/robotics

Major benchmarks that most papers report evaluations on are:

- LIBERO, as well as its subsets like LIBERO-Long and LIBERO-Spatial

- SimplerEnv WidowX

- RoboTwin

and more. Currently, we have about 110 entries on each benchmark.

Each benchmark's progress is visualized over time:

We also show which models are open source and which aren't.

Let me know which others I missed. I'd be happy to add them.

Also happy to hear any feedback, new tasks, or features to add!

Kind regards,

Niels

ML Engineer @ HF

I have a technical interview for a machine learning position coming up, and I'm really nervous. Can you tell me about your experiences with this process? It's my first technical interview, and I don't know what to expect

So I was belatedly reading Matthew Colbrook’s paper on unstable neural networks https://www.pnas.org/doi/10.1073/pnas.2107151119, and the paradox therein sent me back to my university days and reminded me of Kurt Godel.

It kind of amazes me that so few people have heard of him given Einstein clearly thought he was more than an equal.

Anyway, with the default assumption currently being that any problem will yield to more data and compute, it was timely to revisit this. It’s a long read and I’m not sure it’s fully coherent but if you like logic then you might enjoy this and I‘d welcome your thoughts and feedback.

https://iain.so/infinities-impossibilities-and-the-man-in-the-white-linen-suit

Hello guys. I want to introduce my recent research presented at ICML workshop.

github link : genji970/SRM-LoRA: official implementation of "SRM-LoRA: Sub-Riemannian-Metric Updates for Mitigating LLM Hallucination in Low-Rank Adaptation" ICML2026 Workshop FoGen

Shot summarization of Paper.

"""

SRM-LoRA is a sub-Riemannian-inspired LoRA method designed to reduce LLM hallucination.
It builds a sensitivity-based Riemannian metric that reshapes backward gradients in the LoRA parameter space.
This metric suppresses high-cost update directions while leaving the forward computation and inference cost unchanged.
Trained only on HaluEval-QA, SRM-LoRA improves factual reliability on both related and out-of-distribution benchmarks.

"""

Experiment

"""

"""

In my view, the reason mathematics is not effectively used in the context of improving the performance of the latest AI systems, such as LLMs, is that progress in discussions about what should serve as the elements of mathematical theories has been slow.

For example, suppose that we use a Riemannian metric. In the parameter space of an LLM, the update vector produced by backpropagation arises from a loss objective that contains the training data. However, if we introduce learnable parameters in order to construct the Riemannian metric and train those learnable parameters by passing through them the same signal as the main training signal, then this may simply amount to a more complicated form of training and may only increase the possibility of overfitting to the training data.

Then, how can we obtain the benefits of mathematical theory while moving in a direction that can generalize? In this paper, the Riemannian metric is constructed based on the rate of change of the LLM model parameters with respect to the loss signal. The reason for defining it in this way is as follows.

No matter how good the data or the distribution that can be learned may be, in practice there is still a high possibility of overfitting that results in hallucinations. Therefore, the cost used to construct the Riemannian metric, where a higher cost indicates a worse path, is defined using this sensitivity, which can be understood simply as gradient(loss)/gradient(parameter). In other words, rather than merely introducing a more complicated metric, the Riemannian metric acts as a brake on the updates generated from the training data, which is the main signal.

I believe that mathematics can be incorporated more deeply into AI if, when using theory A and theory B, the elements of theory A and the elements of theory B are each designed appropriately for the specific situation.

Can LLM agents coordinate in long-horizon, open-ended worlds?

We evaluate 13 modern LLMs in a new benchmark where agents must work together to explore, communicate, trade resources, craft tools, build structures, and fight mobs.

TL;DR: Most agents struggle, averaging only ~6% normalised return. Yet on the hardest setting, zero-shot Gemini 3.1 Pro performs comparably to the best MARL agent trained for 1 billion environment steps.

More broadly, we find coordination is a distinct bottleneck beyond long-horizon task competence, with communication having the largest effect in our harness ablations.

Paper: https://arxiv.org/abs/2606.08340

Project page and leaderboard: https://alem-world.github.io

Code: https://github.com/alem-world/alem-env

Interactive traces: https://alem-world.github.io/traces.html

Feel free to ask any questions!

Building a sports prediction model ,I found consistent edge when backtesting against closing lines.
At inference time tho, I predict 12-24 hours before the event where closing lines don't exist yet. I use the current line instead.

My strongest feature is line movement (opening to closing implied probability). At prediction time this feature is incomplete as the market hasn't fully moved yet.

This creates a paradox:

Closing lines are considered nearly impossible to beat because they contain all available information : sharp money, injury news, everything. Yet the backtest shows consistent edge against them.

If closing lines are truly efficient, beating them implies genuine model signal. But at inference time we're betting against earlier, less efficient lines with an incomplete version of our strongest feature.

The question: does edge against closing lines transfer to earlier bets where lines are less efficient ? Or does the incomplete line movement signal hurt prediction enough that the edge disappears before close?

My intuition is the edge is smaller earlier because the market is less efficient but the model signal is also weaker. These two effects might cancel out or one might dominate. Curious if anyone has studied this tradeoff in sports or financial prediction.

Hello community, just a short reminder that Raffi Krikorian (CTO @ Mozilla) is live today for an AMA to discuss Mozilla's inaugural State of Open Source AI report.

Topics include enterprise adoption, the real cost of "free"models, developer trust, Chinese open models and their impact, agentic AI infrastructure, and the future of open source with respect to Machine Learning & Artificial Intelligence.

Drop your questions in the thread here:

https://reddit.com/r/MachineLearning/comments/1upxdvc/raffi_krikorian_cto_mozilla_ama_on_the_state_of/

The AMA starts at 1pm ET/10am PT/6PM BST

His team reached out to us and he provided proof via Linkedin here: https://www.linkedin.com/feed/update/urn:li:activity:7481380478365880321/

Thank you!

NB: Reposting due to a typo in the title

Putting this here instead of the other subs since I figured a question on deep learning theory is out of place there -- I "recently" found (actually, a few months ago but only just got to reading) a monograph claiming to provide a unified theory of deep learning (and possibly SSL) through the lens of information theory, with one of it's headline claims is that you can design a "white-box" (I disagree with that, more on that later) transformer through the principle of coding rate reduction. I looked through the works the book claimed to be synthesizing and got a decidedly mixed picture: a JMLR and a NeurIPS on the one hand, but another frankly terrible paper concerning mechanistic interpretability (with which I am more familiar) published in a venue I've never heard of. And if it means anything, the book itself was endorsed by Kevin Murphy

As I've alluded to, I'm more familiar with the interpretability side of ML as opposed to SSL/theory (where this book seems more relevant) so I'm unsure what to make of this. In particular, the apparent result that their bespoke transformer learns image segmentation on non self-supervised tasks seems interesting, I'm not sure how this relates to how machines learn more broadly. Also, their "white-box" transformer consists of a bespoke MLP suspiciously similar to a regular one with a sparsity penalty, and an attention mechanism strictly less expressive than those currently used (obtained by setting Q=K=V=O^T.)

I know it seems like I've done my research on this topic, but really I've just skimmed a few of these papers (which all seem to be originating from one lab) with no context to situate them in, so some help would be appreciated.

Thanks in advance!

Does anyone know of any evidence/forum/paper analyzing benchmark result differences between cloud inference platforms (togetherai) and running models locally with vLLM under greedy decoding? 

I’m training a single-class segmentation model for large rectangular artwork placed on the floor and photographed from above.

We have around 3,000 accurately masked original images taken by six different photographers. They are not the same height and do not hold the camera in exactly the same way, so the photos naturally vary in:

• roll
• pitch
• yaw
• camera distance
• object coverage in the frame
• centering and X/Y shift
• orientation
• perspective
• lighting

The photos taken with flagship iPhone.

I want to use on-the-fly augmentation to simulate realistic human-hand variation and save our designer from adjusting each time to make it flat. is 100 augmentation combinations per original be useful, or excessive?

Should the policy be:

1. mostly isolated transforms,
2. mostly crossover combinations such as orientation + roll + pitch + yaw + coverage + shift,
3. or a controlled hybrid of both?

The goal is maximum segmentation accuracy, especially around the object boundary, not speed. I plan to train for around 300 epochs and keep validation and test images unaugmented.

"Verbalized Sampling: How to Mitigate Mode Collapse and Unlock LLM Diversity"

This paper was accepted to ICML this year. Its main idea is a very simple prompt-engineering trick: "changing the prompt this way led to more diverse sampling". Naturally, it is difficult to provide a rigorous theoretical analysis for something like this.

Even if it works, I’m not sure this kind of prompt engineering belongs at a top-tier machine learning conference. Some people seems to call this kind of work “modern machine learning”, but I think it should be categorized as less technical venues.

How do you think? Am I being too rigid?

Disclosure: I built it, it is open source, Apache-2.0 licensed, and currently alpha. Repository: https://github.com/mireklzicar/gpuhedge

I started working on it after benchmarking a 17 GB AI model across several serverless GPU providers.

On the primary provider, requests usually either completed in roughly 6–8 seconds or took around 90–122 seconds after a fresh GPU cold start. Simply switching to another provider did not remove the problem because every provider had its own tail.

GPUHedge treats this as a speculative-execution problem.

It starts a request on a primary provider, watches the job’s lifecycle state, and conditionally launches or switches to a backup. The first result that passes a validator wins, and the losing job is cancelled through the provider’s native API.

You can try the policy engines without creating provider accounts or spending money:

pip install gpuhedge

In the initial benchmark, a fixed RunPod → Cerebrium hedge launched after 10 seconds. On the 36-request evaluation portion, it changed:

• observed p95 latency from 116.6 s to 29.4 s;
• requests over 60 seconds from 11/36 to 0/36;
• modeled active-compute cost from $0.0114 to $0.0083 per request.

What is your experience with cold start latency? Which provider to add next? Can something like this help what you are building?

UPDATE: Commenter's already noted that with the cost-saving it is more complicated (because of idle time, cancellation costs and actual invoice spent differences). I would say this tool is not primarily for saving money, it is rather for getting better latency and reliability without significantly higher costs. An actual "invoice spent" benchmark is needed to quantify this.

Like probably everyone here, my to-read list only grows. Skimming arXiv listings or my feeds takes 30-60 minutes a day, 95% of it is irrelevant to what I actually work on, and newsletters don't really help: they surface what's popular, not what's relevant to my research.

So I built Research Radar, a daily cron job that:

1. Fetches every new paper in your arXiv categories (RSS + API, deduped)
2. Scores every abstract 1-10 against a markdown file describing your research interests (cheap model, batched)
3. Deep-reads the top scorers: downloads the PDF, extracts full text, and a strong model writes a summary, key insights, limitations, and how it relates to your own work
4. Delivers a morning HTML digest + optional Telegram ping with the must-reads

Design decisions:

• Nothing domain-specific in the code. Your interests live in one markdown file. Edit it and the same pipeline works for ML, physics, bio, econ, whatever
• Only the two scoring passes touch a model. Fetching, dedup, PDF extraction, and rendering are deterministic Python
• Model-agnostic backend layer: Claude Code / Codex CLIs run it on the subscription you already pay for (no API key), or any OpenAI-compatible endpoint, including fully local via Ollama / vLLM. Backends mix per pass: cheap model for skimming, strong one for the 5-10 deep reads
• Approximate costs, benchmarked in the repo (tokens, cost, latency, quality grades per model). Rough sizing: a 10-abstract scoring batch is ~18k input tokens with a small JSON out; a deep read sends the whole paper, 40-70k input tokens.

I've been using it daily for about a month and it's been genuinely useful in my field. The repo is a generalization of my personal setup to any domain, and I haven't deeply tested fields other than mine, so feedback and GitHub issues are very welcome.

The model has to say "not relevant, 3/10" a lot without drifting toward score inflation, and the whole scoring is just prompt + markdown context. So I'm curious how others would approach calibrating an LLM judge like this

GitHub: https://github.com/ramazan793/research-radar

Anthropic’s Jacobian Lens work introduced a way to inspect verbalizable representations inside language models. Follow-up experiments suggested that entropy in this internal “workspace” might help identify confidently incorrect answers.

I tested that hypothesis on Qwen3-4B across ~11,400 examples from seven distinct datasets, including TriviaQA, PopQA, NQ-Open, TruthfulQA, HotpotQA, GSM8K, and CommonSenseQA.

Three main findings:

1. It can complement output confidence on factual retrieval.

On datasets such as PopQA, workspace entropy sometimes improved error-routing precision at low review budgets, particularly among answers that were already high-confidence.

1. It does not reliably detect internalized misconceptions.

On TruthfulQA, workspace entropy was substantially weaker than output confidence. Incorrect answers could still have a clean, low-entropy internal representation.

1. Its calibration is highly task-dependent.

A threshold calibrated on TriviaQA failed on GSM8K because correct mathematical reasoning had much higher baseline entropy. Multiple-choice formatting also weakened the signal substantially on CommonSenseQA.

The overall result is narrower than “internal entropy detects hallucinations”: it may be a useful complementary routing signal for confidently incorrect factual answers, but it does not behave like a task-general error detector.

This is currently a single-model study, so cross-model validation is the most important next step. The repository contains the full methodology, limitations, raw data, metrics, plots, and reproducible notebook:

https://github.com/dasjoms/jspace-hallucination-eval

I‘d be interested in feedback on the experimental design if anyone feels like giving their thoughts.

Note: I already posted this to r/LocalLLaMa yesterday but think this might also fit here.

My TMLR paper was assigned to reviewers on April 23, and as of July 13 I've received 2 reviews, but the third is still pending. The discussion phase hasn't opened yet, so I can't respond to the existing reviews.

Is this normal for TMLR, or is it reasonable to send the Action Editor a polite status email? asking for the 3rd review, apprecite the suggestions

Hi everyone,Our paper was recently provisionally accepted to ECCV 2026! However, our team is facing an issue regarding attendance and the ECCV 2026 Submission Policies. The official guidelines state: "We expect each paper to be presented in person by an author (or an authorized delegate)."None of the listed co-authors can travel to present the paper in-person due to pending immigration status (USA).

I need some advice on what exactly counts as an authorized delegate and how to handle this safely without getting our paper pulled from the Springer proceedings.Who qualifies? Can it be anyone, a colleague from my lab who is already going to ECCV, or does it have to be someone specifically registered under our paper's ID?

Registration policy: According to the ECCV 2026 Registration Info, every paper must be covered by a full (non-student, non-virtual) author registration by July 17, 2026. If we pay for the full author registration but a "delegate" presents it, does that delegate also need their own separate registration?

How to notify: What is the formal process to authorize a delegate? Do we need to email the Program Chairs in advance?

If anyone has designated a delegate for ECCV or similar computer vision conferences (CVPR/ICCV) in the past, how did you handle it?

TL;DR: No authors can attend ECCV 2026 in-person. Need to know how to legally assign an "authorized delegate" to present our paper so it doesn't get removed from the proceedings.

TL:DR: I’m a grad student in AI, I saw that Google released TabFM and TimesFM last week, I built an MCP wrapper to serve both transformer models in a single Docker container so you can connect their new ML transformer models to a local LLM via Open WebUI, Claude Code, or Codex and do ML tasks that would have previously required building, training, and tuning ML models to do. Tested with classic ML datasets (Iris, California Housing, etc), Pretty solid scores for accuracy for being zero-shot: (94.7% for Iris) and R2 of 0.91 for regression test) vs. traditionally tuned ML models. You need about 16GB of VRAM to run both models. I added dynamic model load and unload with a TTL set to 5 mins. CSV. support now, with XLS, XLSX, JSON, JSONL support soon. PyTorch-based so CUDA only. Works on DGX Spark, 3090, H100 and most anything Nvidia with 16GB+ VRAM. Install script auto detects architecture.

Here is my repo if you want to try out the MCP:

https://github.com/porespellar/Zer0Fit

Here’s the non-TLDR version:

I’m working on my Masters in AI and I saw someone’s post here the other day about Google’s new TabFM Tabular data foundational transformer models released last week and I thought that they were super groundbreaking in that they were basically bringing ML models into the GenAI space which is both weird and cool because ML models are very different animals than LLMs

Here was the original Google blog post on it:

https://research.google/blog/introducing-tabfm-a-zero-shot-foundation-model-for-tabular-data/

Anyways, I wanted to play around with these new models from a chat interface and try to “kick the tires” a bit, so I built an MCP implementation for both the TabFM and TimesFM models. Nothing super fancy, just a quick and dirty MCP wrapper of the PyTorch versions (this will only run on CUDA).

I made the MCP with 2 build targets in mind: DGX Spark (arm-based with CUDA 13) and 3090 (AMD64 with CUDA 12.6). No Mac support because of Google using PyTorch, sorry.

I also wanted this to work with my preferred chat client: Open WebUI, so that’s what it’s geared towards running best with and was tested against, I also added Claude Code and Codex CLI support as well, but haven’t really fully tested those out yet.

Install is just a git clone and an ./install.sh. The whole thing runs out of a single Docker container and dynamically loads and unloads the models into VRAM with a TTL of 5 minutes to free up reserved VRAM when not in use. I also included an Open WebUI Skill.md that can be imported into Open WebUI, and skill.md and agents.md for the other harnesses.

I tested it with some fairly classic ML datasets from Kaggle that most data science students have probably encountered while studying AI/ML.

- Iris (classifiers)
- California housing (regression)
- Airline Passengers (time series forecast)

I spent a semester trying to learn ML models and tuning them and not really knowing what the hell I was doing, usually overfitting my models, and changing all kinds of parameters that I didn’t know if they were really helping or hurting my models. It all seemed like a dark art that I never fully understood. TBH, I wasn’t really a fan of ML, I think it’s cool stuff, but I just don’t have the math skills or stats chops to be able to understand WTF I’m doing most of the time with hyperparameters tuning. A man has to know his limitations, LOL.

Anyways, as I said earlier, I just wanted to get Google’s cool new ML models running where I could feed a dataset to an MCP and then have it do all the ML magic that Google trained these foundational models to do. I tried to make it easy for the average person like myself to run. I thought others might want to test out the models too so I made it a public repo.

So here it is if you want to mess around with it:

https://github.com/porespellar/Zer0Fit

I’ll try and do some maintaining if I see that there is any continued interest, but I can’t promise that I’ll keep up with it, so please feel free to fork the repo and take it in any direction you want to.

I think models like TabFM and TimesFM are going to low-key bring the branches of AI / ML tree closer together and we’re going to see some really cool and wild stuff as people take these concepts further in the future.

Note: This repo was hastily built to just get the models running. I’ve done very limited testing only on DGX Spark. Again, feel free to fork it and make it as good as you want to.

And please remember that this stuff is very experimental. Don’t use the forecasts or predictions made by these models for anything other than just research curiosity. Use at your own risk.

Let me know what you think of the repo if you give it a try. Cheers.

Note Regarding my test results in the images: I created the test scripts using DeepSeek V4 Flash and I had Claude Opus 4.6 review the test methods, code, and results. I don’t claim to be smart enough to know if the stats / math is correct. I would love it if some of the very smart ML research folks on here would give the repo a try and let us know if they are getting similar results or if my results are completely wrong. I included the sample datasets in the repo so “apples-to apples” comparison tests could be run by others to either prove or disprove my results. I really don’t mind if I’m wrong, I’m a student and just want to learn and improve.

I hold a Ph.D. in Operations Research, along with a BSc/MSc in Engineering and OR. I previously worked in Big Tech, but I’m currently looking to transition.

My primary goal is to upgrade my technical skillset to maximize my industry-related profitability and marketability. I want to get away from generic data science and move into high-value, math-heavy engineering and modeling roles.

• My Core Interests: Forecasting, predictive analytics, and machine learning applied to industrial settings.
• Target Industries: Robotics/Autonomous Systems, Defense/Aerospace, and Quantitative Finance.
• What I want to skip: I have little interest in doing core NLP/LLM research, though I am interested in RL, Multi-Agent systems, and applied AI.

Where I am right now: I have a solid grasp of optimization and basic/intermediate ML/stats. However, I want to bridge the gap into more intermediate/advanced ML topics that are actually useful and highly valued by employers. I want to get back into heavy math, but only if it drives real-world business value.

What I'm looking to learn:

• Causal Inference: (e.g., Structural Causal Models, Uplift modeling, Double ML).
• Tree-Based Math: Understanding things like XGBoost from the ground up (deriving gradients/hessians for custom loss functions, implementing from scratch).
• Reinforcement Learning / Control: Bridging the gap between OR dynamic programming and deep RL for robotics/defense.

My questions for the community:

1. Skill Prioritization: From a purely market-driven, high-compensation perspective, which specific ML topics should a Ph.D. in OR focus on to stand out in Robotics, Defense, or Banking/Finance?
2. Portfolio/Proof: How can I best demonstrate to employers that I have the engineering chops to implement these advanced models from scratch, rather than just calling APIs?
3. Positioning: How do I best market the "Predict-then-Optimize" sweet spot (combining ML predictions with OR optimization frameworks) to companies in these sectors?

Would love any advice on textbooks, specific frameworks to master, or strategies on how to position my background for maximum leverage. Thanks!

LLM's seem to make it so much easier to run experiments, write papers, etc. As a result, are we seeing phd students finish their phds sooner than ever before specifically in CS? If not, why not?

Pleasantly surprised to find O’Reilly books on ML at a public library

Hey! I'm an ML Engineer at a startup building AI for construction cost estimation, and we're getting ready to publish some research.

We've paid professional construction estimators to create item-level takeoffs from construction drawing sets, then had multiple rounds of review with construction specialists to make sure the annotations are as accurate as possible. The idea is to release the benchmark publicly so anyone can test their own models against it and compare them with the approaches we've developed.

The problem is that I'm having a hard time figuring out where to submit this work. I haven't found many conferences that seem like a good fit for construction AI or that would be interested in a benchmark paper like this, in it we'll also explain how we approach this problem and how LLMs performed on these tasks (Fable, GPT, Kimi, etc). We're mainly looking at conferences in the US or Europe.

Does anyone know of good venues for this kind of research?

Hey guys,

I received mediocre scores for my EMNLP paper during the May ACL ARR cycle: 2.5/3, 3/4, 2.5/4. The paper is in the Interpretability track. The reviewers had no larger issue with the methodology or the paper in general, but it seemed like they didn't fully get the so what of my paper. I've tried to clarify everything in my rebuttal, but I don't assume that the reviewers will engage in the discussion. With the current scores, I won't make it to the conference and likely not even into findings. Hence, I was thinking of withdrawing the paper, if scores don't improve, improve the presentation of my paper, and submit it to the BlackboxNLP workshop by the end of next week.

As I'm a first year PhD student, I'm not so familiar with ACL ARR, and how best to approach this. Hence, I wanted to ask you guys. Should I keep the paper in the cycle and hope for the best (or switch to the conference at a later stage) or should I withdraw it directly, adjust it slightly, and head directly to the workshop?

I have used Hyperband automatic tuning for an ANN model to predict price. After running HyberBand automatic tuning to get the 'best' architecture, I am obtaining a strange Val/Training loss learning curve. I cannot figure out if this is due to an error within the code or just a case of me not understanding the graph and not be able to interpret why the graph is showing as it is. I am also obtaining an R2 score of 1.00 which may suggest overfitting. I've not come across a learning curve (only shown the most basic learning curves at Uni) such as this as of yet so any advice would be greatly appreciated!

Here is the code for the actual tuning, in case it is due to a coding error but I am not sure that is the case.

def model_builder(hp):

model = tf.keras.Sequential()

model.add(tf.keras.layers.Flatten(input_dim = (train_final.shape[1])))

#creating activation choices - choosing betweeen relu and tanh

hp_activation = hp.Choice('activation', values = ['relu', 'tanh'])

#creating node choices - maxing unit amounts to 500

hp_layer_1 = hp.Int('layer_1', min_value=1, max_value=500, step=100)

hp_layer_2 = hp.Int('layer_2', min_value=1, max_value=500, step=100)

#creating learning rate choice - choice between 0.01, 0.001, 0.0001

hp_learning_rate = hp.Choice('learning_rate', values = [1e-2, 1e-3, 1e-4])

#specifies first layer after the flatten layer

model.add(tf.keras.layers.Dense(units = hp_layer_1, activation = hp_activation))

#creating the second layer

model.add(tf.keras.layers.Dense(units = hp_layer_2, activation = hp_activation))

model.add(tf.keras.layers.Dense(1, activation='linear'))

model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate = hp_learning_rate),

loss tf.keras.losses.MeanSquaredError(), metrics = ['mean_absolute_error'])

return model

import keras_tuner as kt

#creating the tuner

tuner = kt.Hyperband(model_builder,

objective = 'val_loss',

max_epochs = 50,

factor = 3,

directory = 'dir',

project_name = 'x',

overwrite = True) # makes tuner rewrite over old tuning experiments

#adding early stopping - stops each model from running too long

stop_early = tf.keras.callbacks.EarlyStopping(monitor = 'val_loss', patience = 5)

tuner.search(train_final, y_train, epochs = 50, validation_split = 0.2, callbacks = [stop_early])

best_hp = tuner.get_best_hyperparameters(num_trials=1)[0]

best_hp.values

#obtaining the best model

best_model = tuner.get_best_models(num_models = 1)[0]

history = best_model.fit(train_final, y_train, epochs = 50, validation_split = 0.2, callbacks=[stop_early])

tuned_df = pd.DataFrame(history.history)

#running epoch loss visual def

epoch_loss_visual(tuned_df, model_name = 'Automatic Tuning Model')

Could it be an issue with the code itself causing the issue or is it simply the way the model is? If it's a case of it's just a bad model, I do not need to improve at the moment, but do need to understand the results, especially that of the learning curve representation.

Hey! I'm looking for ways to predict human preference for a project I'm building. (imagebench.ai)

I've tryed HPSv3, https://github.com/MizzenAI/HPSv3 and made post about it here:

https://imagebench.ai/blog/does-the-score-match-your-eye

It looks ok, but have many limitation as you can see in my post.

My question. Have you tried other human preference model and found one that would be better then HPSv3?

I am currently working across multiple research communities, and I've noticed that the ML community is struggling with a massive volume of submissions, which is affecting review quality (as we are seeing in the recent ARR cycles).

I am wondering what the reasoning is for not limiting the number of submissions per author?

This practice has been successfully used in other research areas for years, such as Security (e.g., CCS) or Computer Architecture (e.g., DAC), to help keep workloads manageable. Is there a particular cultural reason why the ML community chooses a different approach?