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Can training of nonlinear RNNs be efficiently parallelized, ensuring fast convergence even on very long time series from chaotic systems? In our #NeurIPS2026 spotlight “Parallel-in-Time Training of Recurrent Neural Networks for Dynamical Systems (DS) Reconstruction (DSR)” (preprint:…
I rarely write about benchmarks; a competitor always beats them next week. But I care about 'Leaps'. Gemini 4 Argon feels like one to me.
My paper just got accepted for a poster presentation at the Muslims in ML (MusIML) workshop at NeurIPS 2026 which will be held in Sydney, Australia. I'm really proud, but I'm a UG student and completely lack the funds for travel, registration, and accommodation.
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Over the past few years, I’ve submitted several papers to top-tier conferences such as NeurIPS, ICLR, and CVPR, and one concern that seems to come up repeatedly is 'novelty'.
I have responded to all the reviewers with proper rebuttals and modified draft. One interacted with me and after acknowledging the response kinda disappeared again after asking additional questions. I have replied to his additional questions too but he hasn't raised his score.
I started mapping the building blocks shared across all the models in audio.cpp. The result ended up being more interesting than I expected. Qwen has become by far the most common language backbone in this collection: 32 audio model families use a Qwen-family architecture, and 20 of them use Qwen3…
Tokenization is a wildly understudied area of language modeling despite it having effects across all of NLP. Over the past ~8 months, 32 (!) tokenizer researchers put together the most comprehensive survey of the field.
More recently, I am seeing a lot of posts on the sub regarding the workshop acceptance, with lack of funds to attend the conference. I unfortunately want to just say that workshop papers are not given any importance in the community.
Hello all, I built a radar object classifier on RadarScenes, extending a prior single-scan classifier to accumulate observations over a tracked object's history instead of classifying each scan in isolation.
**BACKGROUND** My partner and I have spent the last year building an architecture layer that separates probabilistic intelligence from deterministic authority. We call it IQRAX.
I got accepted for poster presentation at the IAB workshop in Sydney but I won't be able to attend due to a lack of financial support. I wrote the paper as a solo author, and for financial support from my institute, I need to add a supervisor from my college as an author to qualify for assistance.
I do research in neuro-symbolic AI, and like many of you, I was amazed by OpenAI’s recent formal proof of the 3D Navier-Stokes blow-up in Lean 4. Having an AI build a full mathematical proof that compiles with zero errors is a huge milestone for automated reasoning.
I am currently using the dataset CICIDS2017 to train an Isolation Forest model for anomaly detection, I am currently using a split that consists of 70% of ONLY BENIGN traffic for training, 15% BENIGN and 50% attacks for validation and the rest for testing, I used the validation to test with some…
TL;DR what is the right interface for writing custom real-time LLM agents? I've noticed many projects (academic, startups, pet, etc) are trying to make LLMs work in real-time world where things happen while the model is thinking.
Background: I am about to graduate with my PhD next year in Computer Science from an R1 university. My research background is AI in finance.
I work at ORTUS AI (we develop video analytics). We needed to tell from a single CCTV frame whether a camera had been rotated or installed at an angle (or upside-down), so we tried different models for that, without much luck (low accuracy, lots of false positives on regular camera-like frames).
It is a couple hours past the EOD deadline for the workshop, but as of yet I still haven't recieved an indication of whether my paper has been accepted or not. I got a review early yesterday but nothing else. Is there anyone else in the same situation?
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