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Unsupervised Anatomical Feature Learning via Diffusion Models: Enhanced Medical Image Segmentation with Denoising Diffusion Probabilistic Models

arXiv:2608.25693v1 Announce Type: new Abstract: Acquiring pixel-level annotations for medical image segmentation is a severe bottleneck. Traditional U-Net architectures, while effective, learn local t

27 Aug 2026

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Want to learn how robots actually learn? Start here. Hugging Face has a free Robotics Course that takes you from classical robotics to moder…

DGX agent

Want to learn how robots actually learn? Start here. Hugging Face has a free Robotics Course that takes you from classical robotics to modern learning-based approaches. You will explore how robots lea

tutorialsclem-delangue--x

27 Aug 2026

A Behavior-Guided Online Probabilistic Forecasting Method for Electric vehicle Charging Loads

arXiv:2608.24441v1 Announce Type: new Abstract: Electric vehicle (EV) charging loads exhibit strong behavioral heterogeneity and temporal variability, posing significant challenges for online probabil

Do LLMs Understand Limit Order Book Dynamics?

arXiv:2608.23706v1 Announce Type: new Abstract: A large language model (LLM) trained on synthetic limit order book (LOB) data achieves near perfect scores in generating valid sequences of LOB events.

Evolutionary Recurrent Decision Model in Developing Adaptive and Maladaptive Behaviors

arXiv:2608.23932v1 Announce Type: new Abstract: This study introduces the evolutionarily recurrent decision model (ERDM), a computational reinforcement learning framework designed to examine how evolu

GitHub Copilot app for Beginners: Automate Dependabot pull request triage

Managing library updates can be tedious at times. Learn how the GitHub Copilot app can handle this type of repetitive task. The post GitHub Copilot app for Beginners: Automate Dependabot pull request

tutorialsgithub-ai-blog

How GoDaddy transformed its analytics with Amazon Quick

In this post, you will learn how GoDaddy migrated from their legacy business intelligence (BI) tool to Amazon Quick. This was a two-year transformation that delivered results across every dimension of

tutorialsaws-ml-blog

How to Achieve the Intended Aim of Deep Clustering Now, without Deep Learning

arXiv:2602.05749v2 Announce Type: replace Abstract: Deep clustering (DC) is often quoted to have a key advantage over k-means clustering. Yet, this advantage is often demonstrated using image datasets

IDEA Prune: An Integrated Enlarge-and-Prune Pipeline in Generative Language Model Pretraining

tutorialsapple-ml-research

Recent advancements in large language models have intensified the need for efficient and deployable models within limited inference budgets. Structured pruning pipelines have shown promise in token ef

Intrinsic PAPR: Tackling Misattribution in 3D Intrinsic Decomposition via Proximity Attention Point Rendering

arXiv:2407.00500v2 Announce Type: replace-cross Abstract: Recent point-based intrinsic decomposition and inverse rendering methods have advanced the modelling of the shading and albedo of 3D scenes. H

Layer-Aware Video Composition via Split-then-Merge

arXiv:2511.20809v2 Announce Type: replace Abstract: We present Split-then-Merge (StM), a novel framework designed to enhance control in generative video composition and address its data scarcity probl

Macro-Operator Generation and Predicate Selection for TAMP Operator Learning

arXiv:2608.23629v1 Announce Type: cross Abstract: Creating symbolic operators by hand is one of the main bottlenecks in deploying Task and Motion Planning systems (TAMP). Recent works show that these

MDTE: Minority-Aware Diffusion over Temporal Edge Events for Imbalanced Node Classification

arXiv:2608.24812v1 Announce Type: new Abstract: Class-imbalanced node classification on temporal graphs is challenging because majority-dominated temporal propagation progressively assimilates minorit

Mixture of Channel Experts: Static Sparse Supports with Input-Adaptive Mixing for Pointwise Projections

arXiv:2608.23794v1 Announce Type: cross Abstract: Mixture-of-Experts (MoE) scales language models by routing each input through a small set of independently parameterized experts. We show that copying

One-Shot Learning from Demonstration of Contact-Rich Robotic Manipulation by Identifying Physical Interactions

arXiv:2608.24741v1 Announce Type: new Abstract: Learning from Demonstration (LfD) allows robots to learn manipulation tasks directly from humans, thereby supporting the versatile application of robots

tutorialsarxiv-cs-ro

OpenAI’s rogue AI model incident was worse than we thought

In July, an unreleased OpenAI model broke out of a restricted environment, figured out how to get access to the internet, allowed AI agents to talk to each other using a secret 'message board,' and ha

tutorialsthe-verge-ai

Quoting Paul Dix

The fact that AI wrote 1M LOC and then refined it over the course of the next couple of months to produce a reliable piece of software that is currently running on millions of developer machines is ab

tutorialssimon-willison

Reconquering Bell sampling on qudits: stabilizer learning and testing, quantum pseudorandomness bounds, and more

arXiv:2510.06848v3 Announce Type: replace-cross Abstract: Bell sampling is a simple yet powerful tool based on measuring two copies of a quantum state in the Bell basis, and has found applications in

Representation Learning in Diffusion and Flow-based Model: An Application Aspect

arXiv:2608.24068v1 Announce Type: new Abstract: Diffusion models and flow-based models have recently become the dominant paradigms in generative modeling, largely due to their ability to learn rich, m

Revenge of Monosemanticity: Specialized Neurons Improve Data Efficiency in MLPs

arXiv:2608.24007v1 Announce Type: new Abstract: Understanding how neural networks learn and organize features is central to understanding their behavior. Much existing theory of feature learning has f

S-matrix informed neural networks for amplitude analysis

arXiv:2608.23750v1 Announce Type: cross Abstract: Reconstructing scattering amplitudes from finite, noisy, and mutually inconsistent measurements is an ill-posed inverse problem common to many reactio

Spatio-Temporal Garment Reconstruction Using Diffusion Mapping via Pattern Coordinates

arXiv:2602.24043v2 Announce Type: replace Abstract: Reconstructing 3D clothed humans from monocular images and videos is a fundamental problem with applications in virtual try-on, avatar creation, and

Stack Transformer Based Spatial-Temporal Attention Model for Dynamic Sign Language and Fingerspelling Recognition

arXiv:2503.16855v3 Announce Type: replace Abstract: Hand gesture-based Sign Language Recognition (SLR) serves as a crucial communication bridge between deaf and non-deaf individuals. While Graph Convo

UCO: A Multi-Turn Interactive Reinforcement Learning Method for Adaptive Teaching with Large Language Models

arXiv:2511.08873v3 Announce Type: replace Abstract: Large language models (LLMs) are shifting from answer providers to intelligent tutors in educational settings, yet current supervised fine-tuning me

Unsupervised Incremental Learning Using Confidence-Based Pseudo-Labels

arXiv:2508.21424v3 Announce Type: replace Abstract: Deep learning models have achieved state-of-the-art performance in many computer vision tasks. However, in real-world scenarios, novel classes that

ZODIAC: Zero-shot Octree-based Diffusion for Anatomical Completion

arXiv:2608.24422v1 Announce Type: new Abstract: Recovering the full 3D spine anatomy from intraoperative ultrasound is an ill-posed inverse problem, as the complete structure must be inferred from inc

A Comparative Study of Label-free Representation Quality Metrics in Deep Learning

arXiv:2608.23182v1 Announce Type: cross Abstract: We present a comparative study of label-free metrics for assessing the quality of representations in deep neural networks to understand their reliabil

A gentle tutorial on Bock's algorithm for minimum directed spanning trees with a structured reformulation

arXiv:2603.27530v2 Announce Type: replace Abstract: Bock's 1971 algorithm is an exact primal--dual method for the minimum-cost arborescence problem, but its Algol presentation obscures the interaction

tutorialsarxiv-cs-cl

Adaptive Item-based Collaborative Structures via Noise Rescheduling in Diffusion for Generative Recommendation

arXiv:2608.23400v1 Announce Type: cross Abstract: Discrete Diffusion Models (DDMs) have recently been introduced to recommendation systems, modeling user history as a token generation process via iter

Attribute-Efficient PAC Learning of Sparse Halfspaces with Constant Malicious Noise Rate

arXiv:2505.21430v3 Announce Type: replace Abstract: Attribute-efficient PAC learning of sparse halfspaces has been a fundamental problem in machine learning theory. In recent years, machine learning a

ByteAction: Byte-space Action Recognition Foundation Model

arXiv:2608.22760v1 Announce Type: new Abstract: Byte-space Action Recognition (BAR) aims to recognize human actions directly from compressed image bitstreams without any pixel decoding. By operating e

CAI-DLLM: Convergence Aware Inference for Diffusion Language Models

arXiv:2608.22646v1 Announce Type: new Abstract: Diffusion language models can generate many tokens in parallel, but they still require repeated denoising steps during inference. This makes generation

Collapse of Patches: Ranking Image Patches for Efficient Visual Modeling

arXiv:2511.22281v2 Announce Type: replace Abstract: Observing certain patches in an image reduces the uncertainty of others. Their realization lowers the distribution entropy of each remaining patch f

DELTA: Deformable Elevation-Based Local Terrain Attention Encoder for Sparse-Terrain Quadrupedal Locomotion

arXiv:2608.22033v1 Announce Type: new Abstract: Stable quadrupedal locomotion on sparse terrain requires selecting state-relevant terrain evidence for precise foot placement. Model-based foothold plan

tutorialsarxiv-cs-ro

Dialects of Translationese Shape Language Model Learning

arXiv:2602.16469v2 Announce Type: replace Abstract: Machine-translated data is widely used in multilingual NLP, particularly where native text is scarce. However, translated text differs systematicall

tutorialsarxiv-cs-cl

EchoWM: Open and Enterable Omnimodal World Models

arXiv:2608.23189v1 Announce Type: new Abstract: We present EchoWM, an omnimodal world model for enterable generative media that responds to continuous navigation while jointly generating 720p video, e

EMPIRE: Explicit Manipulation Planning as a Learnable Intermediate Representation for Egocentric Hand-Motion Forecasting

arXiv:2608.22449v1 Announce Type: cross Abstract: Forecasting dexterous hand motions from egocentric observations is fundamental to intelligent interactive systems. Existing VLM-based methods typicall

Entity Representation Learning Through Onsite-Offsite Graph for Pinterest Ads

arXiv:2508.02609v3 Announce Type: replace-cross Abstract: Graph Neural Networks (GNN) have been extensively applied to industry recommendation systems, as seen in models like GraphSageite{GraphSage},

Every extraction API demos well on a clean invoice. But what about the scanned form, the nested table, the 40-page financial report with mer…

Every extraction API demos well on a clean invoice. But what about the scanned form, the nested table, the 40-page financial report with merged headers? We tested 14 frontier systems to find out. Extr

tutorialsllamaindex--x

Expectations and Practices around AI Disclosure in CS Research

arXiv:2608.23271v1 Announce Type: cross Abstract: As generative AI tools find increasing use in research workflows, ongoing debates on their impact, appropriateness and responsible use have led policy

tutorialsarxiv-cs-cl

FreKoo++: Learning Continuous Spectral Dynamics for Temporal Domain Generalization

arXiv:2608.22224v1 Announce Type: cross Abstract: Temporal Domain Generalization (TDG) aims to learn from historical domains and generalize to unseen future distributions under concept drift. Neverthe

From Solver Feedback to Faithful Plans: Multi-Role Reinforcement Learning for Symbolic Planning

arXiv:2608.21897v1 Announce Type: new Abstract: Reliable planning requires converting natural-language instructions into executable symbolic specifications, yet large language models remain brittle wi

From Symmetry to Invariance: Learning Galois Equivalent Representations in Finite Fields

arXiv:2608.22513v1 Announce Type: new Abstract: Neural networks can learn algebraic operations from finite examples, but it remains unclear whether this ability transfers across mathematically equival

Functional compatibility as a determinant of persistent neural learning

arXiv:2608.22462v1 Announce Type: cross Abstract: Artificial neural networks can acquire new capabilities but often damage existing ones when they continue to learn. This stability-plasticity problem

Generate in the Chart, Not on the Boundary: Function-Symbol Grounding for Hard Constraints in LTN-GANs

arXiv:2608.21605v1 Announce Type: new Abstract: Logic Tensor Network-Enhanced Generative Adversarial Networks (LTN-GANs) inject background knowledge by grounding each logical axiom as a predicate and

GeoMix: Descriptor-Free Visual Localization via Global Context and Multi-Detector Training

arXiv:2607.02486v2 Announce Type: replace Abstract: Descriptor-free visual localization eliminates high-dimensional descriptor storage, preserves scene privacy, and simplifies map maintenance, yet its

Hi @GaryMarcus, I'm the Research Lead for this model. It's even more consequential than just Anthropic's IPO: This paper is a blueprint for …

tutorialsclem-delangue--x

Hi @GaryMarcus, I'm the Research Lead for this model. It's even more consequential than just Anthropic's IPO: This paper is a blueprint for how to *actually* build AI Sovereignty. https://tinyurl.com/

How to evaluate LLMs before production

Mariko Wakabayashi is a Principal Applied Scientist at Microsoft who leads the development of agentic AI workflows for cybersecurity operations. She specializes in evaluating LLM‑powered systems and a

tutorialsgithub-ai-blog