tutorials

Sensor Geometry as a Flow-Matching Prior for Multi-Channel Brain Signals

arXiv:2610.08355v1 Announce Type: cross Abstract: Flow-matching models start from an isotropic Gaussian source, the standard choice when the correlation structure of the data is unknown in advance. Fo

Should We Skip Diffusion?

Content type

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

DGX agent

arXiv:2610.07002v1 Announce Type: cross Abstract: Diffusion models learn semantic representations while generating images. In the Decoupled Diffusion Transformer (DDT), a condition encoder provides fe

tutorialsarxiv-cs-cv

7 Oct 2026

Source-Learned Reliance for Selective Test-Time Adaptation of Multimodal Time Series

arXiv:2610.07499v1 Announce Type: new Abstract: Multimodal wearable systems must remain reliable when sensor streams become noisy or unavailable. Existing multimodal test-time adaptation (TTA) methods

Stochastic Siamese MAE Pretraining for Longitudinal Medical Images

arXiv:2512.23441v2 Announce Type: replace-cross Abstract: Temporally aware image representations are crucial for capturing disease progression in 3D volumes of longitudinal medical datasets. However,

The Premise Is the Problem: Exchangeability Failure in Self-Monitored Test-Time Adaptation

arXiv:2610.07038v1 Announce Type: new Abstract: Modern forecasting models are often updated after deployment so they can respond to changing data. These updates can also make predictions worse, so pra

The real news here isn’t the result; it’s what we were not told. 1. AI once tried to be a science. Now we get stuff like the completely vagu…

The real news here isn’t the result; it’s what we were not told. 1. AI once tried to be a science. Now we get stuff like the completely vague report from OpenAI below, and a lot of ignorant questions

tutorialsgary-marcus--x

Verdicts Without Annotated Evidence: Rejection Sampling or Label-Only Post-Training for Evidence Recovery?

arXiv:2610.06962v1 Announce Type: cross Abstract: In many review workflows the verdict is the only thing retained. The passages behind it are not marked, because that annotation costs far more than re

Verified, not generated: expert-verified AI study materials and the distribution of learning gains in a university course

arXiv:2610.07097v1 Announce Type: new Abstract: Experimental studies of generative AI in education mostly report average effects, yet field evidence shows that AI can narrow attainment gaps or widen t

We're sharing progress on ChatGPT for Teens, our ChatGPT experience for people under 18, alongside a preview of College Planner, new study t…

We're sharing progress on ChatGPT for Teens, our ChatGPT experience for people under 18, alongside a preview of College Planner, new study tools, and support for college advisers and teen voices. Chat

tutorialsopenai--x

Where to get started if you want to publish papers in Neurips,ACL,ICLR/A* conferences [R]

tutorialsr-machinelearning

I'm a Ai engineer with about 2 years work experience, but let's just assume that I was a undergraduate student just starting out where would I begin so that I can publish a A* conference paper at some

WildMatch: Weakly Supervised Image Matcher Adaptation for Wildlife Re-Identification

arXiv:2610.07384v1 Announce Type: cross Abstract: Individual animal re-identification from camera-trap imagery is an instance retrieval problem central to non-invasive wildlife monitoring: a query ima

Adaptive Partitioning Schemes for Optimistic Optimization

arXiv:2610.04039v1 Announce Type: cross Abstract: Applications such as engineering design often require us to optimize a black-box function, i.e., a system whose inner processing is not analytically k

Anomaly Detection and Generation with Diffusion Models: A Survey

arXiv:2506.09368v2 Announce Type: replace Abstract: Anomaly detection (AD) plays a pivotal role across diverse domains, including cybersecurity, finance, healthcare, and industrial manufacturing, by i

Anosognosia in LLMs: Probing Self-Awareness of Quantized Computational Substrate

arXiv:2610.06174v1 Announce Type: new Abstract: Can LLMs recognize degradation in their own computational substrate? Inspired by anosognosia, a neurological condition in which patients fail to recogni

Backward-Consistent Diffusion Sampling for Sparsely Observed PDE Inverse Problems

arXiv:2610.04624v1 Announce Type: new Abstract: Recovering Partial Differential Equation (PDE) coefficient fields from extremely sparse observations is a severely ill-posed inverse problem for which g

Bayesian Entropy-based Reordering for Calibrated Diffusion Language Models

arXiv:2610.05125v1 Announce Type: new Abstract: Masked Diffusion Language Models (MDLMs) generate sequences by iteratively replacing masked tokens with model predictions. At each denoising step, the d

Best practices for Amazon SageMaker HyperPod administration and governance

Learn how to administer Amazon SageMaker HyperPod through Amazon SageMaker Unified Studio while preserving cluster governance. This post shows platform teams how to design infrastructure boundaries, g

tutorialsaws-ml-blog

Broaden Your Views for Self-Supervised Video Learning

arXiv:2103.16559v3 Announce Type: cross Abstract: Most successful self-supervised learning methods are trained to align the representations of two independent views from the data. State-of-the-art met

Building a context-aware AI assistant on AgentCore and OpenClaw

Off-the-shelf AI assistants forget you between conversations. This post shows how to build a personal assistant that accumulates context using OpenClaw on Amazon Bedrock AgentCore runtime, with AgentC

tutorialsaws-ml-blog

Cross-Modal Solar Image Synthesis: Adapting the Surya Foundation Model from He I 10830 {AA} to EUV Translation and Coronal Hole Segmentation

arXiv:2610.04553v1 Announce Type: cross Abstract: The long observational record of He I 10830 {AA} offers a means to investigate solar morphology before modern extreme-ultraviolet (EUV) imaging. We ad

CurveCodec 2: Skeleton-agnostic animation compression with a learned entropy model

arXiv:2610.04211v1 Announce Type: cross Abstract: Skeletal motion is stored as every joint's transform at every frame, yet most of it is implied by the body rather than by what the motion is about. Co

tutorialsarxiv-cs-ro

DexForge: High-Fidelity Physics-Informed Dexterous Retargeting

arXiv:2610.06331v1 Announce Type: new Abstract: Human demonstrations offer rich examples of precise dexterous manipulation and a promising source of robot training data. However, high-fidelity reprodu

tutorialsarxiv-cs-ro

Diffusion Transformers are Provably Optimal In-context Generators

arXiv:2610.05333v1 Announce Type: new Abstract: Generative foundation models are attracting interest for their ability to produce desired outputs from demonstrations given at inference time, without u

DiVeR: Decision-Critical Verifier Learning for VLA Test-Time Scaling

arXiv:2610.04933v1 Announce Type: cross Abstract: Scaling robot data and model capacity has improved Vision-Language-Action (VLA) policies, but further progress is constrained by the high cost of robo

Do Motion Tokenizers for Co-Speech Gesture Generation Encode Gesture Semantics?

arXiv:2610.03765v1 Announce Type: new Abstract: Discrete motion tokenizers encode motion as atomic units and are widely used for co-speech gesture generation. It remains unclear which motion propertie

Do Neural Networks Learn Structure-Preserving Maps? A Case Study in Latent-to-Hilbert Embeddings

arXiv:2610.05297v1 Announce Type: new Abstract: We ask whether a neural network can learn a structure-preserving map from a compressed latent space to a Hilbert-space representation. Using an 8-dimens

Dynamic Quadtree Tokenization and Transformer for Adaptive Mesh PDE Forecasting

arXiv:2610.04044v1 Announce Type: new Abstract: The quadratic attention cost of Vision Transformers (ViTs) forces a trade-off between spatial resolution and rollout horizon, particularly for fine-scal

Efficient Graph Generation via Direct Prediction and Flow Matching

arXiv:2610.05397v1 Announce Type: new Abstract: Generative modeling of graph-structured data is crucial for tasks ranging from drug discovery to social network simulation. Among these models, denoisin

Embedding Every Font with Neural Networks makes some Nice Structures (including a flower) [P]

tutorialsr-machinelearning

I've been working on a font searching tool for about a year now, and my investigations have centered around pre-training neural networks to produce embeddings of each font. I usually then need to post

Frequency-Decoupled Diffusion Guidance for Non-Blind Image Deblurring

arXiv:2610.06221v1 Announce Type: new Abstract: Pretrained diffusion models provide powerful image priors for training-free posterior sampling in image restoration. To guide this sampling process, fre

From Pixels, Without Pre-training: Joint Generative and Self-Supervised Representation Learning in One Model

arXiv:2610.05711v1 Announce Type: cross Abstract: Strong image generation models are conditioned on class labels, aligned to frozen pretrained encoders, or built on separately trained autoencoders. Wh

GeoFunFlow: Geometric function flow matching for joint probabilistic inference of physical fields and complex geometries

arXiv:2509.24117v2 Announce Type: replace Abstract: Inverse problems governed by partial differential equations (PDEs) arise widely in science and engineering, but are often ill-posed and limited by s

HiER-BLS: A Hierarchy-Guided and Error-Correcting Robust Incremental Broad Learning System

arXiv:2610.05834v1 Announce Type: new Abstract: Broad Learning System (BLS) supports analytical training and incremental expansion, but its growth needs guidance on which inputs new blocks should lear

How Sparse Probability Maps Shape Mixture-of-Experts Routing

arXiv:2610.06677v1 Announce Type: new Abstract: Mixture-of-experts (MoE) routers typically apply softmax to the router scores and keep the top-K experts, making every token use exactly K experts. Spar

How to scale your HEP ML models: A recipe for robust architecture comparisons at scale

arXiv:2610.06784v1 Announce Type: cross Abstract: Much of the recent progress in machine learning domains such as language models has come from scaling laws that predict performance as a function of t

Inverse Mixed-Integer Programming: Learning Constraints then Objective Functions

arXiv:2510.04455v3 Announce Type: replace-cross Abstract: Data-driven inverse optimization for mixed-integer linear programs (MILPs), which seeks to learn an objective function and constraints consist

Langevin Flow Maps: Efficient Molecular Dynamics and Transition Path Sampling

arXiv:2610.05998v1 Announce Type: new Abstract: Molecular dynamics simulations proceed by integrating the Langevin equations over many small femtosecond timesteps. This poses a challenge for estimatin

Learning Subject-Specific Anatomical Representations via Manifold Expansion: Application to Accelerated Multi-Contrast MRI

arXiv:2610.04028v1 Announce Type: new Abstract: Clinical MRI routinely acquires multiple contrast-weighted images of the same anatomy for complementary tissue characterization. However, current accele

Learning Task and Motion Plans from Real Demonstrations with Hybrid Flow Matching

arXiv:2610.04771v1 Announce Type: cross Abstract: Long-horizon mobile manipulation requires a task plan and the motion that executes it. Generative planners trained on demonstration produce both in on

Learning Topological Representations of Protein Structure and Dynamics

arXiv:2606.14737v2 Announce Type: replace-cross Abstract: Modern protein representation models support tasks such as enzyme design and drug discovery, but their reliance on static data such as sequenc

LEON: Location Embeddings from OSM Neighborhoods via Hexagonal Graph Masked Autoencoders

arXiv:2610.05497v1 Announce Type: new Abstract: Geographic information systems increasingly rely on sophisticated spatial representation learning techniques to extract meaningful patterns from complex

Less Context, Better Geometry: Masked Geometric Encoder for Robust 3D Foundation Models

arXiv:2610.06813v1 Announce Type: new Abstract: Recent progress in 3D foundation models has enabled rapid 3D reconstruction and camera calibration by leveraging learned 3D priors from vast amount of s

Lightweight Semantic EEG Foundation Model for Frozen Cross-Disorder Transfer

arXiv:2610.05503v1 Announce Type: new Abstract: Large-scale EEG foundation models have demonstrated promising transferability across neurological disorders, but often require millions of parameters an

Lipschitz Thinking: Ten Years of Certifiable-by-Design Robust Neural Networks

arXiv:2610.06252v1 Announce Type: new Abstract: The Lipschitz property of a deep neural network provides a direct measure of its sensitivity to input perturbations and, when explicitly controlled, off

METRO: Metric-Enhanced Token Routing Operator

arXiv:2610.05100v1 Announce Type: new Abstract: State-of-the-art neural operators scale to complex meshes via slice-and-process architectures, yet many rely on linear compatibility scores for latent t

MIRT: Transformers for Truthful Generative Auctions with Whole-feed Permutation Externalities

arXiv:2610.06559v1 Announce Type: cross Abstract: Modern online platforms commonly rank ads and organic content separately before blending them into a feed displayed to the user, overlooking externali

Neural Bayesian Filtering

arXiv:2510.03614v2 Announce Type: replace Abstract: Sequential estimation under partial observability requires tracking beliefs that may be high-dimensional, multimodal, and non-Gaussian. Classical Ba

OCL-PDE: A Generative Framework for PDE Inverse Problems with Observation-Complementary Latents

arXiv:2610.06259v1 Announce Type: new Abstract: Partial differential equation (PDE) inverse problems are often ill-posed, making fine-scale details difficult to recover. We address this problem by int