Search: “tutorials”

LatentReRig: An SDF-Based VAE with Dual Decoders for Latent-Space Deformation Conditioning

arXiv:2609.31720v1 Announce Type: new Abstract: Transferring deformation between characters with different geometry and topology is challenging because conventional rigs encode behaviour through chara

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Learning Dynamics of Continual Learning: A Unified View of Data Attribution, Forgetting, and Plasticity Loss

DGX agent

arXiv:2609.33620v1 Announce Type: new Abstract: Modern language models are likely to be updated throughout their lifetime rather than trained once and frozen. Each update therefore participates in a r

tutorialsarxiv-cs-lg

29 Sept 2026

Learning to Re-Draft: A Variational Stackelberg Game for Discrete Diffusion

arXiv:2609.35166v1 Announce Type: new Abstract: Discrete diffusion models offer the ability to re-draft, revisiting and correcting earlier tokens throughout generation. This capability depends on the

Learning Where and What to Restore for Composite Image Restoration

arXiv:2503.17915v2 Announce Type: replace-cross Abstract: Real-world degraded images often contain multiple co-occurring degradation types, making composite image restoration fundamentally different f

LVMT: Video Mask Transformer for Long-term Video Segmentation

arXiv:2609.34895v1 Announce Type: new Abstract: Existing online video segmentation methods struggle to track objects in long, complex videos with long-term occlusions. We hypothesize that this limitat

Managing Self-Learning Experts under Per-Round Budget Constraints

arXiv:2510.22654v3 Announce Type: replace Abstract: This paper addresses the problem of sequential decision-making under learning budget constraints. Such settings naturally arise in applications like

Metro-WM: Long-Horizon Latent Planning with Realisable Sub-Goals

arXiv:2609.31868v1 Announce Type: cross Abstract: Model-predictive control with Joint-Embedding Predictive Architectures (JEPAs) provides a strong zero-shot goal-reaching planner, but it is only effec

Multi-Marginal Inverse Optimal Transport for Contrastive Learning Via Explicit Anchor-Positive-Negative Coupling

arXiv:2609.33741v1 Announce Type: cross Abstract: Inverse Optimal Transport (OT) based methods for representation learning learn representations such that the global OT coupling between a pair of data

Not All Rollouts Are Worth Learning: On Trajectory Valuation for Post-Training Reinforcement Learning

arXiv:2609.35072v1 Announce Type: new Abstract: We consider the problem of trajectory valuation in reinforcement learning: how to identify and mitigate detrimental trajectories during online training.

Observability-Informed Optimal Sensor Placement for Soft Robots

arXiv:2609.33681v1 Announce Type: new Abstract: This paper presents the application and experimental evaluation of a systematic method for optimal sensor placement in soft robots. Existing methods eit

On the Relation Between Interval Regret and Dynamic Regret

arXiv:2609.34423v1 Announce Type: new Abstract: Non-stationary online learning has attracted much attention in recent years, as static regret is insufficient to guide algorithm design in changing envi

Optimal Transport Dropout for Structured Predictive Uncertainty

arXiv:2609.33377v1 Announce Type: new Abstract: Deterministic neural networks and neural operators provide point predictions with no intrinsic measure of reliability. Yet, predictive uncertainty may s

Period Segmentation in Transition Network Analysis: A Topological Data Analysis Approach

arXiv:2609.32094v1 Announce Type: cross Abstract: Temporal dynamics in learning behavior can be revealed through period segmentation in Transition Network Analysis (TNA). Cristea et al. demonstrated t

Predictive Dual Smoothing for Column Generation

arXiv:2609.34740v1 Announce Type: new Abstract: Solving large-scale linear programs efficiently is an important challenge in many optimization settings. A key technique is column generation, which alt

Prompt engineering by Quick component: Patterns and pitfalls

Part 2 of our Amazon Quick prompt engineering series goes component by component. Learn the prompt patterns that get the best results from Amazon Quick Research, Quick Flows, Quick Sight, chat agents,

tutorialsaws-ml-blog

QiYao-M: Multimodal Time Series Foundation Model with Role-Aware Modeling of Endogenous and Exogenous Modalities

arXiv:2609.34842v1 Announce Type: new Abstract: Existing multimodal time series foundation models (TSFMs) typically model heterogeneous modalities through largely shared mechanisms, overlooking the di

Retimed Bellman Flows: Escaping the Impossible Triangle of Velocity Bootstrapping

arXiv:2609.32828v1 Announce Type: new Abstract: Flow critics learn return distributions by transporting Gaussian noise to Bellman endpoints via continuous velocity fields. While velocity bootstrapping

Revitalizing Medical Time Series with Vision-Informed Retrieval: A Vision-Language Perspective

arXiv:2609.34652v1 Announce Type: new Abstract: Medical time series (MedTS) underpin many clinical classification tasks, yet existing methods usually represent them only as numerical sequences and und

RT-Super: Learning Tumor Segmentation from Longitudinal Images and Reports

arXiv:2609.35637v1 Announce Type: new Abstract: Multi-tumor segmentation is important for early cancer detection and allows radiologists to visualize, verify, and understand AI predictions. However, t

S^2COPE: Self-Supervised Concept Discovery via Preference Learning

arXiv:2606.14586v2 Announce Type: replace Abstract: Self-supervised learning can learn useful representations from unlabeled images. Yet these representations remain largely opaque, making it difficul

SeLMRoute: Probabilistic Semantic Evidence for Large Language Model Routing

arXiv:2609.34736v1 Announce Type: cross Abstract: Large language model (LLM) routing aims to select the most suitable model for each incoming query. Most existing routers learn this decision directly

Sharp training-conditional coverage for conformal prediction under covariate shift

arXiv:2609.33456v1 Announce Type: cross Abstract: Weighted split conformal prediction reweights calibration scores by the likelihood ratio between the test and training covariate distributions and gua

SphMind: Towards Robust, Training-Free VLM-based Spatial Reasoning with a 360 Camera

arXiv:2609.33462v1 Announce Type: new Abstract: Omnidirectional or 360 cameras provide embodied AI agents with a holistic, wide field-of-view (FoV) view of their surroundings, motivating the use of Mu

Staying on the Attractor: Supervising Neural Surrogates of 3D Turbulence Where They Leave It

arXiv:2609.32864v1 Announce Type: new Abstract: Neural surrogates are trained to predict 3D turbulent flows in place of direct numerical simulation (DNS). For chaotic flows, the goal is short-term poi

Style-Driven Data Synthesis and Degradation-Aware Enhancement for Ultrasound Image Restoration

arXiv:2609.35120v1 Announce Type: new Abstract: Low-cost handheld ultrasound devices can be widely deployed compared to professional hospital ultrasound machines. However, their images suffer from com

The Geometry of Logic: Stratification Induces Semantic Structure and Robust Reasoning

arXiv:2609.32927v1 Announce Type: new Abstract: Transformer-based language models perform well on symbolic tasks, yet it remains unclear whether they learn generalizable rules or rely on statistical s

The Hidden Perception Constraint in Task-Aware Compression

arXiv:2609.35684v1 Announce Type: cross Abstract: With the recent advancements of neural compressors, explicitly incorporating perception constraints into the design of compression schemes has gained

Took a while, but here it is, a mega write-up on language models for text classification and, of course, Jev. It's basically a visual guide …

tutorialssebastian-raschka--x

Took a while, but here it is, a mega write-up on language models for text classification and, of course, Jev. It's basically a visual guide to RNNs, CNNs, transformers, and calibration, with hands-on

TRAP: Understanding and Mitigating Privacy Memorization in Language Models

arXiv:2609.32293v1 Announce Type: cross Abstract: Fine-tuning a language model on sensitive records can leave it able to reproduce them. We ask when this memorization arises and how to prevent it with

Uni-VLaT: Whole-Body Tactile Adaptation of VLA Policies for Humanoid Loco-Manipulation

arXiv:2609.35450v1 Announce Type: new Abstract: Physical contact often determines how a humanoid should respond during loco-manipulation, yet vision and proprioception alone are often insufficient to

Unifying Deep Predicate Invention with Pre-trained Foundation Models

arXiv:2512.17992v2 Announce Type: replace Abstract: Long-horizon robotic tasks are hard due to continuous state-action spaces and sparse feedback. Symbolic world models help by decomposing tasks into

UniPool: Learning Expert-to-Layer Ownership from Brief Global Access

arXiv:2605.06665v2 Announce Type: replace Abstract: Most Mixture-of-Experts (MoE) transformers preassign each expert to one layer for the whole of training. We study whether a brief global-access phas

Unlocking Few-Step Diffusion for Faithful Previews

arXiv:2609.34406v1 Announce Type: cross Abstract: Sampling latency compounds in diffusion workflows, where users generate and discard many candidates before keeping one. Surprisingly, the poor outputs

VGGT-Diff: Visual Geometry Meets Diffusion for Sparse-View Novel View Synthesis

arXiv:2609.33253v1 Announce Type: new Abstract: We present VGGT-Diff, a geometry-routed multi-view diffusion model for sparse-view novel view synthesis. Existing novel view synthesis (NVS) methods fac

VPTwin: Real-Sim-Real Video Prediction for Robotic Manipulation Planning

arXiv:2609.33104v1 Announce Type: new Abstract: While action-conditioned video prediction provides an intuitive world model for robotics, purely data-driven predictors often suffer from compounding er

W2Rep: Learning Visual Representations by Watching the World Change

arXiv:2609.35464v1 Announce Type: new Abstract: Images capture the world at one moment, whereas video reveals how it changes. Image self-supervision learns spatial structure from a single moment, whil

Weighting Schedules Govern What and When Score-Based Generative Models Learn from Multimodal Data

arXiv:2609.35322v1 Announce Type: new Abstract: Score-based generative models generate new samples by integrating a time-dependent drift that carries Gaussian noise onto the target distribution. In pr

What Should a Streaming Video Model Remember?

arXiv:2606.16353v2 Announce Type: replace Abstract: Streaming video understanding models must answer queries at any moment during an ongoing stream, using only what they have observed so far and under

When Can Attention Heads Be Statically Defined?

arXiv:2609.34650v1 Announce Type: new Abstract: Some attention heads learn similar patterns across inputs. Reusing these patterns could reduce training cost by avoiding repeated query-key score comput

When Does Depth Matter For In-Context Learning? Adaptive Inference in Deep Transformers

arXiv:2606.16694v2 Announce Type: replace Abstract: Transformers perform computations through many successive attention and feedforward blocks, allowing them to learn complex correlations between a la

XMatch: Enhancing Covariate-Aware Time Series Forecasting through Tree-Structured Exogenous Matching

arXiv:2609.34939v1 Announce Type: new Abstract: Future exogenous variables provide valuable information for forecasting endogenous time series. Existing covariate-aware methods primarily learn the dir

A Unified Account of Concepts and Chunks

arXiv:2609.30414v1 Announce Type: cross Abstract: Cognitive psychology has studied how people encode, use, and learn concepts that describe categories, and how they represent, recognize, and acquire c

tutorialsarxiv-cs-ai

Aurora-X: Built for Extreme Time Series Forecasting

arXiv:2609.31038v1 Announce Type: new Abstract: Time series foundation models (TSFMs) enable cross-domain forecasting, but their development as general-purpose forecasters remains constrained by under

Automating Amazon Textract adapter lifecycle management across accounts

Learn how to operationalize Amazon Textract Custom Queries adapters for production: infrastructure as code with AWS CloudFormation and Terraform, a cross-account adapter promotion process, a pre-class

tutorialsaws-ml-blog

Combining General and Domain-Specific Pretext Tasks for Brain MR Image Segmentation

arXiv:2609.30708v1 Announce Type: cross Abstract: A key challenge in medical image analysis is the scarcity of large annotated datasets for specific populations and diseases. As deep learning models r

tutorialsarxiv-cs-ai

Foundations of Reinforcement Learning and Interactive Decision Making

arXiv:2312.16730v2 Announce Type: replace Abstract: Interactive decision making is the problem of learning to act well in an unknown environment, using the data that one's own actions generate to cont

Geometric Feature Learning for Functional Data Valued on the Symmetric Positive Definite Manifold

arXiv:2609.30487v1 Announce Type: new Abstract: We here develop a functional neural network, termed MatFAE, for learning trajectories on the Riemannian manifold of symmetric positive definite (SPD) ma

I Act Therefore I Am: When Is JEPA's Action-Conditioning Enough to Learn Causal Mechanisms?

arXiv:2609.31161v1 Announce Type: new Abstract: Recent empirical and theoretical advances suggest that joint-embedding predictive architectures (JEPAs) may learn meaningful representations for action-