Search: “tutorials”

Full-Body Golf Swing Kinematic Reconstruction From a Smartwatch IMU

arXiv:2606.22876v1 Announce Type: new Abstract: Quantitative measurement of the golf swing is critical for evaluating technique and enabling individualized feedback. However, existing methods are impr

Gain Tuning Is Not What You Need: Reward Gain Adaptation for Constrained Locomotion Learning

DGX agent

arXiv:2510.10759v2 Announce Type: replace Abstract: Existing robot locomotion learning techniques rely heavily on the offline selection of proper reward weighting gains and cannot guarantee constraint

tutorialsarxiv-cs-ro

23 Jun 2026

Gen2Balance: Generative Balancing for Long-Tailed Video Action Recognition

arXiv:2606.22416v1 Announce Type: new Abstract: We address the problem of training on long-tailed data for video action recognition. We propose to augment the training set using a text-to-video genera

How Businesses Are Building Specialized AI They Can Trust

Companies are asking how to build specialized AI that fits with the way their workflows actually run. The first wave of enterprise AI was about access. Companies experimented with new frontier and ope

tutorialsnvidia-blog

I automated my job (and it made me a better leader)

Explore how my day as a senior leader looks now that I use 40 automations to help, and learn more about some of my favorites. The post I automated my job (and it made me a better leader) appeared firs

tutorialsgithub-ai-blog

Keep The Essentials: Efficient Reference Conditioned Generation via Token Dropping

arXiv:2606.23682v1 Announce Type: new Abstract: Reference-based diffusion models enable highly controllable image generation by leveraging elements from input images to guide prompt-driven synthesis.

Learning through Internalization

arXiv:2606.20937v1 Announce Type: new Abstract: We study internalization processes, by which neural-network-based systems absorb an explicit computational procedure into their own weights, and how the

Leveraging AutoML for Sustainable Deep Learning: A Multi-Objective HPO Approach on Deep Shift Neural Networks

arXiv:2606.23208v1 Announce Type: new Abstract: Deep Learning (DL) has advanced various fields by extracting complex patterns from large datasets. However, the computational demands of DL models pose

LEViL: Label-Efficient Video Learning via Zero-Shot Distillation over VLM-Generated Pseudo-Label Spaces

arXiv:2606.21358v1 Announce Type: new Abstract: Supervised video pretraining is a common transfer learning practice for improving downstream action recognition performance. However, it requires large-

Lift4D: Harmonizing Single-View 3D Estimation for 4D Reconstruction In-the-Wild

arXiv:2606.23688v1 Announce Type: new Abstract: Reconstructing dynamic non-rigid objects from monocular video requires integrating visual cues from direct observations with data-driven priors over geo

LightOcc: Lightweight Spatial Embedding for Efficient Vision-based 3D Occupancy Prediction

arXiv:2412.05976v2 Announce Type: replace Abstract: Occupancy prediction has garnered increasing attention in recent years for its comprehensive fine-grained environmental representation and strong ge

LLM-Aided A* Search in Non-Geometric Network Graphs

arXiv:2606.23136v1 Announce Type: cross Abstract: Finding the shortest path in non-geometric network graphs, where edge weights encode arbitrary metrics such as latency or monetary cost rather than sp

Look Light, Think Heavy: What Multimodal Chain-of-Thought Reasoning Can and Cannot Do

arXiv:2606.22565v1 Announce Type: cross Abstract: Chain-of-Thought (CoT) has become a standard method for improving reasoning capabilities in large language models (LLMs) by eliciting step-by-step thi

MAVRL: Learning Reward Functions from Multiple Feedback Types with Amortized Variational Inference

arXiv:2602.15206v2 Announce Type: replace Abstract: Reward learning typically relies on a single feedback type or combines multiple feedback types using manually weighted loss terms. Currently, it rem

MIRCaps: A Large-Scale Mixed-Domain Dataset with Image-Level and Region-Level Captions for Fine-Grained Vision-Language Learning

arXiv:2606.21419v1 Announce Type: new Abstract: Despite recent progress in Vision-Language Models (VLMs), mixed-domain image-caption datasets for both general-purpose and CCTV-based video surveillance

Native space based pipelines outperform template space based pipeline in subcortical segmentation

arXiv:2606.21463v1 Announce Type: new Abstract: Accurate segmentation of subcortical regions is critical for neurosurgical planning and functional research. Most automated methods rely on template spa

Neural Operator Processes for Probabilistic Operator Learning under Partial Observations

arXiv:2606.22946v1 Announce Type: new Abstract: Neural operators learn mappings between function spaces, but are typically developed with dense input-output training fields and fully observed inputs a

NewPINNs: Physics-Informing Neural Networks Using Conventional Solvers for Partial Differential Equations

arXiv:2601.17207v2 Announce Type: replace Abstract: We introduce NewPINNs, a physics-informing learning framework that couples neural networks with conventional numerical solvers for solving different

NGPS: Structure-Preserving Self-Supervised Denoising via Neighbor-Guided Patch Sampling

arXiv:2606.23200v1 Announce Type: cross Abstract: Neighboring-slice self-supervised denoising is attractive for volumetric medical imaging, yet inter-slice misalignment breaks anatomical correspondenc

Nice to see the Drivetrain Approach getting some love again! :D https://x.com/ForBo7_/status/2069337722821673174

tutorialsjeremy-howard--x

Nice to see the Drivetrain Approach getting some love again! :D https://x.com/ForBo7_/status/2069337722821673174 Did a close read of @jeremyphoward et al's Drivetrain Approach Define goal → Consider l

Non-Adversarial Imitation Learning Provably Free of Compounding Errors: The Value Flow Mechanism

arXiv:2603.22713v2 Announce Type: replace Abstract: Adversarial imitation learning (AIL) achieves high-quality imitation by mitigating compounding errors inherent to behavioral cloning (BC), yet its a

NullFlow: One-Step Generative Reconstruction

arXiv:2606.22696v1 Announce Type: new Abstract: We propose NullFlow, a principled framework for one-step generative image reconstruction. Our key idea is to confine the generative flow to a measuremen

On the Memorization Behavior of LLMs in Generative Recommendation: Observations, Implications, and Training Strategies

arXiv:2606.17276v3 Announce Type: replace-cross Abstract: Generative recommendation (GR) has emerged as a promising direction for recommender systems. Recently, large language models (LLMs) have been

Online World Modeling Enables Real-World Inverse Reinforcement Learning from Observation

arXiv:2602.24121v2 Announce Type: replace Abstract: Current methods in robot learning are fundamentally bottlenecked by one or more of: hand-designed rewards, simulation modeling, or action supervisio

tutorialsarxiv-cs-ro

PhyGDPO: Physics-Aware Groupwise Direct Preference Optimization for Physically Consistent Text-to-Video Generation

arXiv:2512.24551v4 Announce Type: replace Abstract: Recent advances in text-to-video (T2V) generation have achieved good visual quality, yet synthesizing videos that faithfully follow physical laws re

Poisson2Gaussian: Noise Gaussianization to Enhance Image Denoising

arXiv:2606.23098v1 Announce Type: new Abstract: The quantum nature of light determines the inherent Poisson stochasticity of photon detection, which is ubiquitous in photography, microscopy, and astro

Predictive Gaze Is Preserved but Reorganized toward Monitoring during Robot-Mediated Manipulation

arXiv:2606.21920v1 Announce Type: new Abstract: Goal-directed eye movements are a fundamental component of visuomotor control, enabling humans to anticipate and guide their actions. Whether this antic

tutorialsarxiv-cs-ro

Provable Benefits of RLVR over SFT for Reasoning Models: Learning to Backtrack Efficiently

arXiv:2606.22938v1 Announce Type: new Abstract: Recent advances in large language models (LLMs) have demonstrated that reinforcement fine-tuning of pretrained base models can lead to significant gains

Provable Learning of Random Hierarchy Models and Hierarchical Shallow-to-Deep Chaining

arXiv:2601.19756v2 Announce Type: replace Abstract: The empirical success of deep learning is often attributed to deep networks' ability to exploit hierarchical structure in data, constructing increas

Published by @OReillyMedia here: https://www.oreilly.com/radar/drivetrain-approach-data-products/

tutorialsjeremy-howard--x

The Drivetrain Approach is a framework for building effective data products, developed by Jeremy Howard and published by O'Reilly Media, that focuses on defining business objectives, identifying lever

Rethinking Prototype-based Similarity Learning for Few-Shot Object Detection

arXiv:2606.23069v1 Announce Type: new Abstract: Few-shot object detection aims to detect novel object categories from only a few labeled examples, avoiding costly large-scale annotation. Recent protot

Scalable Bayesian Additive Models for Stellar Flare Detection via Amortized Gaussian Process Inference and Hidden Markov Models

arXiv:2606.22601v1 Announce Type: cross Abstract: Gaussian Processes (GPs) are a powerful tool for Bayesian time-series modeling, yet their cubic computational cost remains a severe barrier for applic

Scalable Training of Spatially Grounded 2D Vision-Language Models for Radiology

arXiv:2606.20477v2 Announce Type: replace Abstract: We study how to train visually grounded vision-language models (VLMs) for radiology without manual spatial annotations. We introduce RefRad2D, a lar

Selective Ensemble Based on Preference-Directed Multi-Objective Bandits

arXiv:2606.21929v1 Announce Type: new Abstract: Selective ensemble for modern machine learning systems requires choosing promising model candidates under limited evaluation budgets, while downstream t

Shared infrastructure, isolated tenants: Pool model multi-tenancy with Amazon Bedrock AgentCore

In this post, you will learn patterns for implementing production-ready multi-tenant systems using Amazon Bedrock AgentCore. You will see these patterns demonstrated through healthcare AI agents that

tutorialsaws-ml-blog

Sparse Point-Guided Fusion of Supervised and Self-Supervised Learning Model for Seaweed Segmentation

arXiv:2606.21026v1 Announce Type: new Abstract: The ocean plays a critical role in sustainable development, particularly in climate change mitigation. Among marine ecosystems, blue carbon ecosystems a

Spectral GS-SLAM: Observability-Aware, Degeneracy-Robust Tracking for Real-Time 3D Gaussian Splatting SLAM

arXiv:2606.21258v1 Announce Type: cross Abstract: Recent 3DGS-SLAM systems enable real-time operation by leveraging conventional feature matching or ICP-based tracking, thereby avoiding the heavy dens

Structural Assessment for Understanding and Guiding Dataset Distillation in Discrete Token Space

arXiv:2606.21705v1 Announce Type: new Abstract: Dataset distillation (DD) has proven to reduce training cost while preserving accuracy. While promising, the factors that make one distilled dataset mor

The New Associationism: Lessons from Deep Learning

arXiv:2606.20600v1 Announce Type: cross Abstract: What can the success of modern AI tell us about how humans learn? This paper argues that taking AI seriously as a model of human learning supports a m

This year @WecoAI will be at the @aiDotEngineer World's Fair. We'll host a hands-on autoresearch workshop on June 29. And I'll give a talk o…

This year @WecoAI will be at the @aiDotEngineer World's Fair. We'll host a hands-on autoresearch workshop on June 29. And I'll give a talk on July 1. Looking forward to chatting with old and new frien

tutorialsswyx--x

VT-DUDA: Visual Token Conditioning for Diffusion-guided Unsupervised Domain Adaptation

arXiv:2606.21700v1 Announce Type: new Abstract: Unsupervised domain adaptation (UDA) aims to learn a target-domain classifier from labeled source data and unlabeled target data under distribution shif

What Do Lorentz-Equivariant Jet Taggers Learn?

arXiv:2606.21790v1 Announce Type: new Abstract: We study what Lorentz-equivariant jet taggers learn internally, using equivariance tests, linear probes and grade ablations across five models including

What Do Neural Networks Learn for TDOA Estimation? A Cross-Architecture Probing Study

arXiv:2606.22020v1 Announce Type: cross Abstract: Neural networks outperform classical GCC-PHAT for Time-Difference-of-Arrival (TDOA) estimation in noise and reverberation, yet their internal strategy

What Does a Chemical Language Model Know About Molecules?

arXiv:2606.23443v1 Announce Type: new Abstract: Chemical language models (cLMs) are widely assumed to learn surface-level syntactic patterns rather than learning meaningful molecular semantics. Here,

Why corporate AI super PACs spent $27 million on a local election

Hello and welcome to Regulator, the newsletter for Verge subscribers chronicling the misadventures of their favorite tech overlords and Washington swamp creatures. ('Favorite' is, of course, subjectiv

tutorialsthe-verge-ai

Words as Difference Makers: How Large Language Models Determine Causal Structure in Text

arXiv:2606.22430v1 Announce Type: cross Abstract: Because large language models (LLMs) are impressively successful in predicting text, it appears that they must have access to a 'world model' represen

ZenBusiness enhances its AI copilot for new business owners with step-by-step blueprints

ZenBusiness Inc., a company that offers an all-in-one-platform to help entrepreneurs launch and grow their businesses, today announced it’s expanding its Velo artificial intelligence guide, featuring

tutorialssiliconangle

Building pay-per-intelligence for AI agents: How Ampersend uses Amazon Bedrock AgentCore Payments