Search: “tutorials”

Adversarial Attack and Disturbance Detection by Hadamard-Coded Output Representations for Object Detection and Semantic Segmentation

arXiv:2606.09536v1 Announce Type: new Abstract: Conventional one-hot encodings often yield poorly calibrated models, being overconfident under attack, and letting entropy-based detection algorithms fa

Tutorials

AetheRock: An Arm-Worn Robot Teaching System for Force-Guided Vision-Tactile Learning

DGX agent

arXiv:2606.09777v1 Announce Type: new Abstract: Force and tactile sensing are indispensable in contact-rich manipulation. However, force-aware robot learning faces critical challenges due to the incom

tutorialsarxiv-cs-ro

9 Jun 2026

ATLAS: Verifier-Guided Adaptive Latent Activation Steering for Efficient LLM Reasoning

arXiv:2601.03093v2 Announce Type: replace Abstract: Recent work on activation and latent steering has demonstrated that modifying internal representations can effectively guide large language models (

C^3ache: Accelerating World Action Models with Cross Inference Chunk Cache

arXiv:2606.08962v1 Announce Type: cross Abstract: World Action Models (WAMs) generalize better than standard Vision-Language-Action (VLA) policies to novel motions and environments, because a video-mo

Degrading performance on ML research without telling the user is shockingly hostile and a terrible look. That could silently damage all so…

Degrading performance on ML research *without telling the user* is shockingly hostile and a terrible look. That could silently damage all sorts of work, including some of my own. Also the type of thin

Discovering and decoding latent mean-field structure with variational autoencoders

arXiv:2606.08694v1 Announce Type: cross Abstract: Generative models are increasingly used to capture correlations in many-body systems, but the representations they learn remain largely opaque to phys

EditSR: Enhancing Neural Symbolic Regression via Edit-based Rectification

arXiv:2606.07915v1 Announce Type: new Abstract: Neural symbolic regression models improve inference efficiency by shifting structural search to pretraining, but their one-pass autoregressive decoding

Ego-Pi: VLA Fine-Tuning for Ego-Centric Human and Robot Data

arXiv:2606.08107v1 Announce Type: cross Abstract: Robotics faces a fundamental challenge of data scarcity. Unlike language or vision research, there is no internet-scale dataset for robotic manipulati

Emergence of Context Characteristics Sensitivity in Large Language Models

arXiv:2606.09525v1 Announce Type: cross Abstract: During instruction fine-tuning (IFT), large language models (LLMs) learn to follow instructions by using the provided context to answer a query. While

Evaluating the Impact of Task Granularity on Catastrophic Forgetting in Continual Learning

arXiv:2606.08013v1 Announce Type: new Abstract: Catastrophic forgetting, the abrupt loss of previously acquired knowledge upon learning new information, remains the central challenge in Continual Lear

Explaining Data Mixing Scaling Laws

arXiv:2606.08167v1 Announce Type: cross Abstract: Recent research has established empirical scaling laws to predict model performance on multi-domain data mixtures. However, a theoretical understandin

Five things you need to know about AI

At SXSW London last week I gave a talk called “Five things you need to know about AI,” in which I shared what I think are the biggest themes in AI right now. I pulled a few things from our first AI10

tutorialsmit-tech-review

From inverse problems to neural operators: prediction, mechanism, and generalization of data-driven models

arXiv:2606.08956v1 Announce Type: new Abstract: Scientists have historically relied on mathematical models based on differential equations to relate system inputs -- forces, fluxes, or heat sources --

From one-off prompts to workflows: How to use custom agents in GitHub Copilot CLI

Custom agents let GitHub Copilot CLI understand your stack and team workflows, turning one-off terminal prompts into repeatable, reviewable processes. The post From one-off prompts to workflows: How t

tutorialsgithub-ai-blog

Graph Neural Networks for Predicting Solvability of Finite Groups

arXiv:2606.07619v1 Announce Type: new Abstract: We present a Graph Neural Network (GNN) framework for the classification of finite groups according to their solvability. Using graph representations as

How to unlock true ROI in software development – a deep dive into the latest DORA research

How do you prove the business value of generative AI to your teams? Technology and finance leaders need to show the clear business value of AI projects to secure ongoing funding. While measuring retur

tutorialsgoogle-cloud-ai

How to use Row-Bot to turn unread emails into a daily action plan

Row-Bot is a local-first desktop AI assistant that can check emails and integrate multiple tools in a single conversation turn . The application includes integrated tools, a personal knowledge graph,

tutorialsr-ollama

I don't really want to have to go to bat against Anthropic, but they've just been unnecessarily antagonistic to all of China, then not so su…

I don't really want to have to go to bat against Anthropic, but they've just been unnecessarily antagonistic to all of China, then not so subtly to open weight models, and now more broadly open AI res

imagine telling your customers there's a small chance you'll randomly decide they're using your product wrong and you won't tell them but wi…

This post critiques opaque AI moderation systems that penalize users without clear explanation, drawing parallels to how some companies might arbitrarily restrict customer access based on undisclosed

Improving Bayesian Optimization via Training-Aware Conditional Diffusion Models

arXiv:2606.08438v1 Announce Type: cross Abstract: Bayesian optimization (BO) is a widely used approach for black-box optimization that uses a Gaussian process (GP) as a surrogate and guides sequential

It's confusing that Trump constantly warns about the security of our elections & foreign interference, yet his administration has taken seve…

It's confusing that Trump constantly warns about the security of our elections & foreign interference, yet his administration has taken several steps to dismantle or divert resources away from key saf

tutorialsyann-lecun--x

June 9th Researcher Reciprocity License 'if you train on it, you let us generate - reverse terms of use void' Status quo 1. We teach frontie…

June 9th Researcher Reciprocity License 'if you train on it, you let us generate - reverse terms of use void' Status quo 1. We teach frontier devs with ICLR/NeurIPS papers, OSS Github contributions 2.

LEAF: A Learning-Enabled ADMM Framework for Accelerated Convex Optimization

arXiv:2606.08993v1 Announce Type: new Abstract: We propose LEAF, a learning-enabled ADMM framework for accelerated convex optimization. The key idea is to approximate the Moreau envelope of the object

Learnable Token Sparsification for Efficient Gigapixel Whole Slide Image Reasoning

arXiv:2606.08641v1 Announce Type: new Abstract: The processing of gigapixel whole slide images within vision language models faces a major difficulty due to an excessive number of visual tokens. Exist

Learning from flowsheets: A generative transformer model for autocompletion of flowsheets

arXiv:2208.00859v2 Announce Type: replace Abstract: We propose a novel method enabling autocompletion of chemical flowsheets. This idea is inspired by the autocompletion of text. We represent flowshee

Leveraging Morphology for Historical Script Metrological Analysis

arXiv:2606.09446v1 Announce Type: new Abstract: Advances in handwritten text recognition have enabled large-scale transcription of historical documents, but still provide limited access to interpretab

LLMs are useful but we still have a long way to go. See my 2020 article The Next Decade in Arxiv, which remains a good guide to the future a…

LLMs are useful but we still have a long way to go. See my 2020 article The Next Decade in Arxiv, which remains a good guide to the future and offers a road map that the companies are increasingly fol

tutorialsgary-marcus--x

LOTTERY: Learning from Reference-Only Samples in Two-Sample Testing under Size Asymmetry

arXiv:2606.08460v1 Announce Type: cross Abstract: Data-adaptive two-sample testing assesses if two samples come from the same distribution, using a discrepancy learned from the data (e.g., via kernel-

MixReasoning: Switching Modes to Think

arXiv:2510.06052v2 Announce Type: replace Abstract: Reasoning models enhance performance by tackling problems in a step-by-step manner, decomposing them into sub-problems and exploring long chains of

Mobility-Embedded POIs: Learning What A Place Is and How It Is Used from Human Movement

arXiv:2601.21149v3 Announce Type: replace-cross Abstract: Recent progress in geospatial foundation models highlights the importance of learning general-purpose representations for real-world locations

Neural Field Tokenizations with Hierarchy and Spatial Locality Priors

arXiv:2606.08204v1 Announce Type: cross Abstract: Neural fields parameterize data as functions from coordinates to values, providing a unified framework for representation learning across modalities.

Neural Legendre-Fenchel transform with Hessian Preconditioning

arXiv:2606.09077v1 Announce Type: new Abstract: The Legendre-Fenchel (LF) transform is a fundamental tool in convex analysis and machine learning that maps lower semi-continuous functions to their con

Not Just After One: Sleep-Inspired Replay Prevents Catastrophic Forgetting After Sequential Tasks

arXiv:2606.08447v1 Announce Type: cross Abstract: One of the critical limitations of artificial neural networks is their lack of ability to continually learn: training on new tasks often leads to inte

On the Wasserstein Geodesic Principal Component Analysis of probability measures

arXiv:2506.04480v2 Announce Type: replace-cross Abstract: This paper focuses on Geodesic Principal Component Analysis (GPCA) on a collection of probability distributions using the Otto-Wasserstein geo

One of my personal favorite features announced at WWDC will I suspect be a sleeper hit: container machines, allowing your Mac to run a light…

One of my personal favorite features announced at WWDC will I suspect be a sleeper hit: container machines, allowing your Mac to run a lightweight, persistent Linux environment with your home director

Open Source is the only way...

Open Source is the only way... First they came for the model builders... I feel we're getting a glimpse of a future where AI is only provided to a privileged few, and that's not a future I want to liv

Orange Lab: Lowering Barriers to Data Mining through Embedded Interactive Workflows

arXiv:2606.09239v1 Announce Type: new Abstract: While visual programming of data analysis workflows has become an important vehicle for the democratization of data science, such systems remain largely

Performative Learning Theory

arXiv:2602.04402v3 Announce Type: replace-cross Abstract: Performative predictions influence the very outcomes they aim to forecast. We study performative predictions that affect a sample (e.g., only

Post-training is (Massive) Supervised Learning

arXiv:2606.07527v1 Announce Type: cross Abstract: The prevailing paradigm for training LLMs has evolved to rely on a massive post-training phase consisting of SFT and RL. In this position paper, we ar

PRISM: Topology-Aware Cross-Modal Imputation for Modality-Deficient Federated Graph Learning

arXiv:2606.09301v1 Announce Type: new Abstract: Multimodal federated graph learning (MM-FGL) aims to collaboratively learn from decentralized graphs with text and images. However, real-world clients m

Propeller-Assisted Robust 3D Hopping Robot with Hierarchical Force Allocation

arXiv:2606.08186v1 Announce Type: new Abstract: Monopedal hopping robots are conceptually simple but highly dynamic and inherently unstable. Achieving robust 3D hopping is still difficult because grou

tutorialsarxiv-cs-ro

Public Machine Learning Solver Framework for Novices in the Machine Learning Domain

arXiv:2606.08212v1 Announce Type: new Abstract: Solving machine learning problems is complex and typically reserved for experts. Over the past two decades, systems have emerged to support non-experts.

Quantifying Noise of Dynamic Vision Sensor

arXiv:2404.01948v3 Announce Type: replace Abstract: Dynamic visual sensors (DVS) are characterized by a large amount of background activity (BA) noise, which it is mixed with the original (cleaned) se

ReTabSyn: Realistic Tabular Data Synthesis via Reinforcement Learning

arXiv:2603.10823v2 Announce Type: replace-cross Abstract: Deep generative models can help with data scarcity and privacy by producing synthetic training data, but they struggle in low-data, imbalanced

RGB-S: Image-Aligned Tactile Saliency for Robust Dexterous Manipulation

arXiv:2606.08765v1 Announce Type: cross Abstract: Effective visuo-tactile integration is critical for robotic dexterous manipulation, especially when visual observations are unreliable or occluded. Ho

Sample-Efficient LLM-Based Detection of Malicious Web Server Logs with Forensically Explainable Reasoning

arXiv:2606.08649v1 Announce Type: cross Abstract: Forensic analysis of web server logs demands both accurate detection and human-readable explanations that can satisfy legal requirements. We present C

Shortcuts in the Tail: Debiasing via Post-Hoc Spectral Compression of Fine-Tuning Updates

arXiv:2606.07596v1 Announce Type: new Abstract: Fine-tuning often introduces spurious correlations alongside task knowledge, causing systematic failures on underrepresented groups. Existing mitigation

Small: 30 billion parameters, 3B active. Efficient: Benchmarks to 33.4 on the Artificial Analysis Coding Index, competitive among similar si…

Small: 30 billion parameters, 3B active. Efficient: Benchmarks to 33.4 on the Artificial Analysis Coding Index, competitive among similar sized models. Open Source: Apache 2.0 license so developers ca

tutorialscohere--x