Knowledge catalogue
arXiv:2601.10222v2 Announce Type: replace-cross Abstract: Optimization is central to both modern machine learning (ML) and scientific machine learning (SciML), yet the structure of the underlying opti
arXiv:2606.03068v1 Announce Type: cross Abstract: While Virtual Nodes (VNs) are often utilized in Message Passing Neural Networks (MPNNs) to facilitate effective message passing, existing VN-based met
arXiv:2606.02841v1 Announce Type: new Abstract: Deep neural networks learn representations where individual features often lack interpretable meaning; a single neuron may activate for scattered, unrel
arXiv:2507.16003v4 Announce Type: replace Abstract: One of the most striking features of Large Language Models (LLMs) is their ability to learn in-context. Namely at inference time an LLM is able to l
arXiv:2601.22601v2 Announce Type: replace Abstract: Federated unlearning (FU) aims to erase designated client-level, class-level, or sample-level knowledge from a global model. Existing studies common
arXiv:2606.02907v1 Announce Type: cross Abstract: Linear probing of large language model (LLM) hidden states is widely used to claim that models learn distinct representations for different reasoning
Nice primer on post-training reasoning data. (bookmark it) This is one of the first primers to pull the scattered post-training reasoning-data literature into one place, synthesizing over 150 public s
arXiv:2606.02623v1 Announce Type: cross Abstract: Solving time-dependent partial differential equations (PDEs) is an important problem in computational science and engineering. Physics-informed neural
Perplexity Computer is for growing businesses. Computer connects to 400+ tools for every type of company, including Intuit QuickBooks, Vercel, Shopify, Canva, and more. Learn more about how people are
arXiv:2602.09708v2 Announce Type: replace-cross Abstract: We propose physics-informed spectral diffusion (PISD), a methodology that combines generative latent diffusion models with physics-informed ma
arXiv:2604.07048v2 Announce Type: replace Abstract: Real-world image dehazing (RID) aims to remove haze-induced degradation from real scenes. This task remains challenging due to non-uniform haze dist
arXiv:2606.03808v1 Announce Type: cross Abstract: We propose PURGE, a machine unlearning algorithm built on a simple but an under-exploited observation: continual learning (CL) and machine unlearning
arXiv:2606.03843v1 Announce Type: cross Abstract: Continual learning methods aim to maximize the stability and plasticity of machine learning models that are trained on a sequence of tasks. The standa
arXiv:2606.03782v1 Announce Type: new Abstract: Large language models (LLMs) offer a promising approach to machine translation (MT) for extremely low-resource languages by incorporating linguistic res
In this post, we look at how to use SOCI on publicly available Deep Learning AMIs and Containers, when to use the various SOCI modes provided by the tool, and how to quickly and efficiently use this t
arXiv:2511.13663v2 Announce Type: replace-cross Abstract: How to construct globally sound abstract interpreters to safely approximate program behaviors remains a bottleneck in abstract interpretation.
arXiv:2606.02927v1 Announce Type: new Abstract: Normalization layers such as BatchNorm and LayerNorm have long been considered essential for stable training in deep networks. This work demonstrates th
arXiv:2606.03715v1 Announce Type: new Abstract: Text-to-image models rely on text prompts as their primary interface to human intent. Prompts are encoded by a text encoder into embeddings that conditi
the best agents aren't just built with the best models: they're built with harnesses purpose-built for the task at hand here's a guide on how to build a harness that's really good at feeding the model
THE PROMPT THAT REPLACES YOUR WORKFLOW AUDIT: You are my business transformation strategist. I want you to find the real AI-era business transformation hiding inside my work by starting from goals, bu
arXiv:2606.03204v1 Announce Type: new Abstract: Underwater manipulation often occurs under degraded visibility due to turbidity, glare, and gripper occlusion, limiting the reliability of vision-based
arXiv:2509.03376v2 Announce Type: replace Abstract: Hyperspectral unmixing (HU) targets to decompose each mixed pixel in remote sensing images into a set of endmembers and their corresponding abundanc
arXiv:2606.03971v1 Announce Type: new Abstract: Causal video generators must predict from the past, but they need not learn only from it. In streaming autoregressive video diffusion, each emitted segm
arXiv:2606.02673v1 Announce Type: new Abstract: Graphs have been used to enhance large language models (LLMs) for structured reasoning, mostly as external knowledge sources are provided to models at t
We have partnered with @Cognition to bring their AI coding platform, Devin, to the Public Sector. Together, we’re helping organizations modernize software development and accelerate secure AI adoption
arXiv:2606.03052v1 Announce Type: new Abstract: Knowledge Distillation (KD) is a powerful tool for model compression, yet the precise mechanisms by which student models acquire feature representations
A Reddit discussion from r/ChatGPT where users share experiences using AI tools like ChatGPT to write code despite lacking programming knowledge or expertise. The thread likely documents how individua
Working with @FireworksAI_HQ to make MAI models easy to fine-tune and fully yours. Microsoft MAI models. Coming soon to Fireworks. Intelligence you control. End-to-end lineage you can prove. Fine-tune
arXiv:2602.01135v3 Announce Type: replace Abstract: Autoregressive models trained via next-token prediction implicitly learn the conditional independence structure of their data-generating process. We
Learn a practical model lifecycle for Microsoft Foundry: select the right model, evaluate quality, optimize cost, operate safely, and improve as production needs change. The post A Developer’s Guide t
arXiv:2606.01283v1 Announce Type: new Abstract: Modeling spatial dependencies is central to spatiotemporal data analysis using Graph Neural Networks (GNNs). Traditional methods rely on distance-based
arXiv:2508.17320v3 Announce Type: replace Abstract: Understanding the internal representations of large language models (LLMs) remains a central challenge for interpretability research. Sparse autoenc
arXiv:2606.00137v1 Announce Type: new Abstract: Texturing 3D meshes plays a vital role in determining the visual realism of digital objects and scenes. Although recent generative 3D approaches based o
arXiv:2606.00461v1 Announce Type: new Abstract: Tremor is a common movement disorder associated with conditions like Parkinson's disease and Essential tremor, traditionally diagnosed through expert cl
arXiv:2606.00892v1 Announce Type: new Abstract: The treatment of ischemic stroke using mechanical thrombectomy involves difficult decisions under intense time constraints. Numerical physics simulation
arXiv:2606.01044v1 Announce Type: new Abstract: Medical visual question answering requires models to ground their responses in image evidence, because visually unsupported answers can mislead downstre
Your healthcare app needs “La médica” not “El médico.” Your legal documents need precise terminology, not generic translations. When domain-specific language matters, generic LLM translation falls sho
arXiv:2606.00340v1 Announce Type: new Abstract: We study optimal learning-rate selection in two-layer and three-layer linear neural networks trained to learn linear target functions. In particular, we
arXiv:2606.01066v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) replaces human preference labels with executable reward functions such as math answer checkers, JS
arXiv:2606.00038v1 Announce Type: cross Abstract: Artificial intelligence (AI) literacy is increasingly recognized as a foundational competency for all university graduates. Yet students' engagement w
arXiv:2407.01374v2 Announce Type: replace Abstract: Malaysian English is a low resource creole language, where it carries the elements of Malay, Chinese, and Tamil languages, in addition to Standard E
arXiv:2606.01372v1 Announce Type: cross Abstract: Can neural networks learn abstract algebraic rules, or do they merely memorize training patterns? We investigate this using MNIST digits as states and
Learn how Microsoft helps developers build trustworthy AI agents with open evaluations, portable runtime controls, production observability, and security workflows that work across frameworks. The pos
arXiv:2510.27249v2 Announce Type: replace Abstract: Deep neural networks (DNNs) have achieved remarkable success in computer vision tasks such as image classification, segmentation, and object detecti
arXiv:2602.06033v2 Announce Type: replace Abstract: Pre-trained vision language models do not have good intuitions about the physical world. Recent work has shown that supervised fine-tuning can impro
arXiv:2606.02224v1 Announce Type: new Abstract: The evolution and dissemination of AI-synthesized images is occurring at an unprecedented rate. Image generators are making rapid progress in their goal
Climbing with no distillation, like the Big Boys do, has been super fun! Read the tech report for a taste of our ̶s̶u̶f̶f̶e̶r̶i̶n̶g̶ journey Super excited to announce seven new world-class MAI models
arXiv:2506.22881v3 Announce Type: replace Abstract: Density ratio estimation is a core concept in statistical machine learning because it provides a unified mechanism for tasks such as importance weig
arXiv:2606.00327v1 Announce Type: cross Abstract: Clustering is widely used across the sciences as the foundation for downstream data-driven scientific discoveries. However, clustering results are hig
arXiv:2606.00950v1 Announce Type: new Abstract: Unsupervised skill discovery (USD) aims to learn diverse behaviors without reward functions, but often results in task-irrelevant or hazardous behaviors
arXiv:2606.00667v1 Announce Type: cross Abstract: It has been proposed that the brain integrates flexible, computationally expensive cortical processing with simpler, lower-cost subcortical mechanisms
arXiv:2606.02170v1 Announce Type: new Abstract: Real-world scenarios involve massive heterogeneous structured data (e.g., tables, knowledge graphs), making effective reasoning over such diverse data i
arXiv:2606.02310v1 Announce Type: new Abstract: Flooding is the most pervasive natural disaster worldwide. Timely and accurate flood inundation mapping are essential for informing disaster risk manage
arXiv:2602.06065v3 Announce Type: replace-cross Abstract: Understanding how the structure of language can be learned from sentences alone is a central question in both cognitive science and machine le
arXiv:2606.02002v1 Announce Type: new Abstract: Blind image quality assessment (BIQA) aims to predict perceived image quality without access to a reference image. Classical natural scene statistics (N
arXiv:2606.01634v1 Announce Type: cross Abstract: Generating realistic time series is essential for scientific research and real-world applications. However, existing methods often emphasize overall d
arXiv:2411.03163v4 Announce Type: replace-cross Abstract: In this work, we initiate the study of Hamiltonian learning for positive temperature bosonic Gaussian states, the quantum generalization of th
arXiv:2606.01730v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used as heuristic advisors for black-box optimization, yet their suggestions and self-reported confidence
This article likely discusses the closure or significant changes to AI2 (Allen Institute for AI), a prominent artificial intelligence research organization founded by Paul Allen. The piece probably co
arXiv:2601.16462v2 Announce Type: replace Abstract: Retrieval-Augmented Generation (RAG) has become a dominant paradigm for mitigating hallucinations in Large Language Models (LLMs) by incorporating e