Search: “tutorials”

Cursor AI has rolled out effort levels functionality to all users on usage-based Bugbot plans, allowing users to configure these settings directly through their Bugbot dashboard. This feature enables

arXiv:2605.08073v1 Announce Type: cross Abstract: Recent event-based image reconstruction methods predominantly rely on Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) to process c

arXiv:2605.07097v1 Announce Type: cross Abstract: We show that, in a precise sense, a broad class of feedforward neural networks learn (have finite sample complexity) in the PAC model: every fixed fin

Explore the beginner's guide to ComfyHub. ComfyHub is the public home for community-built workflows and apps — the fastest way to find and run what the ComfyUI ecosystem has already created, without n

arXiv:2510.01510v3 Announce Type: replace Abstract: We study the problem of zero-shot link prediction on knowledge graphs (KGs), which requires models to generalize to novel entities and novel relatio

arXiv:2605.07474v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models hold great promise for general-purpose robotic intelligence, yet scaling up such models is severely bottlenecked b

arXiv:2605.07203v1 Announce Type: new Abstract: Scene change detection methods built on Gaussian splatting universally follow a render-then-compare paradigm: the pre-change scene is rendered into 2D a

arXiv:2605.07933v1 Announce Type: new Abstract: Latent diffusion models offer an attractive alternative to discrete diffusion for non-autoregressive text generation by operating on continuous text rep

arXiv:2508.05803v2 Announce Type: replace Abstract: Human memory is fleeting. As words are processed, the exact wordforms that make up incoming sentences are rapidly lost. Cognitive scientists have lo

arXiv:2506.12362v3 Announce Type: replace-cross Abstract: Inductive link prediction with knowledge hypergraphs is the task of predicting missing hyperedges involving completely novel entities (i.e., n

I'm delighted that @coursera and @udemy have come together as one company to serve learners. Both Coursera and Udemy were founded with the belief that access to high-quality education changes lives. O

arXiv:2605.07491v1 Announce Type: new Abstract: This paper proposes a novel framework for implicit multi-camera system calibration utilizing Gaussian Process (GP) regression. Conventional explicit cal

arXiv:2605.07010v1 Announce Type: new Abstract: Identifying vulnerable transmission lines in power grids before a cascading failure occurs is challenging: existing methods can learn inter-line failure

arXiv:2605.07648v1 Announce Type: new Abstract: Learning parity functions, more general modular addition, is a challenging machine learning task due to its input sensitivity. A recent study substantia

arXiv:2310.02243v2 Announce Type: replace-cross Abstract: We study the problem of learning a local quantum Hamiltonian H given copies of its Gibbs state rho = e^{-eta H}/extrm{tr}(e^{-eta H}) at a kno

arXiv:2605.06676v1 Announce Type: cross Abstract: Long-context inference in Large Language Models (LLMs) is bottlenecked by the linear growth of Key-Value (KV) cache memory. Existing KV cache compress

// LLMs Improving LLMs // Interesting progress the past of couple of weeks around self-improving AI agents. If autoresearch was interesting, you will like this read. (bookmark it) We've been hand-tuni

arXiv:2605.06809v1 Announce Type: new Abstract: Transformers dominate video recognition. They split videos into tokens, and processing them has expensive superlinear computational cost. Yet videos are

arXiv:2602.03797v2 Announce Type: replace Abstract: We present a new paradigm for creating random features to approximate bi-variate functions (in particular, kernels) defined on general manifolds. Th

arXiv:2605.06317v2 Announce Type: replace-cross Abstract: Existing Vision-Language Navigation (VLN) methods typically adopt an egocentric, step-by-step paradigm, which struggles with error accumulatio

arXiv:2605.07140v1 Announce Type: cross Abstract: Skeleton-based human activity recognition has achieved strong empirical performance, yet most existing models remain black boxes and difficult to inte

arXiv:2605.07660v1 Announce Type: new Abstract: Reinforcement-learning-based post-training has become a key approach for improving the reasoning ability of large language models, but its token-level l

arXiv:2605.06873v1 Announce Type: cross Abstract: Probabilistic conditioning is concerned with the identification of a distribution of a random variable X given a random variable Y. It is a cornerston

arXiv:2605.07572v1 Announce Type: new Abstract: When applying Bayesian optimization (BO) to scientific workflow, a major yet often overlooked source of uncertainty is the task itself -- namely, what t

arXiv:2605.06833v1 Announce Type: cross Abstract: Misbehavior detection in Vehicle-to-Everything (V2X) networks is a second line of defense against insider falsification attacks that cryptographic mec

arXiv:2602.03490v2 Announce Type: replace Abstract: Adaptive cognition requires structured internal models of objects and their relations. Predictive neural networks are often proposed to learn such w

arXiv:2605.08035v1 Announce Type: cross Abstract: Building a site-specific propagation model typically requires either ray-tracing over detailed 3D maps or dense measurement campaigns. Both approaches

arXiv:2605.07938v1 Announce Type: new Abstract: Single-cell representation learning (SCRL) from gene expression data offers a way to uncover the complex regulatory logic underlying cellular function.

arXiv:2605.07755v1 Announce Type: cross Abstract: The theory of state tracking in recurrent architectures has predominantly focused on expressive capacity: whether a fixed architecture can theoretical

arXiv:2605.07212v1 Announce Type: cross Abstract: Electroencephalography (EEG) is a cornerstone of brain-computer interfaces and clinical neuroscience, yet deep learning models are typically trained a

arXiv:2602.03201v3 Announce Type: replace Abstract: Model-based reinforcement learning (MBRL) is sample-efficient but struggles in sparse reward settings. A critical bottleneck arises from the lack of

arXiv:2605.07908v1 Announce Type: cross Abstract: Belief functions are a powerful and popular framework for the mathematical characterisation of uncertainty, in particular in situations in which lack

arXiv:2405.00082v4 Announce Type: replace-cross Abstract: We study the problem of Hamiltonian structure learning from real-time evolution: given the ability to apply e^{-i Ht} for an unknown local Ham

arXiv:2605.07676v1 Announce Type: new Abstract: Standard flow matching scales well but typically relies on an unstructured source distribution, limiting its ability to learn interpretable latent struc

arXiv:2503.14998v3 Announce Type: replace Abstract: Contrastive learning methods in computer vision typically rely on augmented views of the same image or multimodal pretraining strategies that align

arXiv:2605.07086v1 Announce Type: new Abstract: Channel importance in vision networks is usually summarized by a single score. That summary hides two different questions: how much a channel is related

// The Memory Curse in LLM Agents // (bookmark it) Long histories apparently degrades agents as they become increasingly history-following and risk-minimizing. Across 7 LLMs and 4 social dilemma games

arXiv:2605.07471v1 Announce Type: new Abstract: Machine-learning models in high-energy physics are often trained on simulated data, where fully simulated samples are computationally expensive while fa

arXiv:2605.07922v1 Announce Type: new Abstract: Learning hierarchical features in Sparse Autoencoders (SAEs) is essential for capturing the structured nature of real-world data and mitigating issues l

arXiv:2509.22813v2 Announce Type: replace Abstract: State Space Models (SSMs) have emerged as efficient alternatives to Vision Transformers (ViTs), with VMamba standing out as a pioneering architectur

TurboQuant has drawn a lot of attention recently, but the accompanying evals didn't tell the full story. So we ran what I believe is the first comprehensive study of TurboQuant: where it helps, where

arXiv:2605.07470v1 Announce Type: new Abstract: Neural networks (NNs) are inherently multidimensional classifiers that learn complex, non-linear relationships among input observables. While their flex

arXiv:2605.07146v1 Announce Type: new Abstract: Underwater salient object detection (USOD) plays a vital role in marine vision tasks but remains fundamentally challenging due to severe visual degradat

arXiv:2508.06819v3 Announce Type: replace Abstract: The task of parsing subcutaneous vessels in clinical images is often hindered by the high cost and limited availability of ground truth data, as wel

arXiv:2605.07120v1 Announce Type: new Abstract: Template tasks have emerged as a clean testbed for asking whether transformers reason with abstract symbols rather than concrete token names. We study t

arXiv:2605.06831v1 Announce Type: cross Abstract: We theoretically study the hallucination phenomena in two canonical diffusion samplers: the stochastic Denoising Diffusion Probabilistic Model (DDPM)