Knowledge catalogue
arXiv:2608.01029v1 Announce Type: new Abstract: Musculoskeletal robots require an internal body schema that remains consistent under a wide range of physical state changes, including abnormalities suc
arXiv:2608.01740v1 Announce Type: new Abstract: Training-free feature forecasting accelerates diffusion sampling by predicting features at skipped denoising steps. Recent work has mainly focused on de
arXiv:2608.01174v1 Announce Type: new Abstract: This study examines Transformer-based models' ability to learn emoji pragmatics in Arabic digital discourse (ADD), providing evidence from MARBERT's beh
arXiv:2608.00994v1 Announce Type: cross Abstract: Zero-shot image captioning aims to generate image descriptions without annotated image-text pairs. Recent approaches exploit text-to-image models to s
arXiv:2608.00821v1 Announce Type: new Abstract: Whether idiosyncratic, item-specific knowledge is learned before abstract class-level generalizations, or vice versa, is a central question in language
arXiv:2608.02433v1 Announce Type: new Abstract: Reinforcement learning and control theory are two adjacent scientific fields that focus on optimizing the controller of unknown dynamical systems using
arXiv:2608.01104v1 Announce Type: new Abstract: Whole slide image (WSI) classification is an evidence-driven task, where diagnostic cues are often sparse, spatially organized, and class-dependent. Exi
arXiv:2608.02378v1 Announce Type: new Abstract: Private inference protects both user inputs and server models during neural network inference, but existing solutions remain too slow for practical depl
arXiv:2608.01677v1 Announce Type: new Abstract: Myocardial strain analysis of cardiac magnetic resonance (CMR) images provides an important tool for evaluating cardiac function. However, current techn
arXiv:2608.02366v1 Announce Type: new Abstract: Multivariate time series imputation is fundamental to downstream analysis, yet modeling inter-variable dependencies with incomplete observations remains
arXiv:2608.02140v1 Announce Type: new Abstract: Much of the recent progress in image and video recognition has come at the cost of memory: larger models, increased resolution, and longer temporal cont
Learn how to build tools to simplify how you work—without writing a single line of code. The post How the GitHub legal team used Copilot CLI to streamline their workflows appeared first on The GitHub
Learn how Arize Signal turns production traces into ranked issues, proposed fixes, regression datasets, and reviewable pull requests for AI agents. The post How to debug production AI agents with Sign
arXiv:2511.20635v3 Announce Type: replace Abstract: Pre-trained video models learn powerful priors for generating high-quality, temporally coherent content. While these models excel at temporal cohere
arXiv:2407.12288v5 Announce Type: replace-cross Abstract: The progress of machine learning over the past decade is undeniable. In retrospect, it is both remarkable and unsettling that this progress wa
arXiv:2608.00264v1 Announce Type: new Abstract: Vision foundation models, such as DINOv2, learn highly expressive representations but rely on massive, opaque architectures that demand substantial comp
Today, we are introducing the general availability of Web Search on Amazon Bedrock. It is a server-side built-in tool that grounds model responses in current web knowledge. With Web Search, grounding
arXiv:2608.02081v1 Announce Type: new Abstract: In this paper, we study prediction problems for paired comparison data, for example, predicting the win probability between two unmatched players and ra
arXiv:2608.01562v1 Announce Type: new Abstract: Optimal trajectory generation for rigid-body motions on Lie groups can be formulated as a variational problem that minimizes energy functionals defined
arXiv:2608.01730v1 Announce Type: new Abstract: No-reference image quality assessment (NR IQA) has recently benefited from deep and multimodal models, yet many SOTA systems still violate at least one
arXiv:2608.01582v1 Announce Type: cross Abstract: Symmetry is central to modern machine learning and physics: invariances and equivariances improve sample efficiency, robustness, and out-of-distributi
arXiv:2608.00868v1 Announce Type: new Abstract: Many open educational resources are lacking in accessibility, especially in-depth image descriptions. In subjects like Science and Mathematics, however,
arXiv:2608.00400v1 Announce Type: new Abstract: Nonlocal partial differential equations arise in many applications but are often difficult to model and learn because of the presence of nonlocal operat
arXiv:2608.00064v1 Announce Type: new Abstract: Generative models learn the statistical properties of their training data, so high-quality generation depends on clean and representative datasets. In s
arXiv:2602.06737v2 Announce Type: replace Abstract: We present a generalized framework for the range verification of neural networks featuring non-linear activation functions. Our approach first const
arXiv:2608.01825v1 Announce Type: new Abstract: Part-level 3D generation has recently attracted increasing attention for producing structured and editable 3D assets. However, existing methods typicall
arXiv:2608.01600v1 Announce Type: new Abstract: Humanoid robots, with their human-like shape and multi-tasking capabilities, are well-aligned with human-dominated workplaces, like those in civil and c
arXiv:2608.01354v1 Announce Type: new Abstract: Recent studies develop pixel-level multimodal large language models (MLLMs) that support both Region Segmentation and Region Understanding, extending mu
arXiv:2608.01475v1 Announce Type: new Abstract: Neural networks that can grow or both grow and shrink during learning, referred to as growing neural networks and elastic neural networks, respectively,
arXiv:2608.01495v1 Announce Type: new Abstract: Detection transformer models, including DETR and its extensions, learn to output a set of object-level embeddings that can be simultaneously decoded int
arXiv:2608.02176v1 Announce Type: cross Abstract: We study the round complexity of learning a hidden partition P of an n-element universe using PAIR queries: PAIR(x,y) tells us whether x and y belong
arXiv:2510.21590v3 Announce Type: replace Abstract: Current image super-resolution methods show strong performance on natural images but distort text, creating a fundamental trade-off between image qu
arXiv:2608.02027v1 Announce Type: new Abstract: We present scikit-fingerprints, a comprehensive, fully scikit-learn compatible library for molecular machine learning in Python, based on RDKit. Molecul
arXiv:2608.02145v1 Announce Type: new Abstract: In this paper, we propose UniqueSplat, a view-conditioned feed-forward 3D Gaussian Splatting model to reconstruct customized 3D radiance fields for each
arXiv:2608.00530v1 Announce Type: new Abstract: Infrared-visible image fusion under realistic degradation scenarios is a challenging task, as degradations not only cause a loss of reliable modality-sp
arXiv:2603.19040v2 Announce Type: replace Abstract: Differentially private wireless federated learning (DPWFL) is a promising framework for protecting sensitive user data. However, foundational questi
arXiv:2607.28733v1 Announce Type: cross Abstract: This proceedings contribution elaborates on the findings of arXiv:2605.26234v2: a joint work with Marco Usula, where we introduced a machine learning
arXiv:2408.03230v2 Announce Type: replace Abstract: Quantifying and evaluating image complexity can be instrumental in enhancing the performance of various computer vision tasks. Supervised learning c
arXiv:2607.19345v2 Announce Type: replace-cross Abstract: Large language models that generate step-by-step reasoning traces have achieved strong performance on complex tasks, and extending them to lon
arXiv:2607.29337v1 Announce Type: cross Abstract: Background and Objective: Generating realistic medical images with anatomically accurate segmentation masks helps address the shortage of annotated da
arXiv:2509.25146v2 Announce Type: replace-cross Abstract: This paper develops a mathematical argument and algorithms for building representations of data from event-based cameras, that we call Fast Fe
arXiv:2607.29235v1 Announce Type: cross Abstract: Although world-action models (WAMs) enhance long-horizon robot control by predicting visual evolution before acting, long-horizon reliability demands
arXiv:2607.28935v1 Announce Type: new Abstract: Recently, 3D semantic occupancy prediction has garnered increasing attention for understanding the indoor scene. However, unlike structured outdoor envi
Of course, the concern is that in the end, this thread will be fed into the models' training data, but I feel benchmarking isn't so open and very fragmented. submitted by /u/jinnyjuice [link] [comment
arXiv:2603.25464v2 Announce Type: replace-cross Abstract: Zero-shot reinforcement learning (RL) algorithms aim to learn a family of policies from a reward-free dataset, and recover optimal policies fo
arXiv:2607.29180v1 Announce Type: cross Abstract: Text-to-motion generation must produce motions that are semantically correct, temporally coherent, and physically plausible. A natural approach is to
arXiv:2607.29221v1 Announce Type: cross Abstract: We address the challenge of securely and efficiently outsourcing AI computations from a trusted but computationally weak client to an untrusted but po
arXiv:2607.29000v1 Announce Type: cross Abstract: Multimodal information can improve the accuracy of click-through rate (CTR) prediction and effectively alleviate item cold-start and long-tail problem
arXiv:2308.08794v4 Announce Type: replace Abstract: Tipping points are abrupt, drastic, and often irreversible changes in the evolution of non-stationary and chaotic dynamical systems. For instance, i
arXiv:2607.28643v1 Announce Type: cross Abstract: Automating facilitation in online discussions is a long-standing social concern given the increasing time we spend on online spaces and the failure of
arXiv:2607.29592v1 Announce Type: new Abstract: The primary challenge of continual learning (CL) systems is to learn new tasks while remaining performant on previously learned tasks. A similarly impor
arXiv:2607.29032v1 Announce Type: cross Abstract: Large language model (LLM) agents increasingly operate over long interaction histories, where effective reasoning requires identifying and exploiting
arXiv:2607.29256v1 Announce Type: new Abstract: Designing polyimide structures with specific glass transition temperatures (Tg) is highly challenging. Existing methods primarily focus on target-condit
The egg test for AI 'I hereby offer @elonmusk a million dollar bet against his prediction that Optimus will be better than the best humans in surgery by the end of the decade.' ~ @GaryMarcus Haha. I'm
Kimi K3 has set a new bar for OSS model intelligence! 2.8T params, 1M context, OpenAI-compatible API. Complete guide to running Kimi K3 on Together AI 👇 👏👏 @Kimi_Moonshot 👏👏 https://www.together.ai/bl
**Kimi K3 is Moonshot AI’s 2.8‑trillion‑parameter open‑weight language model—the largest ever released—designed for frontier tasks such as long‑horizon coding and deep reasoning.** Its architecture us
Our Twitch streams with @Cohere_Labs are #1 in the tech category⚡️ Cohere Labs' free ML Summer School series has brought together viewers from 20+ countries to learn about everything from NLP in LLMs
TLDR: There is no one prompting trick that will result in Krea 2 Turbo giving you exactly the style you want and across the whole image. Instead, if you are trying to achieve styles without the use of
arXiv:2607.28487v1 Announce Type: new Abstract: Fine-grained segmentation of auricular structures in CT is challenging because the ear occupies a small image region, cartilage boundaries are highly ir
arXiv:2607.27953v1 Announce Type: new Abstract: Combinatorial optimization problems (COPs) underpin many real-world decisions, but their exponentially large search spaces make high-quality solutions c