Knowledge catalogue
arXiv:2606.28655v1 Announce Type: cross Abstract: Parameterized quantum circuits (PQCs) provide a flexible substrate for hybrid quantum machine learning (QML), but their practical value on Noisy Inter
arXiv:2606.28882v1 Announce Type: cross Abstract: Large Language Models (LLMs) have shown the potential to generate code explanations that surpass those of peers in quality, offering promising opportu
arXiv:2606.28457v1 Announce Type: new Abstract: Natural language processing (NLP) applications need large and rich amount of linguistic knowledge. Furthermore, electronic language sources such as dict
arXiv:2606.30328v1 Announce Type: cross Abstract: Rapid prototyping of algorithms is a critical step in modern machine learning. Most algorithms exploit linear algebra, creating a need for lightweight
arXiv:2606.15129v2 Announce Type: replace-cross Abstract: Color fundus photography (CFP) is the mainstay of large-scale retinal screening, but its diagnostic capacity is limited by the lack of depth-r
arXiv:2606.30145v1 Announce Type: new Abstract: Natural face-to-face conversation requires real-time speech generation together with synchronized facial motion. Existing systems only partially address
arXiv:2606.30489v1 Announce Type: cross Abstract: Normalizing Flows excel at modeling a single fixed density, yet many problems across the sciences, such as high energy physics, instead require modeli
arXiv:2606.28476v1 Announce Type: new Abstract: High-precision humanoid control is limited by target-domain dynamics mismatch, where the same control objective can induce different realized motions un
arXiv:2606.29431v1 Announce Type: new Abstract: Despite the impressive capabilities of Large Vision-Language Models (LVLMs), they remain susceptible to hallucination, generating content inconsistent w
arXiv:2602.12155v2 Announce Type: replace Abstract: Post-training of flow matching models-aligning the output distribution with a high-quality target-is mathematically equivalent to imitation learning
arXiv:2606.29064v1 Announce Type: cross Abstract: The unfairness of recommender systems has become a topic of concern due to its significant social and ethical implications. Although existing works ha
arXiv:2606.29783v1 Announce Type: cross Abstract: Vision-based aerial tracking is critical in GPS-denied environments. Reliable perception for tracking depends on large-scale labeled data, yet most ph
arXiv:2601.17146v2 Announce Type: replace-cross Abstract: Empirical investigations into unintended model behavior often show that the algorithm is predicting another outcome than what was intended. Th
arXiv:2606.28607v1 Announce Type: cross Abstract: This work tackles the challenge of enhancing low-resolution LiDAR sensors for SLAM applications through a novel Deep Unrolling-based Super-Resolution
arXiv:2606.29350v1 Announce Type: cross Abstract: Vision-language models and vision-language action models endow the robot with unprecedented capabilities. However, the input of video and high-resolut
arXiv:2507.06764v5 Announce Type: replace-cross Abstract: In this work, we propose Fast Equivariant Imaging (FEI), a novel unsupervised learning framework to rapidly and efficiently train deep imaging
arXiv:2606.29734v1 Announce Type: new Abstract: Earnings announcements release two types of information sequentially: quantitative surprise (numeric earnings-per-share (EPS)/revenue versus analyst est
arXiv:2606.29640v1 Announce Type: cross Abstract: While wireless foundation models (FMs) are demonstrating strong potential to enable AI-Native 6G networks, their high computational cost remains a cri
arXiv:2606.30352v1 Announce Type: new Abstract: Recent advances in 3D scene reconstruction have highlighted the intricate trade-offs among rendering quality, inference efficiency, and data dependency.
arXiv:2606.29493v1 Announce Type: new Abstract: Benchmarks for LLM-assisted theorem proving in Lean are often treated as intrinsically reliable because every solved instance comes with a machine-check
arXiv:2505.22578v2 Announce Type: replace Abstract: The optimization of neural networks under weight decay remains poorly understood from a theoretical standpoint. While weight decay is standard pract
arXiv:2603.25144v2 Announce Type: replace-cross Abstract: Dataset distillation (DD) compresses a large training set into a small synthetic set, reducing storage and training cost, and has shown strong
arXiv:2606.29319v1 Announce Type: new Abstract: Image-based Virtual Try-On (IVTON) has greatly advanced through diffusion models, yet existing methods require many sampling steps and depend on masks w
arXiv:2603.13326v2 Announce Type: replace-cross Abstract: Multimodal Transformers often produce predictions without clarifying how different modalities jointly support a decision. Most existing multim
Hugging Face introduced a feature that displays evaluation results from the 'Every Eval Ever' (EEE) community evaluation initiative directly on model pages, providing users with comprehensive benchmar
Featuring the man of the moment @dylan522p @shaunmmaguire in today's Training Data episode. Nobody is a more trusted industry insider to the biggest infrastructure build-out in history. The story of h
arXiv:2405.00742v2 Announce Type: replace-cross Abstract: Mitigating cybersecurity risk in electric vehicle (EV) charging demand forecasting plays a crucial role in the safe operation of collective EV
arXiv:2606.30161v1 Announce Type: cross Abstract: Federated learning typically aggregates client updates using fixed or heuristic weighting rules, which can be suboptimal when clients have heterogeneo
arXiv:2606.28654v1 Announce Type: cross Abstract: Deep Neural Network (DNN) classifiers suffer from poor calibration when their softmax outputs (predictive confidence) deviate from the empirical likel
arXiv:2509.26468v3 Announce Type: replace Abstract: Benchmark quality is critical for meaningful evaluation and sustained progress in time series forecasting, particularly with the rise of pretrained
arXiv:2606.28391v1 Announce Type: cross Abstract: The wide use of Convolutional Neural Networks (CNN) in numerous domains and real-world classification applications is justified by their high precisio
arXiv:2606.30190v1 Announce Type: cross Abstract: Existing domain-incremental learning (DIL) strategies call for massive amounts of data to adapt to new domains and suffer from the overfitting problem
arXiv:2606.29110v1 Announce Type: new Abstract: Recent progress in flow-based generative modeling has led to models that output high-quality samples while using only a small number of function evaluat
arXiv:2606.30347v1 Announce Type: cross Abstract: We present FFAvatar, a Transformer-based 3D Gaussian framework for fast construction of high-quality and animatable 4D head avatars from one or more r
arXiv:2606.30473v1 Announce Type: cross Abstract: We study retrieval over catalogs of structured metadata, where each record is a small schema whose fields answer different kinds of query. Embedding a
Reuters: Filing: President Trump reports 1.4B+ in income from his family's crypto ventures in 2025, including 500M+ from WLF and 635M from the sale of his TRUMP coin — U.S. President Donald Trump repo
Wall Street Journal: Filings: Microsoft's Ireland hub generated $47B in pretax profits for FY 2025, or 38.1% of its global total; new EU rules require country-by-country reporting — Country-by-country
arXiv:2606.28404v1 Announce Type: cross Abstract: Artificial intelligence depends on large-scale compute resources and their supporting infrastructure. However, AI governance debates treat compute pri
In this post, you'll learn how fine-tuning Amazon Nova models using Amazon SageMaker AI addresses these specific issues by teaching the models to recognize your exact data patterns, distinguish betwee
arXiv:2606.28992v1 Announce Type: cross Abstract: General-purpose large language models (LLMs) have demonstrated strong abilities in opendomain question answering, information extraction, and text gen
arXiv:2606.28933v1 Announce Type: new Abstract: Venture capital (VC) investment decisions face distinct challenges, such as multi-source heterogeneous data, non-stationary time series, and the demand
arXiv:2606.29414v1 Announce Type: new Abstract: Periodic Implicit Neural Representations (INRs) such as SIREN and FINER assign every neuron, the same global frequency, spending the representational bu
arXiv:2606.29972v1 Announce Type: new Abstract: Most of the existing neuro-symbolic AI methods focus on the scenario of static knowledge where objects do not change according to a temporal dimension.
arXiv:2606.28835v1 Announce Type: cross Abstract: Federated Learning (FL) emerged as a promising distributed machine learning paradigm. However, extending FL to the class incremental learning scenario
Reuters: Five Chinese tech and advanced manufacturing companies launched Hong Kong listings today to raise up to 5.6B, led by Apple supplier Luxshare's 3.15B offering — Five Chinese technology and adv
arXiv:2412.02831v2 Announce Type: replace-cross Abstract: The increasing accessibility of radiometric thermal imaging sensors for unmanned aerial vehicles (UAVs) offers significant potential for advan
arXiv:2603.16016v2 Announce Type: replace-cross Abstract: A single egocentric image typically captures only a small portion of the floor, yet a complete metric traversability map of the surroundings w
arXiv:2606.30336v1 Announce Type: new Abstract: We introduce FlexTab, a flexible encoder-decoder architecture for in-context learning on tabular data that pairs a single, task-agnostic encoder with a
arXiv:2606.28962v1 Announce Type: cross Abstract: Model quantization is essential for the efficient deployment of Large Language Models (LLMs), but introduces a critical vulnerability: Quantization-Co
HB 1217 prohibits local governments from pursuing net-zero emissions goals , with additional restrictions on carbon-trading programs, green procurement, and carbon-based taxes or fees . Governor Ron D
arXiv:2606.29059v1 Announce Type: cross Abstract: World modeling requires forecasting uncertain futures while preserving information useful for downstream perception. Existing visual world models ofte
arXiv:2606.29150v1 Announce Type: new Abstract: Discrete flow models have recently shown promising performance on few-step text generation; however, when naively applied to structured reasoning tasks
arXiv:2606.30376v1 Announce Type: cross Abstract: Aligning generative flow models on continuous spaces via online reinforcement learning is constrained by intractable trajectory likelihoods. Existing
arXiv:2606.29917v1 Announce Type: new Abstract: Instance-Specific Image-Goal Navigation (InstanceImageNav) requires a robot to navigate toward the exact object instance depicted in a query image. Exte
The Atlantic: FOIA docs: White House officials used auto-deleting Signal chats even after Trump advised against its use in April 2025, raising federal recordkeeping concerns — New records reveal that
A post about discovering a reset button, likely referring to a technical solution or system restart feature. Without access to the full thread content, the specific context or relevance would require
Fireworks AI announced achieving 446 tokens per second throughput speeds as measured by Artificial Analysis benchmarks, positioning this as their standard performance metric. The company offers custom
Ford CEO Jim Farley in new interview on Chinese EV competition: 'There's no real competition from Tesla, GM or Ford with what we've seen from China. They are completely dominating the EV landscape glo
arXiv:2606.30566v1 Announce Type: cross Abstract: We discover a behavioral invariant in LLM agents under persistent memory poisoning: in architectures where routing information is retrieved through ob
arXiv:2606.30196v1 Announce Type: cross Abstract: This paper offers an in-depth analysis of non-sequential multimodal sentence-level embeddings, with a particular focus on the SONAR model. We demonstr