Knowledge catalogue
As the title suggests I'm currently trying to make a modal to identify which app is being used. The thing is I don't have labelled data and generating it is out of question since that is a lot of work
arXiv:2610.08126v1 Announce Type: cross Abstract: Product Identification has sprung up to become one of the most challenging problems in the automation of the retail industry. With the new industry 5.
arXiv:2610.07472v1 Announce Type: new Abstract: Remotely operated vehicles (ROVs) are widely used to explore and inspect underwater environments such as caves, shipwrecks, and submerged infrastructure
arXiv:2610.08420v1 Announce Type: new Abstract: We establish a polynomial sample complexity separation between symmetry-aware and symmetry-agnostic feature learning. We study growing-rank multi-index
arXiv:2610.06855v1 Announce Type: cross Abstract: Scalable driver identification requires embedding models that maintain discriminative performance as fleet size grows, yet existing triplet-loss formu
arXiv:2610.08314v1 Announce Type: new Abstract: Linear regression and kernel smoothing offer tractable explanations of in-context learning: in both, the features determine the weight assigned to each
arXiv:2610.07224v1 Announce Type: cross Abstract: Clinical notes capture most of what is documented about a patient's care, but they cannot be used for research until protected health information (PHI
arXiv:2610.07778v1 Announce Type: cross Abstract: Attributed graph clustering aims to discover node groups by jointly exploiting node attributes and graph topology, yet its unsupervised nature makes m
arXiv:2610.06858v1 Announce Type: cross Abstract: This demo paper presents the conceptual foundations and the first steps towards implementation of a novel no-code solution for movement data analytics
arXiv:2610.07859v1 Announce Type: new Abstract: Conventional decentralized federated learning (DFL) often focuses on clients, with each client maintaining a model copy, performing updates individually
arXiv:2610.06902v1 Announce Type: new Abstract: Retrieval-augmented generation grounds language models in external context, but for long documents flat top-k retrieval can cluster on a single region a
arXiv:2610.07755v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used as judges for automated AI evaluation. A common practice is to randomize prompt sequences and avera
arXiv:2610.07823v1 Announce Type: new Abstract: The AI CUP 2025 Precise Analysis of Table Tennis Smart Racket Data Competition introduced smart table tennis rackets that collect extensive player swing
arXiv:2610.07070v1 Announce Type: new Abstract: Small language models are inexpensive to serve and can run on private infrastructure, but base models are often not good enough at multi-turn tool calli
arXiv:2610.07503v1 Announce Type: cross Abstract: Two populations of graphs often have to be compared without any correspondence between their vertices, for instance when networks come from different
arXiv:2610.08277v1 Announce Type: cross Abstract: Deciding whether two samples come from the same distribution is a classical problem in statistics, and generative transport offers a new way to approa
arXiv:2610.07572v1 Announce Type: new Abstract: In-context learning (ICL) adapts frozen large multimodal models (LMMs) to new tasks from a few demonstrations (demos), but re-encodes them at every quer
arXiv:2610.07941v1 Announce Type: cross Abstract: Physically-based differentiable rendering enables gradient-based optimization of scene parameters by matching rendered images to measurements, but has
arXiv:2607.09985v2 Announce Type: replace Abstract: Object pose estimation is a fundamental problem in 3D vision. Although recent state-of-the-art approaches achieve strong performance, generalization
arXiv:2610.06938v1 Announce Type: new Abstract: Medical image segmentation remains fragmented along two axes: segmentation paradigms and data dimensionality. Existing methods are typically developed s
arXiv:2610.08463v1 Announce Type: new Abstract: Long-context inference and Retrieval-Augmented Generation (RAG) handle evidence selection at vastly different scales, from a single long prompt to an en
Dataset: https://huggingface.co/datasets/datasocial/tiktok-5.6B-videos If you want to explore the data without downloading billions of rows, you can query my ClickHouse database directly. It includes:
arXiv:2610.08564v1 Announce Type: new Abstract: Tabular foundation models (TFMs) can classify the nodes of a graph without training on it, by reading node and neighborhood features as table rows next
arXiv:2610.07606v1 Announce Type: new Abstract: This paper establishes a theoretical framework for vertical adaptive layer skipping, proving three foundational results: (i) an Expected FLOPs formula (
arXiv:2512.13708v2 Announce Type: replace Abstract: Inferring interaction structure from steady-state observations is a central inverse problem when transient trajectories are unavailable. Here we for
arXiv:2509.26189v2 Announce Type: replace Abstract: The rapid proliferation of Large Language Models has intensified the challenge of distinguishing LLM-generated text from human writing in non-Englis
arXiv:2610.08179v1 Announce Type: new Abstract: Text-driven 3D Gaussian editing commonly does not distinguish the editing reliability of rendered views, although different viewpoints provide supervisi
arXiv:2610.07080v1 Announce Type: new Abstract: Attention maps are often interpreted as evidence of what a forecasting model uses when making predictions. In our load-forecasting model, a CLS represen
arXiv:2610.06877v1 Announce Type: cross Abstract: Accurate prediction does not establish that a world model has recovered a physical law: distinct dynamics can generate identical records under the sam
arXiv:2610.08718v1 Announce Type: new Abstract: Knowledge that a language model appears to forget during finetuning often remains stored and can be recovered, a phenomenon called spurious forgetting.
arXiv:2610.07715v1 Announce Type: cross Abstract: A memory system can retire an obsolete value and later restore it merely because the same old statement appears again. A re-read of an old source and
arXiv:2610.07018v1 Announce Type: new Abstract: Vision-language models (VLMs) have achieved strong performance in multimodal reasoning, yet they remain prone to generating plausible but incorrect answ
arXiv:2609.33155v2 Announce Type: replace Abstract: Test-time scaling and post-training have improved LLM performance in coding and mathematical reasoning, but their effectiveness for individual stanc
arXiv:2610.08165v1 Announce Type: new Abstract: The mechanical properties of a metallic alloy are set by its microstructure and texture: the size and shape of its grains and the orientation of their c
arXiv:2610.07553v1 Announce Type: cross Abstract: LoRA fine-tuning adapts small language models (SLMs) to heterogeneous instruction data within a low-rank update subspace, making it vulnerable to thre
arXiv:2610.08502v1 Announce Type: cross Abstract: Proactive power management systems reduce processor dynamic power through runtime power prediction and power-aware scheduling. Accurate, stable and lo
arXiv:2601.12966v2 Announce Type: replace-cross Abstract: The Lombard effect plays a key role in natural communication, particularly in noisy environments or when addressing hearing-impaired listeners
arXiv:2610.04022v1 Announce Type: new Abstract: Powered neck exoskeletons are developed to restore head-neck motions for those with neck muscle weakness. Current methods using hand-held input devices
arXiv:2610.05130v1 Announce Type: cross Abstract: An electromagnetic inversion scheme that integrates stochastic optimization (STO) and plug-and-play (PNP) regularization into contrast-source inversio
arXiv:2610.06508v1 Announce Type: new Abstract: Illuminant estimation is a fundamental problem in computational photography, as it enables the correction of color shifts induced by varying lighting co
arXiv:2610.04939v1 Announce Type: new Abstract: We ask whether physically meaningful correspondences discovered inside a trained scientific model remain meaningful outside the conditions under which t
arXiv:2610.04256v1 Announce Type: new Abstract: Exactly computing the maximum function is a standard test case for studying depth in ReLU networks. Two hidden layers are known to suffice for up to twe
arXiv:2610.04368v1 Announce Type: new Abstract: Extreme winter weather has repeatedly disrupted gas-fired power generation in the United States, yet the plant-level data needed to systematically quant
arXiv:2507.18104v3 Announce Type: replace Abstract: Brain encoding models predict time-resolved neural activity from computational representations of ongoing experience, providing a principled framewo
arXiv:2610.06464v1 Announce Type: new Abstract: As technology scales to smaller nodes, increasing current densities make electromigration (EM) one of the dominant reliability challenges in on-chip int
arXiv:2610.06798v1 Announce Type: cross Abstract: Machine-learning emulators of chaotic and stochastic systems are usually validated on forecast skill and long-run statistics. Neither certifies that a
arXiv:2610.06701v1 Announce Type: cross Abstract: A recurring design principle in modern optimizers is to decouple update magnitude from the raw gradient norm, yet its consequences for learning-curve
arXiv:2610.05413v1 Announce Type: new Abstract: Evaluating vision encoders requires metrics that reliably predict their downstream performance in multimodal large language models (MLLMs). Although rec
arXiv:2610.05794v1 Announce Type: cross Abstract: We develop and test a theory of language model representations in which there exist atomic features. Our main theoretical insight is that in such a mo
arXiv:2610.05758v1 Announce Type: new Abstract: Holes in LiDAR scans of environments with glass windows remain an unresolved problem in three-dimensional reconstruction. This study presents a pipeline
arXiv:2006.08426v5 Announce Type: replace-cross Abstract: Descent directions are fundamental to first-order methods for constrained convex optimization. In this work, we provide a unified perspective
arXiv:2610.03896v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly used for automated algorithm design. However the computational cost of evaluating the generated algorithms
arXiv:2508.06084v3 Announce Type: replace Abstract: Vision-language models (VLMs) have achieved impressive performance on multimodal inference tasks, but the cost remains a significant challenge due t
arXiv:2610.04391v1 Announce Type: new Abstract: Vision-language-action policies may predict a transferable manipulation strategy yet fail to realize it reliably on the encountered object: objects comp
arXiv:2603.13790v2 Announce Type: replace Abstract: Data selection for language models is often framed as balancing example quality and diversity. We argue that both are consequences of a more fundame
arXiv:2610.05558v1 Announce Type: new Abstract: The Dobrushin coefficient of each attention-rollout factor satisfies kappa(frac12(I+A))lefrac12(1+kappa(A)), and multiplying these inequalities over lay
arXiv:2610.06094v1 Announce Type: new Abstract: Cone-beam computed tomography (CBCT) is widely used in image-guided radiotherapy, but scatter, beam hardening, noise, truncation, and other artifacts li
arXiv:2610.06135v1 Announce Type: new Abstract: Engineering design often starts from a 2D sketch that fixes style and proportions, yet the subsequent 3D shape optimization relies on learned generative
arXiv:2610.06180v1 Announce Type: new Abstract: Whether a time-series pattern is anomalous often depends on the operating regime of the monitored process. A missing event can signal a fault in one reg
arXiv:2610.04346v1 Announce Type: new Abstract: This paper proposes any-scale object detection using arbitrary-scale super-resolution for continuously rescaling object images, while general multi-scal