「embedding」の検索結果
60 件AI-generated images have reached unprecedented realism, while instruction-driven AI editors re-synthesize imag
Capturing an image in a low-light environment produces a weak, noisy sensor signal. Low-light image enhancemen
このリポジトリでは、データとAIアルゴリズムを製品化するためのプラットフォームであるTaipyを提供しています。
ドキュメントを構造化するために使えるオープンソースのETLソリューション。
Knowledge distillation is a technique used to transfer the expertise of large teacher models into small, effic
Evolution equations with time-dependent coefficients force a neural solver to spend capacity on a scale that t
Many causal inference problems require sampling from interventional outcome distributions rather than estimati
A substantial line of work builds rotation invariance directly into point cloud architectures. We benchmark 10
Large Language Models (LLMs) often generate fluent yet incorrect responses, making un- certainty estimation im
Deep learning models for electronic health records achieve strong predictive performance but provide little in
A model pretrained to predict a customer's next event can estimate the probability of future events without tr
Generative recommendation has been widely adopted in large-scale industrial recommendation systems, where mult
Given a relational database (RDB) storing heterogeneous tabular information, how can we predict missing (or fu
Spatial multi-omics technologies jointly profile diverse molecular modalities with spatial context, providing
Large language model (LLM) embeddings have been increasingly adopted in sequential recommendation to encode st
Reverse engineering obfuscated binaries is a core task in malware analysis and software security auditing. Whi
We present ICJ-Graph, a machine-readable resource connecting documents of the International Court of Justice (
Open-set recognition (OSR) requires neural networks to classify known classes while rejecting unknown samples,
Multimodal retrieval must integrate complementary evidence from views that may be missing, noisy, or unequally
Semantic change detection in remote sensing aims to identify and characterize land-cover transitions observed
Existing RAW-based detectors typically introduce learnable ISP modules to convert RAW inputs into sRGB-like fo
Face anti-spoofing (FAS) models often learn attack- and dataset-specific cues that degrade performance under u
Clinical evidence is inherently relational: medical images are interpreted with prior studies, cross-modal exa
Quantum-inspired computer vision often reshapes high-dimensional features into tensors and applies tensor-netw
Hyperspectral image (HSI) classification requires the representation of contiguous spectral signatures and spa
In virtual environments, choosing appropriate outfits requires contextual harmony with surrounding scenes, yet
Computed tomography (CT) provides diverse patient information and various clinical tasks, for which numerous d
Robust multimodal perception for soft robotic grasping is challenged by modality-dependent noise, semantic amb
The spring-loaded inverted pendulum (SLIP) describes the center-of-mass (COM) motion of runners but has no tru
Conformal prediction (CP) provides a distribution-free framework for uncertainty quantification with finite-sa
We present HyVox, a hybrid neuromorphic--von Neumann system for low-energy open-set speaker identification und
Accurate house price prediction is essential for real estate buyers, sellers, agents, and financial institutio
Concerns about LLM homogeneity are typically quantified with embedding similarity, which reduces two texts to
Humans continuously acquire knowledge from carefully crafted materials such as books and tutorial videos, carr
Repeated satellite imagery permits understanding of how our planet changes over time, letting us predict its f
Visualizing and interpreting the dependence structure in relational data often benefits from geometric represe
This letter introduces HALO-LP, a novel homogeneous all-shifted non-interior-point method for linear programmi
This work addresses the challenge of controlling multi-quadrotor systems subject to tight uncertain couplings.
Concept erasure is a prominent approach to achieving fairness in machine learning through the removal of sensi
Embedding models underpin virtually every modern AI system, from search engines to recommendation systems. Des
Self-supervised contrastive learning (SSCL) learns representations by contrasting augmented views of the same
We show that the transformer architecture emerges as the score of last-token laws in a natural statistical mod
Whether models trained on different modalities converge to the same representation of the world is central to
Rotary Position Embedding (RoPE) typically uses uniform coverage across Transformer layers despite heterogeneo
Formula-Driven Supervised Learning (FDSL) enables vision pre-training without real images, but existing method
Autoregressive (AR) image generators are powerful in principle but inflexible in practice: training with a fix
Feature extraction from unstructured data typically relies on opaque embeddings unsuited to interpretable deci
Graph neural networks (GNNs) rely critically on neighborhood aggregation, yet conventional mean aggregation ma
We propose a test of whether two interventions induce the same outcome distribution when outcomes are structur
Fine-tuning Large Language Models (LLMs) on instruction data can quietly erode their safety, even when no trai
Hyperdimensional computing (HDC) offers efficient learning and inference for resource-constrained systems, yet
Standard sequence models encode position by token rank rather than physical coordinate, typically forcing prac
Generative AI assistants are becoming an advertising medium, embedding sponsored content in their answers. Thi
EEG foundation models are advancing at an unprecedented pace, yet their evaluation is driven by downstream acc
A very simple framework for state-of-the-art Natural Language Processing (NLP)
Architecture attribute predictors reduce the cost of neural architecture search by estimating accuracy or late
Efficient neural architecture search (NAS) requires accurate performance estimation under limited evaluation b
Intrinsic explainability remains a challenging problem, particularly in contexts where multilayer perceptrons
Clinical scalp electroencephalography (EEG) offers a noninvasive window into neural dynamics of neuropsychiatr
Sensor networks and radars can deliver signals as organized sets, e.g. ordered signals, signals describing ran