Knowledge catalogue
arXiv:2607.03425v1 Announce Type: new Abstract: Algorithmic recourse addresses the challenge of providing tailored recommendations to users affected by unfavorable machine learning decisions, in poten
arXiv:2607.03245v1 Announce Type: new Abstract: High-throughput plant phenotyping generates valuable data that often remains trapped in unstructured text and isolated RGB images. To bridge this semant
arXiv:2607.04188v1 Announce Type: new Abstract: Intelligent industrial maintenance critically relies on reliable fault diagnosis of rotating machinery. However, it faces formidable challenges from unk
arXiv:2607.05165v1 Announce Type: new Abstract: Non-invasive brain-to-speech decoding aims to restore communication to patients suffering from neurodegenerative disease, without the risks of neurosurg
arXiv:2607.03299v1 Announce Type: cross Abstract: Real-time cardiac cine MRI enables visualization of the beating heart during free breathing, but severe undersampling and motion make reconstruction h
arXiv:2607.03068v1 Announce Type: cross Abstract: Semi-supervised semantic segmentation (SSSS) has long turned on one question, which pseudo-labels to trust, and answered it with ever more careful con
arXiv:2607.05373v1 Announce Type: new Abstract: 3D reconstruction and generation are commonly tackled by separate paradigms: pixel-based regression for reconstruction, and latent diffusion for generat
arXiv:2607.05175v1 Announce Type: new Abstract: We characterize observability in representation learning through Platonic Projection Structures (PPS), an operator-theoretic framework for analyzing rep
arXiv:2508.02932v2 Announce Type: replace Abstract: Low-Rank Adaptation (LoRA) has gained popularity as a fine-tuning approach for Large Language Models (LLMs) due to its low resource requirements and
arXiv:2607.02881v1 Announce Type: new Abstract: Long-horizon behavior prediction aims to infer a user's next action based on a lengthy historical sequence, playing a crucial role in artificial intelli
arXiv:2511.16893v3 Announce Type: replace Abstract: Specialized attention heads dubbed induction heads (IHs) have been argued to underlie the remarkable in-context learning capabilities of modern lang
arXiv:2607.04557v1 Announce Type: cross Abstract: Accurate prediction of patient-specific therapeutic response from pre-treatment transcriptomes is hindered by the scarcity of matched clinical respons
arXiv:2607.04057v1 Announce Type: new Abstract: Robots deployed over long periods must reason about environments that change over time. Existing long-term perception systems often address object chang
arXiv:2607.02768v1 Announce Type: cross Abstract: Pathologic complete response and tumor shrinkage measure whether breast cancer responds to neoadjuvant therapy, but not whether that response was stru
arXiv:2601.02022v2 Announce Type: replace Abstract: We prove that Thompson sampling exhibits ilde{O}(sigma d sqrt{T} + d r sqrt{Tr(Sigma_0)}) Bayesian regret in the linear-Gaussian bandit with a N(mu_
arXiv:2607.02611v1 Announce Type: new Abstract: Work-related Musculoskeletal Disorders (WMSDs) require continuous ergonomic assessments. While Rapid Entire Body Assessment (REBA) is a gold-standard ob
arXiv:2607.05251v1 Announce Type: cross Abstract: Neural network verification and data privacy are inherently in tension: verification demands full access to model parameters and input data, yet both
arXiv:2607.04824v1 Announce Type: new Abstract: We study a sequential learning problem for stable matchings in two-sided markets where preferences on both sides are initially unknown. We focus on a ce
arXiv:2607.04696v1 Announce Type: new Abstract: Establishing large-scale, high-resolution neural connectivity maps is fundamental to elucidating the structural basis of brain function. However, when p
arXiv:2607.03806v1 Announce Type: cross Abstract: Audio foundation models are widely adopted as general-purpose feature extractors, yet the internal structure of their learned representations remains
arXiv:2607.04894v1 Announce Type: new Abstract: Memory-based anomaly detection is attractive because it localizes defects from normal images without training a decoder or synthesizing pseudo anomalies
arXiv:2601.09097v3 Announce Type: replace Abstract: Multi-constraint planning involves identifying, evaluating, and refining candidate plans while satisfying multiple, potentially conflicting constrai
arXiv:2607.05224v1 Announce Type: new Abstract: Code-switching (CS), alternating languages within the same utterance, poses significant challenges for automatic speech recognition (ASR) due to limited
arXiv:2607.02907v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) have achieved remarkable progress but still struggle with complex visual reasoning tasks requiring multi-step
arXiv:2607.02693v1 Announce Type: new Abstract: This study presents a conditional Generative Adversarial Network (cGAN) framework for generating 3D porous media volumes with controlled porosity, train
arXiv:2607.05276v1 Announce Type: cross Abstract: Speaker embeddings, or x-vectors, are widely used to represent speaker identity and speaker-related attributes, but existing embedding extractors are
arXiv:2607.02721v1 Announce Type: new Abstract: 3D Gaussian Splatting (3DGS) enables high-quality real-time novel-view synthesis, but practical scenes often contain millions of Gaussians, making compr
arXiv:2607.03003v1 Announce Type: new Abstract: Social media posts are a rich and valuable source of data for analyzing mental health states and users' well-being using automated analysis tools. In th
arXiv:2511.04805v2 Announce Type: replace-cross Abstract: Mixture-of-Experts (MoE) models have shown strong potential in scaling language models efficiently by activating only a small subset of expert
arXiv:2607.03825v1 Announce Type: cross Abstract: Audio-Visual Question Answering (AVQA) extends classical VQA by requiring joint reasoning over video and synchronized audio. However, many AVQA system
arXiv:2607.04978v1 Announce Type: cross Abstract: Joint-Embedding Predictive Architectures (JEPAs) underpin a growing family of latent world models for control from raw pixels, but every existing JEPA
arXiv:2602.20629v3 Announce Type: replace Abstract: As Large Language Models (LLMs) saturate elementary benchmarks, the research frontier has shifted from generation to the reliability of automated ev
arXiv:2607.04407v1 Announce Type: cross Abstract: Repeated prediction of acoustic fields from spatially distributed boundary excitation is computationally expensive when each source realization requir
arXiv:2607.03187v1 Announce Type: cross Abstract: The classical Kolmogorov--Arnold representation theorem states that any continuous multivariate function can be exactly decomposed into a finite compo
arXiv:2607.05307v1 Announce Type: cross Abstract: A core task in quantum anomaly detection is to compute an anomaly score that quantifies how strongly a test quantum state deviates from a given quantu
arXiv:2505.15441v5 Announce Type: replace-cross Abstract: Natural images exhibit strong geometric regularities: local structures, such as edges, corners, and textures, appear in many orientations and
arXiv:2606.12476v3 Announce Type: replace-cross Abstract: Token-level hallucination detectors are evaluated as classifiers, by AUC over all tokens, yet a streaming monitor is judged by its reaction ti
arXiv:2607.02967v1 Announce Type: new Abstract: Large language models remain limited as continual learning systems, motivating renewed interest in Sparse Distributed Memory (SDM) as an explicit online
arXiv:2607.05113v1 Announce Type: new Abstract: Imagine two users interact with the same LLM. One has been told it is the cutting-edge flagship model; the other, an older, weaker model. They walk away
arXiv:2607.04127v1 Announce Type: new Abstract: We present a real-time LiDAR-based framework for Gaussian Splatting SLAM that tightly couples fast G-ICP registration with spherical rasterization-based
arXiv:2503.21022v2 Announce Type: replace Abstract: The higher-order autocorrelations of integer-valued or rational-valued functions on finite Abelian groups appear naturally in X-ray crystallography,
arXiv:2511.15409v2 Announce Type: replace Abstract: We present a class of algorithms for state estimation in nonlinear, non-Gaussian state-space models. Our approach is based on a variational Lagrangi
arXiv:2607.03626v1 Announce Type: new Abstract: Recent advances in generative modeling have enabled the efficient computation of Schrodinger bridges (SB) in high-dimensional settings by leveraging par
arXiv:2607.02554v1 Announce Type: new Abstract: Sparse-view neural reconstruction is challenging in outdoor driving scenes, where cameras usually move along a narrow forward-facing trajectory and prov
arXiv:2204.13492v5 Announce Type: replace Abstract: We present StreamDEQ, a method that aims to infer frame-wise representations on videos with minimal per-frame computation. Conventional deep network
arXiv:2607.04842v1 Announce Type: new Abstract: Home-based physiotherapy is performed without supervision, which leads to incorrect execution and motivates systems that assess movement automatically f
arXiv:2607.05253v1 Announce Type: new Abstract: Large-scale Vision-Language Models like CLIP have demonstrated impressive open-set localization capabilities at the image level. However, adapting this
arXiv:2210.10619v3 Announce Type: replace-cross Abstract: Reliability measures associated with the prediction of the machine learning models are critical to strengthening user confidence in artificial
arXiv:2607.03784v1 Announce Type: cross Abstract: While prior studies have successfully compressed vision Transformers (ViTs) through various pruning techniques, most have concentrated on width prunin
arXiv:2607.03148v1 Announce Type: cross Abstract: Activation functions are considered an essential primitive for neural nonlinearity, i.e., they enable neural networks to serve as universal approximat
arXiv:2607.04236v1 Announce Type: cross Abstract: Bayes-assisted conformal prediction combines the strengths of Bayesian modelling with exact, distribution-free frequentist coverage guarantees. Althou
arXiv:2607.02584v1 Announce Type: new Abstract: In extbf{DiT-based video generation models equipped with 3D Rotary Position Embeddings (3D RoPE)}, the attention mechanism remains a primary computation
arXiv:2607.05281v1 Announce Type: cross Abstract: Present-day quantum computing is cloud-based, where a user submits a circuit to a service provider's proprietary backend hardware. While providers may
arXiv:2602.23820v2 Announce Type: replace Abstract: SAR supports all-day-and-night oceanic observation, yet vessel identification from SAR images is hampered by speckle noise, intricate land-sea backg
arXiv:2607.05035v1 Announce Type: cross Abstract: Few-shot brain tumor segmentation remains challenging due to noisy support masks, inter-patient variations between support and query images, and the l
arXiv:2607.03207v1 Announce Type: new Abstract: Automatic speech recognition (ASR) has advanced remarkably for standard speech, yet speech affected by neurological conditions remains a challenge. We p
arXiv:2601.03024v3 Announce Type: replace Abstract: We propose Self-Augmented Residual 3D Gaussian Splatting (SA-ResGS), a novel framework to stabilize uncertainty quantification and enhancing uncerta
arXiv:2607.03612v1 Announce Type: new Abstract: Feed-forward 3D reconstruction (F3R) transformers have recently achieved remarkable success. However, scaling them to long image sequences remains chall
Sakana AI has launched a translation service that captures the nuanced 'tone and temperature' of Japanese expressions like '恐縮ですが' (apologetic) and 'それな' (casual agreement), going beyond literal trans
Sakana AIが翻訳機能「Sakana Translate」をリリース、日英中の双方向翻訳に対応＆カジュアル・簡潔・大阪弁などに変換可能 https://gigazine.net/news/20260707-sakana-translate/?utm_source=x&utm_medium=sns&utm_campaign=x_post&utm_content=20260707-sakana-