Knowledge catalogue
arXiv:2610.17699v1 Announce Type: cross Abstract: Single-cell RNA sequencing (scRNA-seq) enables cell-level analysis of biological heterogeneity, but learning effective representations from high-dimen
arXiv:2610.17019v1 Announce Type: cross Abstract: Whisper-style ASR models provide strong zero-shot transcription, but their residual errors are not evenly distributed across datasets, recording condi
arXiv:2507.14777v2 Announce Type: replace Abstract: We investigate foundational questions about the interplay between memorization and learning in large language models (LLMs). When an LLM generates a
arXiv:2610.15564v1 Announce Type: new Abstract: We introduce UNISON, a unified framework that bridges multiple instance learning and recommendation systems by treating both as instances of learning fr
arXiv:2105.10759v4 Announce Type: replace-cross Abstract: We show how a pair of points can uniquely represent a left-infinite sequence obtained from observations of an underlying dynamical system thro
arXiv:2601.17274v2 Announce Type: replace Abstract: We develop unrolled neural networks to solve constrained optimization problems, offering accelerated, learnable counterparts to dual ascent (DA) alg
arXiv:2511.13714v2 Announce Type: replace Abstract: The Segment Anything Model (SAM) family has become a widely adopted vision foundation model, but its ability to control segmentation granularity rem
arXiv:2610.16826v1 Announce Type: cross Abstract: Deepfake speech detectors are typically evaluated as binary classifiers on fixed benchmarks, but deployed systems face evolving attacks, codecs, speak
arXiv:2610.16578v1 Announce Type: cross Abstract: Modern deep classifiers trained by gradient descent need many passes over the data to become accurate, and even then are not always well calibrated; p
arXiv:2610.16204v1 Announce Type: new Abstract: Autoencoders are the core component of latent video generative models, compressing high-dimensional videos into compact latent spaces for downstream gen
arXiv:2610.18615v1 Announce Type: new Abstract: Visual agents must turn video observations into timely decisions to track progress and choose what to do next. Building on the emerging System One parad
arXiv:2610.15728v1 Announce Type: new Abstract: Visible watermarks convey document ownership, confidentiality, and status, but can also carry adversarial signals. We introduce Auto-Regional Adversaria
arXiv:2610.16567v1 Announce Type: new Abstract: Vision-Language-Action models (VLAs) have achieved high success rates on familiar manipulation tasks, yet it remains unclear whether such scores reflect
arXiv:2610.15092v1 Announce Type: new Abstract: Quantization plays a crucial role in efficiently deploying deep learning models on resource-constrained devices. Post-training quantization does not req
arXiv:2610.18147v1 Announce Type: new Abstract: We present a real-time framework for expressive legged locomotion that enables independent control over velocity and style. Unlike existing data-driven
arXiv:2603.27158v3 Announce Type: replace Abstract: While highly accelerated non-Cartesian acquisition protocols significantly reduce scan time, they often entail long reconstruction delays. Deep lear
arXiv:2610.15850v1 Announce Type: new Abstract: Sparse autoencoder (SAE) features are typically discovered from how their activations co-vary with behaviour, yet they are often used through interventi
arXiv:2610.16641v1 Announce Type: new Abstract: Cross-task generalization remains difficult for world action models (WAMs), even when their predicted futures depict plausible manipulation. This gap ca
arXiv:2610.17844v1 Announce Type: new Abstract: Spatial understanding is a necessary step in moving multimodal large language models (MLLMs) toward reasoning about the 3D physical world. Their visual
arXiv:2610.15035v1 Announce Type: new Abstract: Contrastive learning has become the standard paradigm for multimodal representation learning, aligning semantically matched pairs while separating misma
arXiv:2610.18155v1 Announce Type: new Abstract: Standard identifiability theory assumes one-dimensional latent factors, but learned representations can encode latent information across multidimensiona
arXiv:2610.16020v1 Announce Type: new Abstract: Layer-wise post-training quantization methods let each layer compensate for the quantization error inherited from earlier layers. We ask under what assu
arXiv:2605.06204v2 Announce Type: replace-cross Abstract: Trimming suspicious calibration points is a common response to contamination in conformal prediction. Its effect on clean-target coverage, how
arXiv:2610.15139v1 Announce Type: new Abstract: Linear-attention and state-space LLMs carry long context in a fixed-size recurrent state, and quantizing that state is the natural way to cut its cost d
arXiv:2610.16220v1 Announce Type: new Abstract: Participant-disjoint cross-validation prevents direct participant overlap, but it may still produce optimistic estimates when acquisition site and diagn
arXiv:2610.14090v1 Announce Type: new Abstract: Exterior Gaussian-splat twins for real-estate listings are captured to a fixed recipe: a consumer drone orbits a single property in several overlapping
arXiv:2610.16801v1 Announce Type: cross Abstract: Tabular foundation models (TFMs) achieve state-of-the-art performance on IID benchmarks, but how do they respond to repeated measurements at the same
arXiv:2610.17285v1 Announce Type: new Abstract: Modern neural-network training uses mixed recipes, assigning AdamW-like or sign-based updates to embedding and output layers and spectral updates to hid
arXiv:2610.15299v1 Announce Type: new Abstract: Misclassification is a critical problem in many machine learning applications. Since even the classifier models with high accuracy (e.g., > 95%) still i
arXiv:2610.16337v1 Announce Type: new Abstract: Rotary Positional Embedding (RoPE) is widely used to encode relative positions in Transformers. In multi-view settings, however, patch coordinates are d
arXiv:2610.13476v1 Announce Type: new Abstract: Representational similarity measures (RSA, CKA, Procrustes) are chosen for the transformations they treat as irrelevant, and all of them take the set of
arXiv:2610.13653v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) has substantially improved LLM reasoning, yet growing evidence suggests it mainly performs distr
arXiv:2610.12718v1 Announce Type: cross Abstract: Large language models are stochastic by nature. Repeated executions under the same experimental conditions may produce different responses. We propose
arXiv:2602.01149v2 Announce Type: replace-cross Abstract: Machine learning (ML) has emerged as a powerful approach for accelerating the discovery of thermoelectric (TE) materials, yet many models suff
arXiv:2610.13603v1 Announce Type: new Abstract: Psychology has proposed many principles of cognition. Each, however, was developed in isolation, leaving their combined explanatory power unknown. We as
arXiv:2610.13407v1 Announce Type: new Abstract: Exhaustive optimistic scoring can make action selection costly in linear bandits with a large fixed set of K actions, requiring O(K) score evaluations p
arXiv:2610.12701v1 Announce Type: new Abstract: Perspectivist NLP has established that annotator positionality shapes labels for subjective tasks, but the field still lacks methods for operationalisin
arXiv:2610.13799v1 Announce Type: cross Abstract: Variance reduction improves the convergence rate of stochastic non-convex optimization under the average smoothness condition. Existing adaptive varia
arXiv:2610.12972v1 Announce Type: new Abstract: Humanoid robots performing lifting and material-handling tasks must maintain balance despite unknown and varying payloads. However, payload uncertainty
arXiv:2610.12917v1 Announce Type: new Abstract: AI shopping assistants increasingly influence which products consumers consider. We study retail assortment planning under AI mediation, where a retaile
arXiv:2610.13928v1 Announce Type: new Abstract: Capability evaluations aim to surface dangerous model behaviors before deployment, but their reliability depends on the assumption that hidden capabilit
arXiv:2610.12679v1 Announce Type: new Abstract: Efficient sampling from Bayesian posterior distributions in high dimensions remains a hard problem, despite advances in theory and algorithms. Multiple
arXiv:2510.00734v2 Announce Type: replace-cross Abstract: Bayesian optimal experimental design provides a principled framework for selecting experimental configurations that maximize obtained informat
arXiv:2610.13518v1 Announce Type: cross Abstract: In machine learning, a self-attention dynamics describes the attention mechanism of transformers, and is obtained treating the layer index of a transf
arXiv:2610.12545v1 Announce Type: cross Abstract: Autoregressive decoding is less suited to non-causally ordered data, e.g., cross-references or future dependencies, since their outputs cannot be revi
arXiv:2501.18560v2 Announce Type: replace Abstract: We consider bandits with anytime knapsacks (BwAK), a novel version of the BwK problem where there is an extit{anytime} cost constraint instead of a
arXiv:2610.12997v1 Announce Type: new Abstract: Prior-art retrieval systems are structurally organized around classification codes, making cross-code discovery difficult. This paper asks if an unsuper
arXiv:2610.13121v1 Announce Type: new Abstract: Cyberbullying is a growing concern in online communities, yet existing Bengali corpora remain limited in contextual information, annotation transparency
arXiv:2602.01667v2 Announce Type: replace Abstract: Prediction-set size offers a coarse summary of conformal uncertainty. We develop a richer perspective through the geometry of credal sets: families
arXiv:2610.12919v1 Announce Type: cross Abstract: We consider online binary prediction with paid stochastic features, where the learner can spend resources to obtain better observations of the underly
arXiv:2610.12977v1 Announce Type: new Abstract: Generative recommendation (GR) of social media platforms commonly retrieves items by autoregressively decoding discrete Semantic IDs (SIDs) with finite-
arXiv:2505.22184v3 Announce Type: replace-cross Abstract: Social media platforms have experienced a significant rise in toxic content, including abusive language and discriminatory remarks, presenting
arXiv:2603.00530v2 Announce Type: replace Abstract: Sampling from unnormalized densities using diffusion models has emerged as a powerful paradigm. However, while recent approaches that use least-squa
arXiv:2610.12530v1 Announce Type: new Abstract: Training-free feature reuse accelerates diffusion transformer inference by replacing full denoising computation at selected steps with cached approximat
arXiv:2610.13693v1 Announce Type: cross Abstract: Neural networks (NNs), while highly flexible and versatile, are often poorly calibrated. Post-hoc calibration methods adjust the outputs of an already
arXiv:2610.12644v1 Announce Type: cross Abstract: Backdoor Attacks on Large Language Models (LLMs) typically follow a trigger-response pattern, where satisfying a trigger condition activates a single
arXiv:2610.13520v1 Announce Type: new Abstract: Identifying causal effects from observational data typically requires a fully specified causal graph, an assumption that is often unrealistic in complex
arXiv:2610.12520v1 Announce Type: cross Abstract: The goal of circuit discovery is to decompose a monolithic neural network into tiny modular subnetworks which are interpretable in the sense that each
arXiv:2610.12580v1 Announce Type: new Abstract: Event ontology is the foundational framework for defining events in a dataset. Event type induction generally involves domain experts who define guideli
arXiv:2610.13433v1 Announce Type: cross Abstract: This paper addresses the grid quantization error of deep-learning (DL)-based direction-of-arrival (DOA) estimation. Classification- based DOA methods