id stringlengths 9 16 | title stringlengths 4 278 | categories stringlengths 5 104 | abstract stringlengths 6 4.09k |
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2502.06243 | Multi-Scale Transformer Architecture for Accurate Medical Image
Classification | cs.CV cs.LG | This study introduces an AI-driven skin lesion classification algorithm built
on an enhanced Transformer architecture, addressing the challenges of accuracy
and robustness in medical image analysis. By integrating a multi-scale feature
fusion mechanism and refining the self-attention process, the model effectively
ex... |
2502.06244 | PiKE: Adaptive Data Mixing for Multi-Task Learning Under Low Gradient
Conflicts | cs.LG | Modern machine learning models are trained on diverse datasets and tasks to
improve generalization. A key challenge in multitask learning is determining
the optimal data mixing and sampling strategy across different data sources.
Prior research in this multi-task learning setting has primarily focused on
mitigating g... |
2502.06247 | Advance sharing for stabilizer-based quantum secret sharing schemes | quant-ph cs.CR cs.IT math.IT | In stabilizer-based quantum secret sharing schemes, it is known that some
shares can be distributed to participants before a secret is given to the
dealer. This distribution is known as advance sharing. It is already known that
a set of shares is advance shareable only if it is a forbidden set. However, it
was not kn... |
2502.06249 | Conditioning through indifference in quantum mechanics | quant-ph cs.AI math.PR | We can learn (more) about the state a quantum system is in through
measurements. We look at how to describe the uncertainty about a quantum
system's state conditional on executing such measurements. We show that by
exploiting the interplay between desirability, coherence and indifference, a
general rule for condition... |
2502.06250 | DGNO: A Novel Physics-aware Neural Operator for Solving Forward and
Inverse PDE Problems based on Deep, Generative Probabilistic Modeling | cs.LG math-ph math.MP | Solving parametric partial differential equations (PDEs) and associated
PDE-based, inverse problems is a central task in engineering and physics, yet
existing neural operator methods struggle with high-dimensional, discontinuous
inputs and require large amounts of {\em labeled} training data. We propose the
Deep Gene... |
2502.06252 | Evaluating Entity Retrieval in Electronic Health Records: a Semantic Gap
Perspective | cs.IR cs.CL | Entity retrieval plays a crucial role in the utilization of Electronic Health
Records (EHRs) and is applied across a wide range of clinical practices.
However, a comprehensive evaluation of this task is lacking due to the absence
of a public benchmark. In this paper, we propose the development and release of
a novel ... |
2502.06255 | Towards Efficient and Intelligent Laser Weeding: Method and Dataset for
Weed Stem Detection | cs.CV cs.AI | Weed control is a critical challenge in modern agriculture, as weeds compete
with crops for essential nutrient resources, significantly reducing crop yield
and quality. Traditional weed control methods, including chemical and
mechanical approaches, have real-life limitations such as associated
environmental impact an... |
2502.06257 | K-ON: Stacking Knowledge On the Head Layer of Large Language Model | cs.CL cs.AI | Recent advancements in large language models (LLMs) have significantly
improved various natural language processing (NLP) tasks. Typically, LLMs are
trained to predict the next token, aligning well with many NLP tasks. However,
in knowledge graph (KG) scenarios, entities are the fundamental units and
identifying an e... |
2502.06258 | Emergent Response Planning in LLM | cs.CL cs.LG | In this work, we argue that large language models (LLMs), though trained to
predict only the next token, exhibit emergent planning behaviors:
$\textbf{their hidden representations encode future outputs beyond the next
token}$. Through simple probing, we demonstrate that LLM prompt representations
encode global attrib... |
2502.06261 | Reducing Variance Caused by Communication in Decentralized Multi-agent
Deep Reinforcement Learning | cs.LG | In decentralized multi-agent deep reinforcement learning (MADRL),
communication can help agents to gain a better understanding of the environment
to better coordinate their behaviors. Nevertheless, communication may involve
uncertainty, which potentially introduces variance to the learning of
decentralized agents. In... |
2502.06268 | Spectral-factorized Positive-definite Curvature Learning for NN Training | stat.ML cs.LG | Many training methods, such as Adam(W) and Shampoo, learn a positive-definite
curvature matrix and apply an inverse root before preconditioning. Recently,
non-diagonal training methods, such as Shampoo, have gained significant
attention; however, they remain computationally inefficient and are limited to
specific typ... |
2502.06269 | Progressive Collaborative and Semantic Knowledge Fusion for Generative
Recommendation | cs.IR | With the recent surge in interest surrounding generative paradigms,
generative recommendation has increasingly attracted the attention of
researchers in the recommendation community. This paradigm generally consists
of two stages. In the first stage, pretrained semantic embeddings or
collaborative ID embeddings are q... |
2502.06272 | Beyond Batch Learning: Global Awareness Enhanced Domain Adaptation | cs.LG | In domain adaptation (DA), the effectiveness of deep learning-based models is
often constrained by batch learning strategies that fail to fully apprehend the
global statistical and geometric characteristics of data distributions.
Addressing this gap, we introduce 'Global Awareness Enhanced Domain Adaptation'
(GAN-DA)... |
2502.06274 | HODDI: A Dataset of High-Order Drug-Drug Interactions for Computational
Pharmacovigilance | cs.LG cs.AI q-bio.MN | Drug-side effect research is vital for understanding adverse reactions
arising in complex multi-drug therapies. However, the scarcity of higher-order
datasets that capture the combinatorial effects of multiple drugs severely
limits progress in this field. Existing resources such as TWOSIDES primarily
focus on pairwis... |
2502.06279 | DebateBench: A Challenging Long Context Reasoning Benchmark For Large
Language Models | cs.CL cs.LG | We introduce DebateBench, a novel dataset consisting of an extensive
collection of transcripts and metadata from some of the world's most
prestigious competitive debates. The dataset consists of British Parliamentary
debates from prestigious debating tournaments on diverse topics, annotated with
detailed speech-level... |
2502.06280 | IceBerg: Debiased Self-Training for Class-Imbalanced Node Classification | cs.LG | Graph Neural Networks (GNNs) have achieved great success in dealing with
non-Euclidean graph-structured data and have been widely deployed in many
real-world applications. However, their effectiveness is often jeopardized
under class-imbalanced training sets. Most existing studies have analyzed
class-imbalanced node ... |
2502.06281 | Application of quantum machine learning using quantum kernel algorithms
on multiclass neuron M type classification | quant-ph cs.LG | The functional characterization of different neuronal types has been a
longstanding and crucial challenge. With the advent of physical quantum
computers, it has become possible to apply quantum machine learning algorithms
to translate theoretical research into practical solutions. Previous studies
have shown the adva... |
2502.06282 | Jakiro: Boosting Speculative Decoding with Decoupled Multi-Head via MoE | cs.CL cs.AI cs.LG | Speculative decoding (SD) accelerates large language model inference by using
a smaller draft model to predict multiple tokens, which are then verified in
parallel by the larger target model. However, the limited capacity of the draft
model often necessitates tree-based sampling to improve prediction accuracy,
where ... |
2502.06283 | On the Expressiveness of Rational ReLU Neural Networks With Bounded
Depth | cs.LG cs.DM | To confirm that the expressive power of ReLU neural networks grows with their
depth, the function $F_n = \max \{0,x_1,\ldots,x_n\}$ has been considered in
the literature. A conjecture by Hertrich, Basu, Di Summa, and Skutella [NeurIPS
2021] states that any ReLU network that exactly represents $F_n$ has at least
$\lce... |
2502.06285 | End-to-End Multi-Microphone Speaker Extraction Using Relative Transfer
Functions | cs.SD cs.AI | This paper introduces a multi-microphone method for extracting a desired
speaker from a mixture involving multiple speakers and directional noise in a
reverberant environment. In this work, we propose leveraging the instantaneous
relative transfer function (RTF), estimated from a reference utterance recorded
in the s... |
2502.06287 | CT-UIO: Continuous-Time UWB-Inertial-Odometer Localization Using
Non-Uniform B-spline with Fewer Anchors | cs.RO | Ultra-wideband (UWB) based positioning with fewer anchors has attracted
significant research interest in recent years, especially under
energy-constrained conditions. However, most existing methods rely on
discrete-time representations and smoothness priors to infer a robot's motion
states, which often struggle with ... |
2502.06288 | Enhancing Ground-to-Aerial Image Matching for Visual Misinformation
Detection Using Semantic Segmentation | cs.CV | The recent advancements in generative AI techniques, which have significantly
increased the online dissemination of altered images and videos, have raised
serious concerns about the credibility of digital media available on the
Internet and distributed through information channels and social networks. This
issue part... |
2502.06289 | Is an Ultra Large Natural Image-Based Foundation Model Superior to a
Retina-Specific Model for Detecting Ocular and Systemic Diseases? | eess.IV cs.AI cs.CV | The advent of foundation models (FMs) is transforming medical domain. In
ophthalmology, RETFound, a retina-specific FM pre-trained sequentially on 1.4
million natural images and 1.6 million retinal images, has demonstrated high
adaptability across clinical applications. Conversely, DINOv2, a
general-purpose vision FM... |
2502.06292 | Occupancy-SLAM: An Efficient and Robust Algorithm for Simultaneously
Optimizing Robot Poses and Occupancy Map | cs.RO | Joint optimization of poses and features has been extensively studied and
demonstrated to yield more accurate results in feature-based SLAM problems.
However, research on jointly optimizing poses and non-feature-based maps
remains limited. Occupancy maps are widely used non-feature-based environment
representations b... |
2502.06295 | DVFS-Aware DNN Inference on GPUs: Latency Modeling and Performance
Analysis | cs.LG cs.NI | The rapid development of deep neural networks (DNNs) is inherently
accompanied by the problem of high computational costs. To tackle this
challenge, dynamic voltage frequency scaling (DVFS) is emerging as a promising
technology for balancing the latency and energy consumption of DNN inference by
adjusting the computi... |
2502.06298 | SeaExam and SeaBench: Benchmarking LLMs with Local Multilingual
Questions in Southeast Asia | cs.CL cs.AI | This study introduces two novel benchmarks, SeaExam and SeaBench, designed to
evaluate the capabilities of Large Language Models (LLMs) in Southeast Asian
(SEA) application scenarios. Unlike existing multilingual datasets primarily
derived from English translations, these benchmarks are constructed based on
real-worl... |
2502.06300 | The impact of allocation strategies in subset learning on the expressive
power of neural networks | cs.LG | In traditional machine learning, models are defined by a set of parameters,
which are optimized to perform specific tasks. In neural networks, these
parameters correspond to the synaptic weights. However, in reality, it is often
infeasible to control or update all weights. This challenge is not limited to
artificial ... |
2502.06301 | Utilizing Novelty-based Evolution Strategies to Train Transformers in
Reinforcement Learning | cs.LG cs.NE | In this paper, we experiment with novelty-based variants of OpenAI-ES, the
NS-ES and NSR-ES algorithms, and evaluate their effectiveness in training
complex, transformer-based architectures designed for the problem of
reinforcement learning such as Decision Transformers. We also test if we can
accelerate the novelty-... |
2502.06302 | Latent Convergence Modulation in Large Language Models: A Novel Approach
to Iterative Contextual Realignment | cs.CL | Token prediction stability remains a challenge in autoregressive generative
models, where minor variations in early inference steps often lead to
significant semantic drift over extended sequences. A structured modulation
mechanism was introduced to regulate hidden state transitions, ensuring that
latent representati... |
2502.06307 | Cell Nuclei Detection and Classification in Whole Slide Images with
Transformers | cs.CV | Accurate and efficient cell nuclei detection and classification in
histopathological Whole Slide Images (WSIs) are pivotal for digital pathology
applications. Traditional cell segmentation approaches, while commonly used,
are computationally expensive and require extensive post-processing, limiting
their practicality... |
2502.06309 | Analog In-memory Training on General Non-ideal Resistive Elements: The
Impact of Response Functions | cs.LG cs.AR math.OC | As the economic and environmental costs of training and deploying large
vision or language models increase dramatically, analog in-memory computing
(AIMC) emerges as a promising energy-efficient solution. However, the training
perspective, especially its training dynamic, is underexplored. In AIMC
hardware, the train... |
2502.06314 | From Pixels to Components: Eigenvector Masking for Visual Representation
Learning | cs.LG cs.AI cs.CV | Predicting masked from visible parts of an image is a powerful
self-supervised approach for visual representation learning. However, the
common practice of masking random patches of pixels exhibits certain failure
modes, which can prevent learning meaningful high-level features, as required
for downstream tasks. We p... |
2502.06316 | Can AI Examine Novelty of Patents?: Novelty Evaluation Based on the
Correspondence between Patent Claim and Prior Art | cs.CL | Assessing the novelty of patent claims is a critical yet challenging task
traditionally performed by patent examiners. While advancements in NLP have
enabled progress in various patent-related tasks, novelty assessment remains
unexplored. This paper introduces a novel challenge by evaluating the ability
of large lang... |
2502.06323 | A physics-based data-driven model for CO$_2$ gas diffusion electrodes to
drive automated laboratories | cond-mat.mtrl-sci cs.LG | The electrochemical reduction of atmospheric CO$_2$ into high-energy
molecules with renewable energy is a promising avenue for energy storage that
can take advantage of existing infrastructure especially in areas where
sustainable alternatives to fossil fuels do not exist. Automated laboratories
are currently being d... |
2502.06324 | UniDemoir\'e: Towards Universal Image Demoir\'eing with Data Generation
and Synthesis | cs.CV cs.AI | Image demoir\'eing poses one of the most formidable challenges in image
restoration, primarily due to the unpredictable and anisotropic nature of
moir\'e patterns. Limited by the quantity and diversity of training data,
current methods tend to overfit to a single moir\'e domain, resulting in
performance degradation f... |
2502.06327 | Prompt-Driven Continual Graph Learning | cs.LG cs.AI | Continual Graph Learning (CGL), which aims to accommodate new tasks over
evolving graph data without forgetting prior knowledge, is garnering
significant research interest. Mainstream solutions adopt the memory
replay-based idea, ie, caching representative data from earlier tasks for
retraining the graph model. Howev... |
2502.06329 | Expect the Unexpected: FailSafe Long Context QA for Finance | cs.CL | We propose a new long-context financial benchmark, FailSafeQA, designed to
test the robustness and context-awareness of LLMs against six variations in
human-interface interactions in LLM-based query-answer systems within finance.
We concentrate on two case studies: Query Failure and Context Failure. In the
Query Fail... |
2502.06331 | Conformal Prediction Regions are Imprecise Highest Density Regions | stat.ML cs.LG math.PR | Recently, Cella and Martin proved how, under an assumption called consonance,
a credal set (i.e. a closed and convex set of probabilities) can be derived
from the conformal transducer associated with transductive conformal
prediction. We show that the Imprecise Highest Density Region (IHDR) associated
with such a cre... |
2502.06335 | Microcanonical Langevin Ensembles: Advancing the Sampling of Bayesian
Neural Networks | cs.LG | Despite recent advances, sampling-based inference for Bayesian Neural
Networks (BNNs) remains a significant challenge in probabilistic deep learning.
While sampling-based approaches do not require a variational distribution
assumption, current state-of-the-art samplers still struggle to navigate the
complex and highl... |
2502.06336 | DefTransNet: A Transformer-based Method for Non-Rigid Point Cloud
Registration in the Simulation of Soft Tissue Deformation | cs.CV cs.AI | Soft-tissue surgeries, such as tumor resections, are complicated by tissue
deformations that can obscure the accurate location and shape of tissues. By
representing tissue surfaces as point clouds and applying non-rigid point cloud
registration (PCR) methods, surgeons can better understand tissue deformations
before,... |
2502.06337 | Accelerating Outlier-robust Rotation Estimation by Stereographic
Projection | cs.CV cs.RO | Rotation estimation plays a fundamental role in many computer vision and
robot tasks. However, efficiently estimating rotation in large inputs
containing numerous outliers (i.e., mismatches) and noise is a recognized
challenge. Many robust rotation estimation methods have been designed to
address this challenge. Unfo... |
2502.06338 | Zero-shot Depth Completion via Test-time Alignment with Affine-invariant
Depth Prior | cs.CV | Depth completion, predicting dense depth maps from sparse depth measurements,
is an ill-posed problem requiring prior knowledge. Recent methods adopt
learning-based approaches to implicitly capture priors, but the priors
primarily fit in-domain data and do not generalize well to out-of-domain
scenarios. To address th... |
2502.06341 | Facial Analysis Systems and Down Syndrome | cs.CV cs.AI cs.HC cs.LG | The ethical, social and legal issues surrounding facial analysis technologies
have been widely debated in recent years. Key critics have argued that these
technologies can perpetuate bias and discrimination, particularly against
marginalized groups. We contribute to this field of research by reporting on
the limitati... |
2502.06342 | The exponential distribution of the orders of demonstrative, numeral,
adjective and noun | cs.CL physics.soc-ph | The frequency of the preferred order for a noun phrase formed by
demonstrative, numeral, adjective and noun has received significant attention
over the last two decades. We investigate the actual distribution of the
preferred 24 possible orders. There is no consensus on whether it can be
well-fitted by an exponential... |
2502.06343 | Causal Lifting of Neural Representations: Zero-Shot Generalization for
Causal Inferences | cs.LG stat.ML | A plethora of real-world scientific investigations is waiting to scale with
the support of trustworthy predictive models that can reduce the need for
costly data annotations. We focus on causal inferences on a target experiment
with unlabeled factual outcomes, retrieved by a predictive model fine-tuned on
a labeled s... |
2502.06348 | AiRacleX: Automated Detection of Price Oracle Manipulations via
LLM-Driven Knowledge Mining and Prompt Generation | cs.CR cs.AI | Decentralized finance (DeFi) applications depend on accurate price oracles to
ensure secure transactions, yet these oracles are highly vulnerable to
manipulation, enabling attackers to exploit smart contract vulnerabilities for
unfair asset valuation and financial gain. Detecting such manipulations
traditionally reli... |
2502.06349 | Provably Near-Optimal Federated Ensemble Distillation with Negligible
Overhead | cs.LG | Federated ensemble distillation addresses client heterogeneity by generating
pseudo-labels for an unlabeled server dataset based on client predictions and
training the server model using the pseudo-labeled dataset. The unlabeled
server dataset can either be pre-existing or generated through a data-free
approach. The ... |
2502.06351 | Calibrating LLMs with Information-Theoretic Evidential Deep Learning | cs.LG | Fine-tuned large language models (LLMs) often exhibit overconfidence,
particularly when trained on small datasets, resulting in poor calibration and
inaccurate uncertainty estimates. Evidential Deep Learning (EDL), an
uncertainty-aware approach, enables uncertainty estimation in a single forward
pass, making it a pro... |
2502.06352 | LANTERN++: Enhanced Relaxed Speculative Decoding with Static Tree
Drafting for Visual Auto-regressive Models | cs.CV | Speculative decoding has been widely used to accelerate autoregressive (AR)
text generation. However, its effectiveness in visual AR models remains limited
due to token selection ambiguity, where multiple tokens receive similarly low
probabilities, reducing acceptance rates. While dynamic tree drafting has been
propo... |
2502.06354 | Guidance-base Diffusion Models for Improving Photoacoustic Image Quality | cs.CV | Photoacoustic(PA) imaging is a non-destructive and non-invasive technology
for visualizing minute blood vessel structures in the body using ultrasonic
sensors. In PA imaging, the image quality of a single-shot image is poor, and
it is necessary to improve the image quality by averaging many single-shot
images. Theref... |
2502.06355 | Fine-tuning Multimodal Transformers on Edge: A Parallel Split Learning
Approach | cs.DC cs.LG | Multimodal transformers integrate diverse data types like images, audio, and
text, advancing tasks such as audio-visual understanding and image-text
retrieval; yet their high parameterization limits deployment on
resource-constrained edge devices. Split Learning (SL), which partitions models
at a designated cut-layer... |
2502.06358 | Towards bandit-based prompt-tuning for in-the-wild foundation agents | cs.LG | Prompting has emerged as the dominant paradigm for adapting large,
pre-trained transformer-based models to downstream tasks. The Prompting
Decision Transformer (PDT) enables large-scale, multi-task offline
reinforcement learning pre-training by leveraging stochastic trajectory prompts
to identify the target task. How... |
2502.06359 | Occlusion-Aware Contingency Safety-Critical Planning for Autonomous
Vehicles | cs.RO | Ensuring safe driving while maintaining travel efficiency for autonomous
vehicles in dynamic and occluded environments is a critical challenge. This
paper proposes an occlusion-aware contingency safety-critical planning approach
for real-time autonomous driving in such environments. Leveraging reachability
analysis f... |
2502.06361 | Weld n'Cut: Automated fabrication of inflatable fabric actuators | cs.RO | Lightweight, durable textile-based inflatable soft actuators are widely used
in soft robotics, particularly for wearable robots in rehabilitation and in
enhancing human performance in demanding jobs. Fabricating these actuators
typically involves multiple steps: heat-sealable fabrics are fused with a heat
press, and ... |
2502.06362 | Proprioceptive Origami Manipulator | cs.RO | Origami offers a versatile framework for designing morphable structures and
soft robots by exploiting the geometry of folds. Tubular origami structures can
act as continuum manipulators that balance flexibility and strength. However,
precise control of such manipulators often requires reliance on vision-based
systems... |
2502.06363 | Improved Regret Analysis in Gaussian Process Bandits: Optimality for
Noiseless Reward, RKHS norm, and Non-Stationary Variance | cs.LG stat.ML | We study the Gaussian process (GP) bandit problem, whose goal is to minimize
regret under an unknown reward function lying in some reproducing kernel
Hilbert space (RKHS). The maximum posterior variance analysis is vital in
analyzing near-optimal GP bandit algorithms such as maximum variance reduction
(MVR) and phase... |
2502.06364 | Automatic Identification of Samples in Hip-Hop Music via Multi-Loss
Training and an Artificial Dataset | cs.SD cs.LG eess.AS | Sampling, the practice of reusing recorded music or sounds from another
source in a new work, is common in popular music genres like hip-hop and rap.
Numerous services have emerged that allow users to identify connections between
samples and the songs that incorporate them, with the goal of enhancing music
discovery.... |
2502.06367 | FOCUS -- Multi-View Foot Reconstruction From Synthetically Trained Dense
Correspondences | cs.CV | Surface reconstruction from multiple, calibrated images is a challenging task
- often requiring a large number of collected images with significant overlap.
We look at the specific case of human foot reconstruction. As with previous
successful foot reconstruction work, we seek to extract rich per-pixel geometry
cues ... |
2502.06374 | Hyperparameters in Score-Based Membership Inference Attacks | cs.LG cs.AI | Membership Inference Attacks (MIAs) have emerged as a valuable framework for
evaluating privacy leakage by machine learning models. Score-based MIAs are
distinguished, in particular, by their ability to exploit the confidence scores
that the model generates for particular inputs. Existing score-based MIAs
implicitly ... |
2502.06376 | Many-Task Federated Fine-Tuning via Unified Task Vectors | cs.LG cs.CV | Federated Learning (FL) traditionally assumes homogeneous client tasks;
however, in real-world scenarios, clients often specialize in diverse tasks,
introducing task heterogeneity. To address this challenge, Many-Task FL
(MaT-FL) has emerged, enabling clients to collaborate effectively despite task
diversity. Existin... |
2502.06379 | Solving Linear-Gaussian Bayesian Inverse Problems with Decoupled
Diffusion Sequential Monte Carlo | cs.LG cs.AI stat.ML | A recent line of research has exploited pre-trained generative diffusion
models as priors for solving Bayesian inverse problems. We contribute to this
research direction by designing a sequential Monte Carlo method for
linear-Gaussian inverse problems which builds on ``decoupled diffusion", where
the generative proce... |
2502.06380 | Structure-preserving contrastive learning for spatial time series | cs.LG cs.CV | Informative representations enhance model performance and generalisability in
downstream tasks. However, learning self-supervised representations for
spatially characterised time series, like traffic interactions, poses
challenges as it requires maintaining fine-grained similarity relations in the
latent space. In th... |
2502.06387 | How Humans Help LLMs: Assessing and Incentivizing Human Preference
Annotators | cs.LG cs.GT econ.TH | Human-annotated preference data play an important role in aligning large
language models (LLMs). In this paper, we investigate the questions of
assessing the performance of human annotators and incentivizing them to provide
high-quality annotations. The quality assessment of language/text annotation
faces two challen... |
2502.06390 | When Data Manipulation Meets Attack Goals: An In-depth Survey of Attacks
for VLMs | cs.CV | Vision-Language Models (VLMs) have gained considerable prominence in recent
years due to their remarkable capability to effectively integrate and process
both textual and visual information. This integration has significantly
enhanced performance across a diverse spectrum of applications, such as scene
perception and... |
2502.06392 | TANGLED: Generating 3D Hair Strands from Images with Arbitrary Styles
and Viewpoints | cs.CV cs.GR | Hairstyles are intricate and culturally significant with various geometries,
textures, and structures. Existing text or image-guided generation methods fail
to handle the richness and complexity of diverse styles. We present TANGLED, a
novel approach for 3D hair strand generation that accommodates diverse image
input... |
2502.06394 | SynthDetoxM: Modern LLMs are Few-Shot Parallel Detoxification Data
Annotators | cs.CL | Existing approaches to multilingual text detoxification are hampered by the
scarcity of parallel multilingual datasets. In this work, we introduce a
pipeline for the generation of multilingual parallel detoxification data. We
also introduce SynthDetoxM, a manually collected and synthetically generated
multilingual pa... |
2502.06395 | AppVLM: A Lightweight Vision Language Model for Online App Control | cs.AI | The utilisation of foundation models as smartphone assistants, termed app
agents, is a critical research challenge. These agents aim to execute human
instructions on smartphones by interpreting textual instructions and performing
actions via the device's interface. While promising, current approaches face
significant... |
2502.06398 | Learning Counterfactual Outcomes Under Rank Preservation | cs.LG stat.ML | Counterfactual inference aims to estimate the counterfactual outcome at the
individual level given knowledge of an observed treatment and the factual
outcome, with broad applications in fields such as epidemiology, econometrics,
and management science. Previous methods rely on a known structural causal
model (SCM) or... |
2502.06399 | A Linearly Convergent Algorithm for Computing the Petz-Augustin
Information | quant-ph cs.IT math.IT math.OC | We propose an iterative algorithm for computing the Petz-Augustin information
of order $\alpha\in(1/2,1)\cup(1,\infty)$. The optimization error is guaranteed
to converge at a rate of $O\left(\vert 1-1/\alpha \vert^T\right)$, where $T$ is
the number of iterations. Let $n$ denote the cardinality of the input alphabet
o... |
2502.06401 | Habitizing Diffusion Planning for Efficient and Effective Decision
Making | cs.LG | Diffusion models have shown great promise in decision-making, also known as
diffusion planning. However, the slow inference speeds limit their potential
for broader real-world applications. Here, we introduce Habi, a general
framework that transforms powerful but slow diffusion planning models into fast
decision-maki... |
2502.06403 | The AI off-switch problem as a signalling game: bounded rationality and
incomparability | cs.LG | The off-switch problem is a critical challenge in AI control: if an AI system
resists being switched off, it poses a significant risk. In this paper, we
model the off-switch problem as a signalling game, where a human decision-maker
communicates its preferences about some underlying decision problem to an AI
agent, w... |
2502.06407 | An Automated Machine Learning Framework for Surgical Suturing Action
Detection under Class Imbalance | cs.LG cs.RO | In laparoscopy surgical training and evaluation, real-time detection of
surgical actions with interpretable outputs is crucial for automated and
real-time instructional feedback and skill development. Such capability would
enable development of machine guided training systems. This paper presents a
rapid deployment a... |
2502.06412 | Toolbox for Developing Physics Informed Neural Networks for Power
Systems Components | eess.SY cs.SY | This paper puts forward the vision of creating a library of
neural-network-based models for power system simulations. Traditional numerical
solvers struggle with the growing complexity of modern power systems,
necessitating faster and more scalable alternatives. Physics-Informed Neural
Networks (PINNs) offer promise ... |
2502.06415 | Systematic Outliers in Large Language Models | cs.CL cs.AI cs.LG | Outliers have been widely observed in Large Language Models (LLMs),
significantly impacting model performance and posing challenges for model
compression. Understanding the functionality and formation mechanisms of these
outliers is critically important. Existing works, however, largely focus on
reducing the impact o... |
2502.06418 | Robust Watermarks Leak: Channel-Aware Feature Extraction Enables
Adversarial Watermark Manipulation | cs.CV cs.CR | Watermarking plays a key role in the provenance and detection of AI-generated
content. While existing methods prioritize robustness against real-world
distortions (e.g., JPEG compression and noise addition), we reveal a
fundamental tradeoff: such robust watermarks inherently improve the redundancy
of detectable patte... |
2502.06419 | Occ-LLM: Enhancing Autonomous Driving with Occupancy-Based Large
Language Models | cs.RO | Large Language Models (LLMs) have made substantial advancements in the field
of robotic and autonomous driving. This study presents the first
Occupancy-based Large Language Model (Occ-LLM), which represents a pioneering
effort to integrate LLMs with an important representation. To effectively
encode occupancy as inpu... |
2502.06424 | CS-SHAP: Extending SHAP to Cyclic-Spectral Domain for Better
Interpretability of Intelligent Fault Diagnosis | cs.LG cs.AI | Neural networks (NNs), with their powerful nonlinear mapping and end-to-end
capabilities, are widely applied in mechanical intelligent fault diagnosis
(IFD). However, as typical black-box models, they pose challenges in
understanding their decision basis and logic, limiting their deployment in
high-reliability scenar... |
2502.06425 | Generating Privacy-Preserving Personalized Advice with Zero-Knowledge
Proofs and LLMs | cs.CR cs.AI | Large language models (LLMs) are increasingly utilized in domains such as
finance, healthcare, and interpersonal relationships to provide advice tailored
to user traits and contexts. However, this personalization often relies on
sensitive data, raising critical privacy concerns and necessitating data
minimization. To... |
2502.06427 | Hybrid State-Space and GRU-based Graph Tokenization Mamba for
Hyperspectral Image Classification | cs.CV | Hyperspectral image (HSI) classification plays a pivotal role in domains such
as environmental monitoring, agriculture, and urban planning. However, it faces
significant challenges due to the high-dimensional nature of the data and the
complex spectral-spatial relationships inherent in HSI. Traditional methods,
inclu... |
2502.06428 | CoS: Chain-of-Shot Prompting for Long Video Understanding | cs.CV | Multi-modal Large Language Models (MLLMs) struggle with long videos due to
the need for excessive visual tokens. These tokens exceed massively the context
length of MLLMs, resulting in filled by redundant task-irrelevant shots. How to
select shots is an unsolved critical problem: sparse sampling risks missing key
det... |
2502.06430 | Content-Driven Local Response: Supporting Sentence-Level and
Message-Level Mobile Email Replies With and Without AI | cs.HC cs.CL | Mobile emailing demands efficiency in diverse situations, which motivates the
use of AI. However, generated text does not always reflect how people want to
respond. This challenges users with AI involvement tradeoffs not yet considered
in email UIs. We address this with a new UI concept called Content-Driven Local
Re... |
2502.06431 | FCVSR: A Frequency-aware Method for Compressed Video Super-Resolution | cs.CV | Compressed video super-resolution (SR) aims to generate high-resolution (HR)
videos from the corresponding low-resolution (LR) compressed videos. Recently,
some compressed video SR methods attempt to exploit the spatio-temporal
information in the frequency domain, showing great promise in super-resolution
performance... |
2502.06432 | Prompt-SID: Learning Structural Representation Prompt via Latent
Diffusion for Single-Image Denoising | cs.CV cs.AI | Many studies have concentrated on constructing supervised models utilizing
paired datasets for image denoising, which proves to be expensive and
time-consuming. Current self-supervised and unsupervised approaches typically
rely on blind-spot networks or sub-image pairs sampling, resulting in pixel
information loss an... |
2502.06434 | Rethinking Large-scale Dataset Compression: Shifting Focus From Labels
to Images | cs.CV cs.LG | Dataset distillation and dataset pruning are two prominent techniques for
compressing datasets to improve computational and storage efficiency. Despite
their overlapping objectives, these approaches are rarely compared directly.
Even within each field, the evaluation protocols are inconsistent across
various methods,... |
2502.06438 | FEMBA: Efficient and Scalable EEG Analysis with a Bidirectional Mamba
Foundation Model | cs.LG cs.AI | Accurate and efficient electroencephalography (EEG) analysis is essential for
detecting seizures and artifacts in long-term monitoring, with applications
spanning hospital diagnostics to wearable health devices. Robust EEG analytics
have the potential to greatly improve patient care. However, traditional deep
learnin... |
2502.06439 | Testing software for non-discrimination: an updated and extended audit
in the Italian car insurance domain | cs.SE cs.AI cs.HC cs.LG | Context. As software systems become more integrated into society's
infrastructure, the responsibility of software professionals to ensure
compliance with various non-functional requirements increases. These
requirements include security, safety, privacy, and, increasingly,
non-discrimination.
Motivation. Fairness i... |
2502.06440 | SIGMA: Sheaf-Informed Geometric Multi-Agent Pathfinding | cs.RO cs.AI cs.MA | The Multi-Agent Path Finding (MAPF) problem aims to determine the shortest
and collision-free paths for multiple agents in a known, potentially
obstacle-ridden environment. It is the core challenge for robotic deployments
in large-scale logistics and transportation. Decentralized learning-based
approaches have shown ... |
2502.06443 | Low-dimensional Functions are Efficiently Learnable under Randomly
Biased Distributions | cs.LG stat.ML | The problem of learning single index and multi index models has gained
significant interest as a fundamental task in high-dimensional statistics. Many
recent works have analysed gradient-based methods, particularly in the setting
of isotropic data distributions, often in the context of neural network
training. Such s... |
2502.06445 | Benchmarking Vision-Language Models on Optical Character Recognition in
Dynamic Video Environments | cs.CV | This paper introduces an open-source benchmark for evaluating Vision-Language
Models (VLMs) on Optical Character Recognition (OCR) tasks in dynamic video
environments. We present a curated dataset containing 1,477 manually annotated
frames spanning diverse domains, including code editors, news broadcasts,
YouTube vid... |
2502.06452 | SparseFocus: Learning-based One-shot Autofocus for Microscopy with
Sparse Content | cs.CV q-bio.QM | Autofocus is necessary for high-throughput and real-time scanning in
microscopic imaging. Traditional methods rely on complex hardware or iterative
hill-climbing algorithms. Recent learning-based approaches have demonstrated
remarkable efficacy in a one-shot setting, avoiding hardware modifications or
iterative mecha... |
2502.06453 | MATH-Perturb: Benchmarking LLMs' Math Reasoning Abilities against Hard
Perturbations | cs.LG cs.AI cs.CL | Large language models have demonstrated impressive performance on challenging
mathematical reasoning tasks, which has triggered the discussion of whether the
performance is achieved by true reasoning capability or memorization. To
investigate this question, prior work has constructed mathematical benchmarks
when ques... |
2502.06460 | Group-CLIP Uncertainty Modeling for Group Re-Identification | cs.CV | Group Re-Identification (Group ReID) aims matching groups of pedestrians
across non-overlapping cameras. Unlike single-person ReID, Group ReID focuses
more on the changes in group structure, emphasizing the number of members and
their spatial arrangement. However, most methods rely on certainty-based
models, which co... |
2502.06466 | Inflatable Kirigami Crawlers | cs.RO | Kirigami offers unique opportunities for guided morphing by leveraging the
geometry of the cuts. This work presents inflatable kirigami crawlers created
by introducing cut patterns into heat-sealable textiles to achieve locomotion
upon cyclic pneumatic actuation. Inflating traditional air pouches results in
symmetric... |
2502.06468 | Beyond Literal Token Overlap: Token Alignability for Multilinguality | cs.CL | Previous work has considered token overlap, or even similarity of token
distributions, as predictors for multilinguality and cross-lingual knowledge
transfer in language models. However, these very literal metrics assign large
distances to language pairs with different scripts, which can nevertheless show
good cross-... |
2502.06469 | Stochastic MPC with Online-optimized Policies and Closed-loop Guarantees | eess.SY cs.SY math.OC | This paper proposes a stochastic model predictive control method for linear
systems affected by additive Gaussian disturbances. Closed-loop satisfaction of
probabilistic constraints and recursive feasibility of the underlying convex
optimization problem is guaranteed. Optimization over feedback policies online
increa... |
2502.06470 | A Survey of Theory of Mind in Large Language Models: Evaluations,
Representations, and Safety Risks | cs.CL cs.AI | Theory of Mind (ToM), the ability to attribute mental states to others and
predict their behaviour, is fundamental to social intelligence. In this paper,
we survey studies evaluating behavioural and representational ToM in Large
Language Models (LLMs), identify important safety risks from advanced LLM ToM
capabilitie... |
2502.06472 | KARMA: Leveraging Multi-Agent LLMs for Automated Knowledge Graph
Enrichment | cs.CL cs.AI cs.CE cs.DL | Maintaining comprehensive and up-to-date knowledge graphs (KGs) is critical
for modern AI systems, but manual curation struggles to scale with the rapid
growth of scientific literature. This paper presents KARMA, a novel framework
employing multi-agent large language models (LLMs) to automate KG enrichment
through st... |
2502.06474 | UniMoD: Efficient Unified Multimodal Transformers with Mixture-of-Depths | cs.CV | Unified multimodal transformers, which handle both generation and
understanding tasks within a shared parameter space, have received increasing
attention in recent research. Although various unified transformers have been
proposed, training these models is costly due to redundant tokens and heavy
attention computatio... |
2502.06476 | Image Intrinsic Scale Assessment: Bridging the Gap Between Quality and
Resolution | cs.CV | Image Quality Assessment (IQA) measures and predicts perceived image quality
by human observers. Although recent studies have highlighted the critical
influence that variations in the scale of an image have on its perceived
quality, this relationship has not been systematically quantified. To bridge
this gap, we intr... |
2502.06480 | Logarithmic Regret of Exploration in Average Reward Markov Decision
Processes | cs.LG stat.ML | In average reward Markov decision processes, state-of-the-art algorithms for
regret minimization follow a well-established framework: They are model-based,
optimistic and episodic. First, they maintain a confidence region from which
optimistic policies are computed using a well-known subroutine called Extended
Value ... |
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