id stringlengths 9 16 | title stringlengths 4 278 | categories stringlengths 5 104 | abstract stringlengths 6 4.09k |
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2502.13968 | Betsu-Betsu: Multi-View Separable 3D Reconstruction of Two Interacting
Objects | cs.CV | Separable 3D reconstruction of multiple objects from multi-view RGB images --
resulting in two different 3D shapes for the two objects with a clear
separation between them -- remains a sparsely researched problem. It is
challenging due to severe mutual occlusions and ambiguities along the objects'
interaction boundar... |
2502.13969 | Bridging Simulation and Reality: A 3D Clustering-Based Deep Learning
Model for UAV-Based RF Source Localization | eess.SP cs.AI | Localization of radio frequency (RF) sources has critical applications,
including search and rescue, jammer detection, and monitoring of hostile
activities. Unmanned aerial vehicles (UAVs) offer significant advantages for RF
source localization (RFSL) over terrestrial methods, leveraging autonomous 3D
navigation and ... |
2502.13972 | IncepFormerNet: A multi-scale multi-head attention network for SSVEP
classification | eess.SP cs.AI cs.LG | In recent years, deep learning (DL) models have shown outstanding performance
in EEG classification tasks, particularly in Steady-State Visually Evoked
Potential(SSVEP)-based Brain-Computer-Interfaces(BCI)systems. DL methods have
been successfully applied to SSVEP-BCI. This study proposes a new model called
IncepForm... |
2502.13974 | Segmentation-free integration of nuclei morphology and spatial
transcriptomics for retinal images | eess.IV cs.CV | This study introduces SEFI (SEgmentation-Free Integration), a novel method
for integrating morphological features of cell nuclei with spatial
transcriptomics data. Cell segmentation poses a significant challenge in the
analysis of spatial transcriptomics data, as tissue-specific structural
complexities and densely pa... |
2502.13976 | Regulariza\c{c}\~ao, aprendizagem profunda e interdisciplinaridade em
problemas inversos mal-postos | eess.IV cs.LG | In this book, written in Portuguese, we discuss what ill-posed problems are
and how the regularization method is used to solve them. In the form of
questions and answers, we reflect on the origins and future of regularization,
relating the similarities and differences of its meaning in different areas,
including inve... |
2502.13979 | Utilizing Effective Dynamic Graph Learning to Shield Financial Stability
from Risk Propagation | q-fin.RM cs.AI cs.LG | Financial risks can propagate across both tightly coupled temporal and
spatial dimensions, posing significant threats to financial stability.
Moreover, risks embedded in unlabeled data are often difficult to detect. To
address these challenges, we introduce GraphShield, a novel approach with three
key innovations: En... |
2502.13982 | Benchmarking Automatic Speech Recognition coupled LLM Modules for
Medical Diagnostics | eess.AS cs.LG | Natural Language Processing (NLP) and Voice Recognition agents are rapidly
evolving healthcare by enabling efficient, accessible, and professional patient
support while automating grunt work. This report serves as my self project
wherein models finetuned on medical call recordings are analysed through a
two-stage sys... |
2502.13983 | Gesture-Aware Zero-Shot Speech Recognition for Patients with Language
Disorders | eess.AS cs.AI | Individuals with language disorders often face significant communication
challenges due to their limited language processing and comprehension
abilities, which also affect their interactions with voice-assisted systems
that mostly rely on Automatic Speech Recognition (ASR). Despite advancements in
ASR that address di... |
2502.13990 | Remote Sensing Semantic Segmentation Quality Assessment based on Vision
Language Model | eess.IV cs.LG | The complexity of scenes and variations in image quality result in
significant variability in the performance of semantic segmentation methods of
remote sensing imagery (RSI) in supervised real-world scenarios. This makes the
evaluation of semantic segmentation quality in such scenarios an issue to be
resolved. Howev... |
2502.13991 | Learning to Discover Regulatory Elements for Gene Expression Prediction | q-bio.GN cs.AI | We consider the problem of predicting gene expressions from DNA sequences. A
key challenge of this task is to find the regulatory elements that control gene
expressions. Here, we introduce Seq2Exp, a Sequence to Expression network
explicitly designed to discover and extract regulatory elements that drive
target gene ... |
2502.13994 | Generative Detail Enhancement for Physically Based Materials | cs.GR cs.AI | We present a tool for enhancing the detail of physically based materials
using an off-the-shelf diffusion model and inverse rendering. Our goal is to
enhance the visual fidelity of materials with detail that is often tedious to
author, by adding signs of wear, aging, weathering, etc. As these appearance
details are o... |
2502.13996 | Beyond Single-Value Metrics: Evaluating and Enhancing LLM Unlearning
with Cognitive Diagnosis | cs.LG | Due to the widespread use of LLMs and the rising critical ethical and safety
concerns, LLM unlearning methods have been developed to remove harmful
knowledge and undesirable capabilities. In this context, evaluations are mostly
based on single-value metrics such as QA accuracy. However, these metrics often
fail to ca... |
2502.13998 | A Baseline Method for Removing Invisible Image Watermarks using Deep
Image Prior | eess.IV cs.AI | Image watermarks have been considered a promising technique to help detect
AI-generated content, which can be used to protect copyright or prevent fake
image abuse. In this work, we present a black-box method for removing invisible
image watermarks, without the need of any dataset of watermarked images or any
knowled... |
2502.14000 | Human-Artificial Interaction in the Age of Agentic AI: A
System-Theoretical Approach | cs.MA cs.AI cs.HC | This paper presents a novel perspective on human-computer interaction (HCI),
framing it as a dynamic interplay between human and computational agents within
a networked system. Going beyond traditional interface-based approaches, we
emphasize the importance of coordination and communication among heterogeneous
agents... |
2502.14001 | Towards a perturbation-based explanation for medical AI as
differentiable programs | stat.ML cs.AI cs.LG | Recent advancement in machine learning algorithms reaches a point where
medical devices can be equipped with artificial intelligence (AI) models for
diagnostic support and routine automation in clinical settings. In medicine and
healthcare, there is a particular demand for sufficient and objective
explainability of t... |
2502.14003 | Rectified Lagrangian for Out-of-Distribution Detection in Modern
Hopfield Networks | cs.LG cs.AI | Modern Hopfield networks (MHNs) have recently gained significant attention in
the field of artificial intelligence because they can store and retrieve a
large set of patterns with an exponentially large memory capacity. A MHN is
generally a dynamical system defined with Lagrangians of memory and feature
neurons, wher... |
2502.14004 | Inter3D: A Benchmark and Strong Baseline for Human-Interactive 3D Object
Reconstruction | cs.GR cs.LG | Recent advancements in implicit 3D reconstruction methods, e.g., neural
rendering fields and Gaussian splatting, have primarily focused on novel view
synthesis of static or dynamic objects with continuous motion states. However,
these approaches struggle to efficiently model a human-interactive object with
n movable ... |
2502.14005 | Smaller But Better: Unifying Layout Generation with Smaller Large
Language Models | cs.LG | We propose LGGPT, an LLM-based model tailored for unified layout generation.
First, we propose Arbitrary Layout Instruction (ALI) and Universal Layout
Response (ULR) as the uniform I/O template. ALI accommodates arbitrary layout
generation task inputs across multiple layout domains, enabling LGGPT to unify
both task-... |
2502.14008 | MaskPrune: Mask-based LLM Pruning for Layer-wise Uniform Structures | cs.CL cs.AI cs.LG | The remarkable performance of large language models (LLMs) in various
language tasks has attracted considerable attention. However, the
ever-increasing size of these models presents growing challenges for deployment
and inference. Structured pruning, an effective model compression technique, is
gaining increasing att... |
2502.14009 | Benchmarking Self-Supervised Methods for Accelerated MRI Reconstruction | eess.IV cs.LG | Reconstructing MRI from highly undersampled measurements is crucial for
accelerating medical imaging, but is challenging due to the ill-posedness of
the inverse problem. While supervised deep learning approaches have shown
remarkable success, they rely on fully-sampled ground truth data, which is
often impractical or... |
2502.14010 | Which Attention Heads Matter for In-Context Learning? | cs.LG cs.AI cs.CL | Large language models (LLMs) exhibit impressive in-context learning (ICL)
capability, enabling them to perform new tasks using only a few demonstrations
in the prompt. Two different mechanisms have been proposed to explain ICL:
induction heads that find and copy relevant tokens, and function vector (FV)
heads whose a... |
2502.14011 | DFDT: Dynamic Fast Decision Tree for IoT Data Stream Mining on Edge
Devices | cs.LG cs.AI cs.NI | The Internet of Things generates massive data streams, with edge computing
emerging as a key enabler for online IoT applications and 5G networks. Edge
solutions facilitate real-time machine learning inference, but also require
continuous adaptation to concept drifts. Ensemble-based solutions improve
predictive perfor... |
2502.14013 | Appeal prediction for AI up-scaled Images | cs.GR cs.AI eess.IV | DNN- or AI-based up-scaling algorithms are gaining in popularity due to the
improvements in machine learning. Various up-scaling models using CNNs, GANs or
mixed approaches have been published. The majority of models are evaluated
using PSRN and SSIM or only a few example images. However, a performance
evaluation wit... |
2502.14018 | I Want 'Em All (At Once) -- Ultrametric Cluster Hierarchies | cs.LG | Hierarchical clustering is a powerful tool for exploratory data analysis,
organizing data into a tree of clusterings from which a partition can be
chosen. This paper generalizes these ideas by proving that, for any reasonable
hierarchy, one can optimally solve any center-based clustering objective over
it (such as $k... |
2502.14019 | Dehumanizing Machines: Mitigating Anthropomorphic Behaviors in Text
Generation Systems | cs.CL cs.AI cs.HC | As text generation systems' outputs are increasingly anthropomorphic --
perceived as human-like -- scholars have also raised increasing concerns about
how such outputs can lead to harmful outcomes, such as users over-relying or
developing emotional dependence on these systems. How to intervene on such
system outputs ... |
2502.14022 | A General Framework for Augmenting Lossy Compressors with Topological
Guarantees | cs.DC cs.IT math.IT | Topological descriptors such as contour trees are widely utilized in
scientific data analysis and visualization, with applications from materials
science to climate simulations. It is desirable to preserve topological
descriptors when data compression is part of the scientific workflow for these
applications. However... |
2502.14023 | Dynamic Activation with Knowledge Distillation for Energy-Efficient
Spiking NN Ensembles | cs.LG cs.AI cs.CV cs.NE | While foundation AI models excel at tasks like classification and
decision-making, their high energy consumption makes them unsuitable for
energy-constrained applications. Inspired by the brain's efficiency, spiking
neural networks (SNNs) have emerged as a viable alternative due to their
event-driven nature and compa... |
2502.14037 | DiffSampling: Enhancing Diversity and Accuracy in Neural Text Generation | cs.CL cs.AI cs.LG | Despite their increasing performance, large language models still tend to
reproduce training data, generate several repetitions, and focus on the most
common grammatical structures and words. A possible cause is the decoding
strategy adopted: the most common ones either consider only the most probable
tokens, reducin... |
2502.14043 | Asking for Help Enables Safety Guarantees Without Sacrificing
Effectiveness | cs.LG cs.AI | Most reinforcement learning algorithms with regret guarantees rely on a
critical assumption: that all errors are recoverable. Recent work by Plaut et
al. discarded this assumption and presented algorithms that avoid "catastrophe"
(i.e., irreparable errors) by asking for help. However, they provided only
safety guaran... |
2502.14044 | Enhancing Cognition and Explainability of Multimodal Foundation Models
with Self-Synthesized Data | cs.CV cs.LG | Large multimodal models (LMMs) have shown impressive capabilities in a wide
range of visual tasks. However, they often struggle with fine-grained visual
reasoning, failing to identify domain-specific objectives and provide
justifiable explanations for their predictions. To address this, we propose a
novel visual reje... |
2502.14045 | Position: There are no Champions in Long-Term Time Series Forecasting | cs.LG cs.AI | Recent advances in long-term time series forecasting have introduced numerous
complex prediction models that consistently outperform previously published
architectures. However, this rapid progression raises concerns regarding
inconsistent benchmarking and reporting practices, which may undermine the
reliability of t... |
2502.14047 | Towards a Learning Theory of Representation Alignment | cs.LG cs.AI stat.ML | It has recently been argued that AI models' representations are becoming
aligned as their scale and performance increase. Empirical analyses have been
designed to support this idea and conjecture the possible alignment of
different representations toward a shared statistical model of reality. In this
paper, we propos... |
2502.14048 | Semantic Decomposition and Selective Context Filtering -- Text
Processing Techniques for Context-Aware NLP-Based Systems | cs.CL cs.AI cs.HC | In this paper, we present two techniques for use in context-aware systems:
Semantic Decomposition, which sequentially decomposes input prompts into a
structured and hierarchal information schema in which systems can parse and
process easily, and Selective Context Filtering, which enables systems to
systematically fil... |
2502.14050 | Diversity-driven Data Selection for Language Model Tuning through Sparse
Autoencoder | cs.CL cs.AI cs.LG | Current pre-trained large language models typically need instruction tuning
to align with human preferences. However, instruction tuning data is often
quantity-saturated due to the large volume of data collection and fast model
iteration, leaving coreset data selection important but underexplored. On the
other hand, ... |
2502.14051 | RocketKV: Accelerating Long-Context LLM Inference via Two-Stage KV Cache
Compression | cs.CL cs.LG | Transformer-based Large Language Models rely critically on KV cache to
efficiently handle extended contexts during the decode phase. Yet, the size of
the KV cache grows proportionally with the input length, burdening both memory
bandwidth and capacity as decoding progresses. To address this challenge, we
present Rock... |
2502.14053 | Goggin's corrected Kalman Filter: Guarantees and Filtering Regimes | cs.IT math.IT | In this paper we revisit a non-linear filter for {\em non-Gaussian} noises
that was introduced in [1]. Goggin proved that transforming the observations by
the score function and then applying the Kalman Filter (KF) to the transformed
observations results in an asymptotically optimal filter. In the current paper,
we s... |
2502.14054 | A Low-Complexity Scheme for Multi-Message Private Information Retrieval | cs.IT math.IT | Private Information Retrieval (PIR) is a fundamental problem in the broader
fields of security and privacy. In recent years, the problem has garnered
significant attention from the research community, leading to achievability
schemes and converse results for many important PIR settings.
This paper focuses on the Mu... |
2502.14060 | New Lower Bounds for Stochastic Non-Convex Optimization through
Divergence Composition | stat.ML cs.LG math.OC | We study fundamental limits of first-order stochastic optimization in a range
of nonconvex settings, including L-smooth functions satisfying Quasar-Convexity
(QC), Quadratic Growth (QG), and Restricted Secant Inequalities (RSI). While
the convergence properties of standard algorithms are well-understood in
determinis... |
2502.14061 | EfficientPose 6D: Scalable and Efficient 6D Object Pose Estimation | cs.CV cs.AI cs.LG | In industrial applications requiring real-time feedback, such as quality
control and robotic manipulation, the demand for high-speed and accurate pose
estimation remains critical. Despite advances improving speed and accuracy in
pose estimation, finding a balance between computational efficiency and
accuracy poses si... |
2502.14063 | PedDet: Adaptive Spectral Optimization for Multimodal Pedestrian
Detection | cs.CV | Pedestrian detection in intelligent transportation systems has made
significant progress but faces two critical challenges: (1) insufficient fusion
of complementary information between visible and infrared spectra, particularly
in complex scenarios, and (2) sensitivity to illumination changes, such as
low-light or ov... |
2502.14064 | Triad: Vision Foundation Model for 3D Magnetic Resonance Imaging | cs.CV cs.AI | Vision foundation models (VFMs) are pre-trained on extensive image datasets
to learn general representations for diverse types of data. These models can
subsequently be fine-tuned for specific downstream tasks, significantly
boosting performance across a broad range of applications. However, existing
vision foundatio... |
2502.14066 | Experiment Design with Gaussian Process Regression with Applications to
Chance-Constrained Control | eess.SY cs.SY | Learning for control in repeated tasks allows for well-designed experiments
to gather the most useful data. We consider the setting in which we use a
data-driven controller that does not have access to the true system dynamics.
Rather, the controller uses inferred dynamics based on the available
information. In order... |
2502.14068 | A Racing Dataset and Baseline Model for Track Detection in Autonomous
Racing | cs.CV cs.AI eess.IV | A significant challenge in racing-related research is the lack of publicly
available datasets containing raw images with corresponding annotations for the
downstream task. In this paper, we introduce RoRaTrack, a novel dataset that
contains annotated multi-camera image data from racing scenarios for track
detection. ... |
2502.14070 | DiffExp: Efficient Exploration in Reward Fine-tuning for Text-to-Image
Diffusion Models | cs.CV cs.AI | Fine-tuning text-to-image diffusion models to maximize rewards has proven
effective for enhancing model performance. However, reward fine-tuning methods
often suffer from slow convergence due to online sample generation. Therefore,
obtaining diverse samples with strong reward signals is crucial for improving
sample e... |
2502.14074 | Investigating Non-Transitivity in LLM-as-a-Judge | cs.AI cs.CL cs.LG | Automatic evaluation methods based on large language models (LLMs) are
emerging as the standard tool for assessing the instruction-following abilities
of LLM-based agents. The most common method in this paradigm, pairwise
comparisons with a baseline model, critically depends on the assumption of
transitive preference... |
2502.14075 | Towards Vector Optimization on Low-Dimensional Vector Symbolic
Architecture | cs.LG | Vector Symbolic Architecture (VSA) is emerging in machine learning due to its
efficiency, but they are hindered by issues of hyperdimensionality and
accuracy. As a promising mitigation, the Low-Dimensional Computing (LDC) method
significantly reduces the vector dimension by ~100 times while maintaining
accuracy, by e... |
2502.14079 | Population Dynamics Control with Partial Observations | math.OC cs.LG | We study the problem of controlling population dynamics, a class of linear
dynamical systems evolving on the probability simplex, from the perspective of
online non-stochastic control. While Golowich et.al. 2024 analyzed the fully
observable setting, we focus on the more realistic, partially observable case,
where on... |
2502.14080 | Personalized Education with Generative AI and Digital Twins: VR, RAG,
and Zero-Shot Sentiment Analysis for Industry 4.0 Workforce Development | cs.CY cs.AI | The Fourth Industrial Revolution (4IR) technologies, such as cloud computing,
machine learning, and AI, have improved productivity but introduced challenges
in workforce training and reskilling. This is critical given existing workforce
shortages, especially in marginalized communities like Underrepresented
Minoritie... |
2502.14083 | Are Rules Meant to be Broken? Understanding Multilingual Moral Reasoning
as a Computational Pipeline with UniMoral | cs.CL | Moral reasoning is a complex cognitive process shaped by individual
experiences and cultural contexts and presents unique challenges for
computational analysis. While natural language processing (NLP) offers
promising tools for studying this phenomenon, current research lacks cohesion,
employing discordant datasets a... |
2502.14086 | Navigating Semantic Relations: Challenges for Language Models in
Abstract Common-Sense Reasoning | cs.CL cs.AI | Large language models (LLMs) have achieved remarkable performance in
generating human-like text and solving reasoning tasks of moderate complexity,
such as question-answering and mathematical problem-solving. However, their
capabilities in tasks requiring deeper cognitive skills, such as common-sense
understanding an... |
2502.14087 | Learning from End User Data with Shuffled Differential Privacy over
Kernel Densities | cs.LG cs.CR cs.DS | We study a setting of collecting and learning from private data distributed
across end users. In the shuffled model of differential privacy, the end users
partially protect their data locally before sharing it, and their data is also
anonymized during its collection to enhance privacy. This model has recently
become ... |
2502.14088 | Regression in EO: Are VLMs Up to the Challenge? | cs.CV | Earth Observation (EO) data encompass a vast range of remotely sensed
information, featuring multi-sensor and multi-temporal, playing an
indispensable role in understanding our planet's dynamics. Recently, Vision
Language Models (VLMs) have achieved remarkable success in perception and
reasoning tasks, bringing new i... |
2502.14090 | MambaLiteSR: Image Super-Resolution with Low-Rank Mamba using Knowledge
Distillation | eess.IV cs.CV | Generative Artificial Intelligence (AI) has gained significant attention in
recent years, revolutionizing various applications across industries. Among
these, advanced vision models for image super-resolution are in high demand,
particularly for deployment on edge devices where real-time processing is
crucial. Howeve... |
2502.14092 | Hybrid Visual Servoing of Tendon-driven Continuum Robots | cs.RO cs.CV cs.SY eess.SY | This paper introduces a novel Hybrid Visual Servoing (HVS) approach for
controlling tendon-driven continuum robots (TDCRs). The HVS system combines
Image-Based Visual Servoing (IBVS) with Deep Learning-Based Visual Servoing
(DLBVS) to overcome the limitations of each method and improve overall
performance. IBVS offer... |
2502.14094 | CND-IDS: Continual Novelty Detection for Intrusion Detection Systems | cs.CR cs.LG | Intrusion detection systems (IDS) play a crucial role in IoT and network
security by monitoring system data and alerting to suspicious activities.
Machine learning (ML) has emerged as a promising solution for IDS, offering
highly accurate intrusion detection. However, ML-IDS solutions often overlook
two critical aspe... |
2502.14095 | Retrieving Versus Understanding Extractive Evidence in Few-Shot Learning | cs.CL | A key aspect of alignment is the proper use of within-document evidence to
construct document-level decisions. We analyze the relationship between the
retrieval and interpretation of within-document evidence for large language
model in a few-shot setting. Specifically, we measure the extent to which model
prediction ... |
2502.14096 | Aligned Multi Objective Optimization | cs.LG math.OC | To date, the multi-objective optimization literature has mainly focused on
conflicting objectives, studying the Pareto front, or requiring users to
balance tradeoffs. Yet, in machine learning practice, there are many scenarios
where such conflict does not take place. Recent findings from multi-task
learning, reinforc... |
2502.14099 | Point Cloud Geometry Scalable Coding Using a Resolution and
Quality-conditioned Latents Probability Estimator | cs.CV | In the current age, users consume multimedia content in very heterogeneous
scenarios in terms of network, hardware, and display capabilities. A naive
solution to this problem is to encode multiple independent streams, each
covering a different possible requirement for the clients, with an obvious
negative impact in b... |
2502.14100 | Towards Context-Robust LLMs: A Gated Representation Fine-tuning Approach | cs.CL cs.IR | Large Language Models (LLMs) enhanced with external contexts, such as through
retrieval-augmented generation (RAG), often face challenges in handling
imperfect evidence. They tend to over-rely on external knowledge, making them
vulnerable to misleading and unhelpful contexts. To address this, we propose
the concept o... |
2502.14102 | Explainable Distributed Constraint Optimization Problems | cs.AI | The Distributed Constraint Optimization Problem (DCOP) formulation is a
powerful tool to model cooperative multi-agent problems that need to be solved
distributively. A core assumption of existing approaches is that DCOP solutions
can be easily understood, accepted, and adopted, which may not hold, as
evidenced by th... |
2502.14105 | Conformal Prediction under L\'evy-Prokhorov Distribution Shifts:
Robustness to Local and Global Perturbations | stat.ML cs.LG math.ST stat.ME stat.TH | Conformal prediction provides a powerful framework for constructing
prediction intervals with finite-sample guarantees, yet its robustness under
distribution shifts remains a significant challenge. This paper addresses this
limitation by modeling distribution shifts using L\'evy-Prokhorov (LP)
ambiguity sets, which c... |
2502.14111 | Comprehensive Review on the Control of Heat Pumps for Energy Flexibility
in Distribution Networks | eess.SY cs.SY | Decarbonization plans promote the transition to heat pumps (HPs), creating
new opportunities for their energy flexibility in demand response programs,
solar photovoltaic integration and optimization of distribution networks. This
paper reviews scheduling-based and real-time optimization methods for
controlling HPs wi... |
2502.14112 | To Stand on the Shoulders of Giants: Should We Protect Initial
Discoveries in Multi-Agent Exploration? | cs.MA | Exploring new ideas is a fundamental aspect of research and development
(R\&D), which often occurs in competitive environments. Most ideas are
subsequent, i.e. one idea today leads to more ideas tomorrow. According to one
approach, the best way to encourage exploration is by granting protection on
discoveries to the ... |
2502.14113 | Object-centric Binding in Contrastive Language-Image Pretraining | cs.CV cs.AI | Recent advances in vision language models (VLM) have been driven by
contrastive models such as CLIP, which learn to associate visual information
with their corresponding text descriptions. However, these models have
limitations in understanding complex compositional scenes involving multiple
objects and their spatial... |
2502.14114 | Zero loss guarantees and explicit minimizers for generic
overparametrized Deep Learning networks | cs.LG cs.AI math.AP math.OC stat.ML | We determine sufficient conditions for overparametrized deep learning (DL)
networks to guarantee the attainability of zero loss in the context of
supervised learning, for the $\mathcal{L}^2$ cost and {\em generic} training
data. We present an explicit construction of the zero loss minimizers without
invoking gradient... |
2502.14115 | Chasing the Timber Trail: Machine Learning to Reveal Harvest Location
Misrepresentation | cs.LG cs.CE cs.CY | Illegal logging poses a significant threat to global biodiversity, climate
stability, and depresses international prices for legal wood harvesting and
responsible forest products trade, affecting livelihoods and communities across
the globe. Stable isotope ratio analysis (SIRA) is rapidly becoming an
important tool f... |
2502.14119 | Meaning Beyond Truth Conditions: Evaluating Discourse Level
Understanding via Anaphora Accessibility | cs.CL | We present a hierarchy of natural language understanding abilities and argue
for the importance of moving beyond assessments of understanding at the lexical
and sentence levels to the discourse level. We propose the task of anaphora
accessibility as a diagnostic for assessing discourse understanding, and to
this end,... |
2502.14120 | A Supervised Machine-Learning Approach For Turboshaft Engine Dynamic
Modeling Under Real Flight Conditions | cs.LG cs.SY eess.SY | Rotorcraft engines are highly complex, nonlinear thermodynamic systems that
operate under varying environmental and flight conditions. Simulating their
dynamics is crucial for design, fault diagnostics, and deterioration control
phases, and requires robust and reliable control systems to estimate engine
performance t... |
2502.14121 | Multi-Objective Bayesian Optimization for Networked Black-Box Systems: A
Path to Greener Profits and Smarter Designs | stat.ML cs.AI cs.LG | Designing modern industrial systems requires balancing several competing
objectives, such as profitability, resilience, and sustainability, while
accounting for complex interactions between technological, economic, and
environmental factors. Multi-objective optimization (MOO) methods are commonly
used to navigate the... |
2502.14122 | Benchmarking LLMs for Political Science: A United Nations Perspective | cs.CL cs.CY cs.ET | Large Language Models (LLMs) have achieved significant advances in natural
language processing, yet their potential for high-stake political
decision-making remains largely unexplored. This paper addresses the gap by
focusing on the application of LLMs to the United Nations (UN) decision-making
process, where the sta... |
2502.14123 | Understanding SGD with Exponential Moving Average: A Case Study in
Linear Regression | cs.LG math.OC stat.ML | Exponential moving average (EMA) has recently gained significant popularity
in training modern deep learning models, especially diffusion-based generative
models. However, there have been few theoretical results explaining the
effectiveness of EMA. In this paper, to better understand EMA, we establish the
risk bound ... |
2502.14125 | Modular Prompt Learning Improves Vision-Language Models | cs.CV | Pre-trained vision-language models are able to interpret visual concepts and
language semantics. Prompt learning, a method of constructing prompts for text
encoders or image encoders, elicits the potentials of pre-trained models and
readily adapts them to new scenarios. Compared to fine-tuning, prompt learning
enable... |
2502.14127 | Which of These Best Describes Multiple Choice Evaluation with LLMs? A)
Forced B) Flawed C) Fixable D) All of the Above | cs.CL | Multiple choice question answering (MCQA) is popular for LLM evaluation due
to its simplicity and human-like testing, but we argue for its reform. We first
reveal flaws in MCQA's format, as it struggles to: 1) test
generation/subjectivity; 2) match LLM use cases; and 3) fully test knowledge.
We instead advocate for g... |
2502.14129 | GlossGau: Efficient Inverse Rendering for Glossy Surface with
Anisotropic Spherical Gaussian | cs.CV | The reconstruction of 3D objects from calibrated photographs represents a
fundamental yet intricate challenge in the domains of computer graphics and
vision. Although neural reconstruction approaches based on Neural Radiance
Fields (NeRF) have shown remarkable capabilities, their processing costs remain
substantial. ... |
2502.14131 | Gradients can train reward models: An Empirical Risk Minimization
Approach for Offline Inverse RL and Dynamic Discrete Choice Model | cs.LG cs.AI econ.EM | We study the problem of estimating Dynamic Discrete Choice (DDC) models, also
known as offline Maximum Entropy-Regularized Inverse Reinforcement Learning
(offline MaxEnt-IRL) in machine learning. The objective is to recover reward or
$Q^*$ functions that govern agent behavior from offline behavior data. In this
paper... |
2502.14132 | Can Community Notes Replace Professional Fact-Checkers? | cs.CL cs.AI | Two commonly-employed strategies to combat the rise of misinformation on
social media are (i) fact-checking by professional organisations and (ii)
community moderation by platform users. Policy changes by Twitter/X and, more
recently, Meta, signal a shift away from partnerships with fact-checking
organisations and to... |
2502.14133 | Self-Regularization with Latent Space Explanations for Controllable
LLM-based Classification | cs.CL | Modern text classification methods heavily rely on contextual embeddings from
large language models (LLMs). Compared to human-engineered features, these
embeddings provide automatic and effective representations for classification
model training. However, they also introduce a challenge: we lose the ability
to manual... |
2502.14135 | Cluster Analysis and Concept Drift Detection in Malware | cs.LG cs.CR | Concept drift refers to gradual or sudden changes in the properties of data
that affect the accuracy of machine learning models. In this paper, we address
the problem of concept drift detection in the malware domain. Specifically, we
propose and analyze a clustering-based approach to detecting concept drift.
Using a ... |
2502.14137 | Collaborative Retrieval for Large Language Model-based Conversational
Recommender Systems | cs.IR | Conversational recommender systems (CRS) aim to provide personalized
recommendations via interactive dialogues with users. While large language
models (LLMs) enhance CRS with their superior understanding of context-aware
user preferences, they typically struggle to leverage behavioral data, which
have proven to be im... |
2502.14140 | ModSkill: Physical Character Skill Modularization | cs.CV cs.GR cs.RO | Human motion is highly diverse and dynamic, posing challenges for imitation
learning algorithms that aim to generalize motor skills for controlling
simulated characters. Previous methods typically rely on a universal full-body
controller for tracking reference motion (tracking-based model) or a unified
full-body skil... |
2502.14142 | Token Adaptation via Side Graph Convolution for Temporally and Spatially
Efficient Fine-tuning of 3D Point Cloud Transformers | cs.CV | Parameter-efficient fine-tuning (PEFT) of pre-trained 3D point cloud
Transformers has emerged as a promising technique for 3D point cloud analysis.
While existing PEFT methods attempt to minimize the number of tunable
parameters, they still suffer from high temporal and spatial computational
costs during fine-tuning.... |
2502.14143 | Multi-Agent Risks from Advanced AI | cs.MA cs.AI cs.CY cs.ET cs.LG | The rapid development of advanced AI agents and the imminent deployment of
many instances of these agents will give rise to multi-agent systems of
unprecedented complexity. These systems pose novel and under-explored risks. In
this report, we provide a structured taxonomy of these risks by identifying
three key failu... |
2502.14144 | UM_FHS at TREC 2024 PLABA: Exploration of Fine-tuning and AI agent
approach for plain language adaptations of biomedical text | cs.CL | This paper describes our submissions to the TREC 2024 PLABA track with the
aim to simplify biomedical abstracts for a K8-level audience (13-14 years old
students). We tested three approaches using OpenAI's gpt-4o and gpt-4o-mini
models: baseline prompt engineering, a two-AI agent approach, and fine-tuning.
Adaptation... |
2502.14145 | LLM-Enhanced Dialogue Management for Full-Duplex Spoken Dialogue Systems | cs.CL eess.AS | Achieving full-duplex communication in spoken dialogue systems (SDS) requires
real-time coordination between listening, speaking, and thinking. This paper
proposes a semantic voice activity detection (VAD) module as a dialogue manager
(DM) to efficiently manage turn-taking in full-duplex SDS. Implemented as a
lightwe... |
2502.14146 | Efficient and Optimal Policy Gradient Algorithm for Corrupted
Multi-armed Bandits | cs.LG | In this paper, we consider the stochastic multi-armed bandits problem with
adversarial corruptions, where the random rewards of the arms are partially
modified by an adversary to fool the algorithm. We apply the policy gradient
algorithm SAMBA to this setting, and show that it is computationally efficient,
and achiev... |
2502.14147 | Learning the P2D Model for Lithium-Ion Batteries with SOH Detection | cs.LG physics.chem-ph | Lithium ion batteries are widely used in many applications. Battery
management systems control their optimal use and charging and predict when the
battery will cease to deliver the required output on a planned duty or driving
cycle. Such systems use a simulation of a mathematical model of battery
performance. These m... |
2502.14149 | PitVQA++: Vector Matrix-Low-Rank Adaptation for Open-Ended Visual
Question Answering in Pituitary Surgery | cs.CV cs.AI | Vision-Language Models (VLMs) in visual question answering (VQA) offer a
unique opportunity to enhance intra-operative decision-making, promote
intuitive interactions, and significantly advancing surgical education.
However, the development of VLMs for surgical VQA is challenging due to limited
datasets and the risk ... |
2502.14150 | Risk-Sensitive Security-Constrained Economic Dispatch: Pricing and
Algorithm Design | eess.SY cs.SY econ.TH | We propose a risk-sensitive security-constrained economic dispatch (R-SCED)
formulation capturing the tradeoff between dispatch cost and resilience against
potential line failures, where risk is modeled via the conditional value at
risk (CVaR). In the context of our formulation, we analyze revenue adequacy and
side p... |
2502.14155 | Giving AI Personalities Leads to More Human-Like Reasoning | cs.AI cs.CL cs.CY | In computational cognitive modeling, capturing the full spectrum of human
judgment and decision-making processes, beyond just optimal behaviors, is a
significant challenge. This study explores whether Large Language Models (LLMs)
can emulate the breadth of human reasoning by predicting both intuitive, fast
System 1 a... |
2502.14156 | Mixed Signals: A Diverse Point Cloud Dataset for Heterogeneous LiDAR V2X
Collaboration | cs.CV | Vehicle-to-everything (V2X) collaborative perception has emerged as a
promising solution to address the limitations of single-vehicle perception
systems. However, existing V2X datasets are limited in scope, diversity, and
quality. To address these gaps, we present Mixed Signals, a comprehensive V2X
dataset featuring ... |
2502.14158 | Dual-level Mixup for Graph Few-shot Learning with Fewer Tasks | cs.LG cs.SI | Graph neural networks have been demonstrated as a powerful paradigm for
effectively learning graph-structured data on the web and mining content from
it.Current leading graph models require a large number of labeled samples for
training, which unavoidably leads to overfitting in few-shot scenarios. Recent
research ha... |
2502.14160 | Efficient Inverse Multiagent Learning | cs.GT cs.AI cs.LG econ.TH | In this paper, we study inverse game theory (resp. inverse multiagent
learning) in which the goal is to find parameters of a game's payoff functions
for which the expected (resp. sampled) behavior is an equilibrium. We formulate
these problems as generative-adversarial (i.e., min-max) optimization problems,
for which... |
2502.14166 | Prediction-Powered Adaptive Shrinkage Estimation | stat.ML cs.LG stat.ME | Prediction-Powered Inference (PPI) is a powerful framework for enhancing
statistical estimates by combining limited gold-standard data with machine
learning (ML) predictions. While prior work has demonstrated PPI's benefits for
individual statistical tasks, modern applications require answering numerous
parallel stat... |
2502.14168 | Deep learning based infrared small object segmentation: Challenges and
future directions | cs.CV | Infrared sensing is a core method for supporting unmanned systems, such as
autonomous vehicles and drones. Recently, infrared sensors have been widely
deployed on mobile and stationary platforms for detection and classification of
objects from long distances and in wide field of views. Given its success in
the vision... |
2502.14170 | Blockchain-based Framework for Scalable and Incentivized Federated
Learning | cs.LG cs.DC | Federated Learning (FL) enables collaborative model training without sharing
raw data, preserving privacy while harnessing distributed datasets. However,
traditional FL systems often rely on centralized aggregating mechanisms,
introducing trust issues, single points of failure, and limited mechanisms for
incentivizin... |
2502.14171 | Enhancing Conversational Agents with Theory of Mind: Aligning Beliefs,
Desires, and Intentions for Human-Like Interaction | cs.CL | Natural language interaction with agentic Artificial Intelligence (AI),
driven by Large Language Models (LLMs), is expected to remain a dominant
paradigm in the near future. While humans instinctively align their
communication with mental states -- an ability known as Theory of Mind (ToM),
current LLM powered systems... |
2502.14172 | Finite Sample Analysis of Distributional TD Learning with Linear
Function Approximation | stat.ML cs.LG | In this paper, we investigate the finite-sample statistical rates of
distributional temporal difference (TD) learning with linear function
approximation. The aim of distributional TD learning is to estimate the return
distribution of a discounted Markov decision process for a given policy {\pi}.
Prior works on statis... |
2502.14174 | Weighted Low-rank Approximation via Stochastic Gradient Descent on
Manifolds | math.OC cs.AI cs.LG stat.ML | We solve a regularized weighted low-rank approximation problem by a
stochastic gradient descent on a manifold. To guarantee the convergence of our
stochastic gradient descent, we establish a convergence theorem on manifolds
for retraction-based stochastic gradient descents admitting confinements. On
sample data from ... |
2502.14176 | A modal logic translation of the AGM axioms for belief revision | cs.LO cs.AI | Building on the analysis of Bonanno (Artificial Intelligence, 2025) we
introduce a simple modal logic containing three modal operators: a unimodal
belief operator, a bimodal conditional operator and the unimodal global
operator. For each AGM axiom for belief revision, we provide a corresponding
modal axiom. The corre... |
2502.14177 | InstaSHAP: Interpretable Additive Models Explain Shapley Values
Instantly | cs.LG stat.ML | In recent years, the Shapley value and SHAP explanations have emerged as one
of the most dominant paradigms for providing post-hoc explanations of black-box
models. Despite their well-founded theoretical properties, many recent works
have focused on the limitations in both their computational efficiency and
their rep... |
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