Search is not available for this dataset
id string | submitter string | authors string | title string | comments string | journal-ref string | doi string | report-no string | categories string | license string | abstract string | versions list | update_date timestamp[s] | authors_parsed list | prompt string |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2504.07887 | Riccardo Cantini | Riccardo Cantini, Alessio Orsino, Massimo Ruggiero, Domenico Talia | Benchmarking Adversarial Robustness to Bias Elicitation in Large
Language Models: Scalable Automated Assessment with LLM-as-a-Judge | null | null | null | null | cs.CL cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Large Language Models (LLMs) have revolutionized artificial intelligence,
driving advancements in machine translation, summarization, and conversational
agents. However, their increasing integration into critical societal domains
has raised concerns about embedded biases, which can perpetuate stereotypes and
compromi... | [
{
"version": "v1",
"created": "Thu, 10 Apr 2025 16:00:59 GMT"
}
] | 2025-04-11T00:00:00 | [
[
"Cantini",
"Riccardo",
""
],
[
"Orsino",
"Alessio",
""
],
[
"Ruggiero",
"Massimo",
""
],
[
"Talia",
"Domenico",
""
]
] | TITLE: Benchmarking Adversarial Robustness to Bias Elicitation in Large
Language Models: Scalable Automated Assessment with LLM-as-a-Judge
ABSTRACT: Large Language Models (LLMs) have revolutionized artificial intelligence,
driving advancements in machine translation, summarization, and conversational
agents. Howe... |
2504.07901 | Hongcheng Guo | Hongcheng Guo, Fei Zhao, Shaosheng Cao, Xinze Lyu, Ziyan Liu, Yue
Wang, Boyang Wang, Zhoujun Li, Chonggang Lu, Zhe Xu, Yao Hu | Redefining Machine Translation on Social Network Services with Large
Language Models | null | null | null | null | cs.CL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The globalization of social interactions has heightened the need for machine
translation (MT) on Social Network Services (SNS), yet traditional models
struggle with culturally nuanced content like memes, slang, and pop culture
references. While large language models (LLMs) have advanced general-purpose
translation, t... | [
{
"version": "v1",
"created": "Thu, 10 Apr 2025 16:24:28 GMT"
}
] | 2025-04-11T00:00:00 | [
[
"Guo",
"Hongcheng",
""
],
[
"Zhao",
"Fei",
""
],
[
"Cao",
"Shaosheng",
""
],
[
"Lyu",
"Xinze",
""
],
[
"Liu",
"Ziyan",
""
],
[
"Wang",
"Yue",
""
],
[
"Wang",
"Boyang",
""
],
[
"Li",
"Zhoujun",
... | TITLE: Redefining Machine Translation on Social Network Services with Large
Language Models
ABSTRACT: The globalization of social interactions has heightened the need for machine
translation (MT) on Social Network Services (SNS), yet traditional models
struggle with culturally nuanced content like memes, slang, a... |
2504.07905 | Iat Hin Tam | Frederick Iat-Hin Tam, Fabien Augsburger, Tom Beucler | From Winter Storm Thermodynamics to Wind Gust Extremes: Discovering
Interpretable Equations from Data | 9 pages, 4 figures | null | null | null | physics.ao-ph stat.AP | http://creativecommons.org/licenses/by/4.0/ | Reliably identifying and understanding temporal precursors to extreme wind
gusts is crucial for early warning and mitigation. This study proposes a simple
data-driven approach to extract key predictors from a dataset of historical
extreme European winter windstorms and derive simple equations linking these
precursors... | [
{
"version": "v1",
"created": "Thu, 10 Apr 2025 16:28:22 GMT"
}
] | 2025-04-11T00:00:00 | [
[
"Tam",
"Frederick Iat-Hin",
""
],
[
"Augsburger",
"Fabien",
""
],
[
"Beucler",
"Tom",
""
]
] | TITLE: From Winter Storm Thermodynamics to Wind Gust Extremes: Discovering
Interpretable Equations from Data
ABSTRACT: Reliably identifying and understanding temporal precursors to extreme wind
gusts is crucial for early warning and mitigation. This study proposes a simple
data-driven approach to extract key pred... |
2504.07912 | Rosie Zhao | Rosie Zhao, Alexandru Meterez, Sham Kakade, Cengiz Pehlevan, Samy
Jelassi, Eran Malach | Echo Chamber: RL Post-training Amplifies Behaviors Learned in
Pretraining | null | null | null | null | cs.LG | http://creativecommons.org/licenses/by/4.0/ | Reinforcement learning (RL)-based fine-tuning has become a crucial step in
post-training language models for advanced mathematical reasoning and coding.
Following the success of frontier reasoning models, recent work has
demonstrated that RL fine-tuning consistently improves performance, even in
smaller-scale models;... | [
{
"version": "v1",
"created": "Thu, 10 Apr 2025 17:15:53 GMT"
}
] | 2025-04-11T00:00:00 | [
[
"Zhao",
"Rosie",
""
],
[
"Meterez",
"Alexandru",
""
],
[
"Kakade",
"Sham",
""
],
[
"Pehlevan",
"Cengiz",
""
],
[
"Jelassi",
"Samy",
""
],
[
"Malach",
"Eran",
""
]
] | TITLE: Echo Chamber: RL Post-training Amplifies Behaviors Learned in
Pretraining
ABSTRACT: Reinforcement learning (RL)-based fine-tuning has become a crucial step in
post-training language models for advanced mathematical reasoning and coding.
Following the success of frontier reasoning models, recent work has
de... |
2504.07916 | Guanyi Mou | Wen Ge and Guanyi Mou, Emmanuel O. Agu, Kyumin Lee | Semantically Encoding Activity Labels for Context-Aware Human Activity
Recognition | Percom 2025 | null | null | null | cs.LG | http://creativecommons.org/licenses/by/4.0/ | Prior work has primarily formulated CA-HAR as a multi-label classification
problem, where model inputs are time-series sensor data and target labels are
binary encodings representing whether a given activity or context occurs. These
CA-HAR methods either predicted each label independently or manually imposed
relation... | [
{
"version": "v1",
"created": "Thu, 10 Apr 2025 17:30:07 GMT"
}
] | 2025-04-11T00:00:00 | [
[
"Ge",
"Wen",
""
],
[
"Mou",
"Guanyi",
""
],
[
"Agu",
"Emmanuel O.",
""
],
[
"Lee",
"Kyumin",
""
]
] | TITLE: Semantically Encoding Activity Labels for Context-Aware Human Activity
Recognition
ABSTRACT: Prior work has primarily formulated CA-HAR as a multi-label classification
problem, where model inputs are time-series sensor data and target labels are
binary encodings representing whether a given activity or con... |
2504.07927 | Yongyi Shi | Yongyi Shi, Ge Wang | Zero-Shot Low-dose CT Denoising via Sinogram Flicking | 4 pages, 4 figures | null | null | null | eess.IV cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Many low-dose CT imaging methods rely on supervised learning, which requires
a large number of paired noisy and clean images. However, obtaining paired
images in clinical practice is challenging. To address this issue, zero-shot
self-supervised methods train denoising networks using only the information
within a sing... | [
{
"version": "v1",
"created": "Thu, 10 Apr 2025 17:42:01 GMT"
}
] | 2025-04-11T00:00:00 | [
[
"Shi",
"Yongyi",
""
],
[
"Wang",
"Ge",
""
]
] | TITLE: Zero-Shot Low-dose CT Denoising via Sinogram Flicking
ABSTRACT: Many low-dose CT imaging methods rely on supervised learning, which requires
a large number of paired noisy and clean images. However, obtaining paired
images in clinical practice is challenging. To address this issue, zero-shot
self-supervised ... |
2504.07934 | Xiyao Wang | Xiyao Wang, Zhengyuan Yang, Chao Feng, Hongjin Lu, Linjie Li,
Chung-Ching Lin, Kevin Lin, Furong Huang, Lijuan Wang | SoTA with Less: MCTS-Guided Sample Selection for Data-Efficient Visual
Reasoning Self-Improvement | 21 pages, 5 figures | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we present an effective method to enhance visual reasoning
with significantly fewer training samples, relying purely on self-improvement
with no knowledge distillation. Our key insight is that the difficulty of
training data during reinforcement fine-tuning (RFT) is critical. Appropriately
challenging ... | [
{
"version": "v1",
"created": "Thu, 10 Apr 2025 17:49:05 GMT"
}
] | 2025-04-11T00:00:00 | [
[
"Wang",
"Xiyao",
""
],
[
"Yang",
"Zhengyuan",
""
],
[
"Feng",
"Chao",
""
],
[
"Lu",
"Hongjin",
""
],
[
"Li",
"Linjie",
""
],
[
"Lin",
"Chung-Ching",
""
],
[
"Lin",
"Kevin",
""
],
[
"Huang",
"Fur... | TITLE: SoTA with Less: MCTS-Guided Sample Selection for Data-Efficient Visual
Reasoning Self-Improvement
ABSTRACT: In this paper, we present an effective method to enhance visual reasoning
with significantly fewer training samples, relying purely on self-improvement
with no knowledge distillation. Our key insight... |
2504.07936 | Jordi Linares-Pellicer | Jordi Linares-Pellicer, Juan Izquierdo-Domenech, Isabel Ferri-Molla,
Carlos Aliaga-Torro | We Are All Creators: Generative AI, Collective Knowledge, and the Path
Towards Human-AI Synergy | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Generative AI presents a profound challenge to traditional notions of human
uniqueness, particularly in creativity. Fueled by neural network based
foundation models, these systems demonstrate remarkable content generation
capabilities, sparking intense debates about authorship, copyright, and
intelligence itself. Thi... | [
{
"version": "v1",
"created": "Thu, 10 Apr 2025 17:50:17 GMT"
}
] | 2025-04-11T00:00:00 | [
[
"Linares-Pellicer",
"Jordi",
""
],
[
"Izquierdo-Domenech",
"Juan",
""
],
[
"Ferri-Molla",
"Isabel",
""
],
[
"Aliaga-Torro",
"Carlos",
""
]
] | TITLE: We Are All Creators: Generative AI, Collective Knowledge, and the Path
Towards Human-AI Synergy
ABSTRACT: Generative AI presents a profound challenge to traditional notions of human
uniqueness, particularly in creativity. Fueled by neural network based
foundation models, these systems demonstrate remarkabl... |
2504.07939 | Artem Bazhenov | Artem Bazhenov, Sergei Satsevich, Sergei Egorov, Farit Khabibullin,
Dzmitry Tsetserukou | Echo: An Open-Source, Low-Cost Teleoperation System with Force Feedback
for Dataset Collection in Robot Learning | null | null | null | null | cs.RO | http://creativecommons.org/licenses/by/4.0/ | In this article, we propose Echo, a novel joint-matching teleoperation system
designed to enhance the collection of datasets for manual and bimanual tasks.
Our system is specifically tailored for controlling the UR manipulator and
features a custom controller with force feedback and adjustable sensitivity
modes, enab... | [
{
"version": "v1",
"created": "Thu, 10 Apr 2025 17:51:37 GMT"
}
] | 2025-04-11T00:00:00 | [
[
"Bazhenov",
"Artem",
""
],
[
"Satsevich",
"Sergei",
""
],
[
"Egorov",
"Sergei",
""
],
[
"Khabibullin",
"Farit",
""
],
[
"Tsetserukou",
"Dzmitry",
""
]
] | TITLE: Echo: An Open-Source, Low-Cost Teleoperation System with Force Feedback
for Dataset Collection in Robot Learning
ABSTRACT: In this article, we propose Echo, a novel joint-matching teleoperation system
designed to enhance the collection of datasets for manual and bimanual tasks.
Our system is specifically t... |
2504.07943 | Yunhan Yang | Yunhan Yang, Yuan-Chen Guo, Yukun Huang, Zi-Xin Zou, Zhipeng Yu,
Yangguang Li, Yan-Pei Cao, Xihui Liu | HoloPart: Generative 3D Part Amodal Segmentation | Project Page: https://vast-ai-research.github.io/HoloPart | null | null | null | cs.CV | http://creativecommons.org/licenses/by/4.0/ | 3D part amodal segmentation--decomposing a 3D shape into complete,
semantically meaningful parts, even when occluded--is a challenging but crucial
task for 3D content creation and understanding. Existing 3D part segmentation
methods only identify visible surface patches, limiting their utility. Inspired
by 2D amodal ... | [
{
"version": "v1",
"created": "Thu, 10 Apr 2025 17:53:31 GMT"
}
] | 2025-04-11T00:00:00 | [
[
"Yang",
"Yunhan",
""
],
[
"Guo",
"Yuan-Chen",
""
],
[
"Huang",
"Yukun",
""
],
[
"Zou",
"Zi-Xin",
""
],
[
"Yu",
"Zhipeng",
""
],
[
"Li",
"Yangguang",
""
],
[
"Cao",
"Yan-Pei",
""
],
[
"Liu",
"Xih... | TITLE: HoloPart: Generative 3D Part Amodal Segmentation
ABSTRACT: 3D part amodal segmentation--decomposing a 3D shape into complete,
semantically meaningful parts, even when occluded--is a challenging but crucial
task for 3D content creation and understanding. Existing 3D part segmentation
methods only identify vis... |
2504.07945 | Hao Yu | Hao Yu, Rupayan Mallick, Margrit Betke, Sarah Adel Bargal | GenEAva: Generating Cartoon Avatars with Fine-Grained Facial Expressions
from Realistic Diffusion-based Faces | null | null | null | null | cs.CV cs.AI | http://creativecommons.org/licenses/by/4.0/ | Cartoon avatars have been widely used in various applications, including
social media, online tutoring, and gaming. However, existing cartoon avatar
datasets and generation methods struggle to present highly expressive avatars
with fine-grained facial expressions and are often inspired from real-world
identities, rai... | [
{
"version": "v1",
"created": "Thu, 10 Apr 2025 17:54:02 GMT"
}
] | 2025-04-11T00:00:00 | [
[
"Yu",
"Hao",
""
],
[
"Mallick",
"Rupayan",
""
],
[
"Betke",
"Margrit",
""
],
[
"Bargal",
"Sarah Adel",
""
]
] | TITLE: GenEAva: Generating Cartoon Avatars with Fine-Grained Facial Expressions
from Realistic Diffusion-based Faces
ABSTRACT: Cartoon avatars have been widely used in various applications, including
social media, online tutoring, and gaming. However, existing cartoon avatar
datasets and generation methods strugg... |
2504.07948 | Jean-Philip Piquemal | Anouar Benali, Thomas Pl\'e, Olivier Adjoua, Valay Agarawal, Thomas
Applencourt, Marharyta Blazhynska, Raymond Clay III, Kevin Gasperich, Khalid
Hossain, Jeongnim Kim, Christopher Knight, Jaron T. Krogel, Yvon Maday,
Maxime Maria, Mathieu Montes, Ye Luo, Evgeny Posenitskiy, Corentin Villot,
Venkat Vishwanath, L... | Pushing the Accuracy Limit of Foundation Neural Network Models with
Quantum Monte Carlo Forces and Path Integrals | null | null | null | null | physics.chem-ph | http://creativecommons.org/licenses/by/4.0/ | We propose an end-to-end integrated strategy for the production of highly
accurate quantum chemistry (QC) synthetic datasets aimed at deriving atomistic
Foundation Machine Learning (ML) Models. We first present a GPU-accelerated QC
database generation Exascale protocol able to produce the required energies and
forces... | [
{
"version": "v1",
"created": "Thu, 10 Apr 2025 17:55:09 GMT"
}
] | 2025-04-11T00:00:00 | [
[
"Benali",
"Anouar",
""
],
[
"Plé",
"Thomas",
""
],
[
"Adjoua",
"Olivier",
""
],
[
"Agarawal",
"Valay",
""
],
[
"Applencourt",
"Thomas",
""
],
[
"Blazhynska",
"Marharyta",
""
],
[
"Clay",
"Raymond",
"III"
... | TITLE: Pushing the Accuracy Limit of Foundation Neural Network Models with
Quantum Monte Carlo Forces and Path Integrals
ABSTRACT: We propose an end-to-end integrated strategy for the production of highly
accurate quantum chemistry (QC) synthetic datasets aimed at deriving atomistic
Foundation Machine Learning (M... |
2504.07955 | Yuanhong Yu | Yuanhong Yu, Xingyi He, Chen Zhao, Junhao Yu, Jiaqi Yang, Ruizhen Hu,
Yujun Shen, Xing Zhu, Xiaowei Zhou, Sida Peng | BoxDreamer: Dreaming Box Corners for Generalizable Object Pose
Estimation | Project page: https://zju3dv.github.io/boxdreamer | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper presents a generalizable RGB-based approach for object pose
estimation, specifically designed to address challenges in sparse-view
settings. While existing methods can estimate the poses of unseen objects,
their generalization ability remains limited in scenarios involving occlusions
and sparse reference v... | [
{
"version": "v1",
"created": "Thu, 10 Apr 2025 17:58:35 GMT"
}
] | 2025-04-11T00:00:00 | [
[
"Yu",
"Yuanhong",
""
],
[
"He",
"Xingyi",
""
],
[
"Zhao",
"Chen",
""
],
[
"Yu",
"Junhao",
""
],
[
"Yang",
"Jiaqi",
""
],
[
"Hu",
"Ruizhen",
""
],
[
"Shen",
"Yujun",
""
],
[
"Zhu",
"Xing",
""... | TITLE: BoxDreamer: Dreaming Box Corners for Generalizable Object Pose
Estimation
ABSTRACT: This paper presents a generalizable RGB-based approach for object pose
estimation, specifically designed to address challenges in sparse-view
settings. While existing methods can estimate the poses of unseen objects,
their ... |
2504.07959 | Dongyoung Kim | Dongyoung Kim, Mahmoud Afifi, Dongyun Kim, Michael S. Brown, Seon Joo
Kim | CCMNet: Leveraging Calibrated Color Correction Matrices for Cross-Camera
Color Constancy | null | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Computational color constancy, or white balancing, is a key module in a
camera's image signal processor (ISP) that corrects color casts from scene
lighting. Because this operation occurs in the camera-specific raw color space,
white balance algorithms must adapt to different cameras. This paper introduces
a learning-... | [
{
"version": "v1",
"created": "Thu, 10 Apr 2025 17:59:31 GMT"
}
] | 2025-04-11T00:00:00 | [
[
"Kim",
"Dongyoung",
""
],
[
"Afifi",
"Mahmoud",
""
],
[
"Kim",
"Dongyun",
""
],
[
"Brown",
"Michael S.",
""
],
[
"Kim",
"Seon Joo",
""
]
] | TITLE: CCMNet: Leveraging Calibrated Color Correction Matrices for Cross-Camera
Color Constancy
ABSTRACT: Computational color constancy, or white balancing, is a key module in a
camera's image signal processor (ISP) that corrects color casts from scene
lighting. Because this operation occurs in the camera-specifi... |
2504.07960 | Zhongyu Li | Zhong-Yu Li, Ruoyi Du, Juncheng Yan, Le Zhuo, Zhen Li, Peng Gao,
Zhanyu Ma, Ming-Ming Cheng | VisualCloze: A Universal Image Generation Framework via Visual
In-Context Learning | Project page: https://visualcloze.github.io/ | null | null | null | cs.CV | http://creativecommons.org/licenses/by/4.0/ | Recent progress in diffusion models significantly advances various image
generation tasks. However, the current mainstream approach remains focused on
building task-specific models, which have limited efficiency when supporting a
wide range of different needs. While universal models attempt to address this
limitation... | [
{
"version": "v1",
"created": "Thu, 10 Apr 2025 17:59:42 GMT"
}
] | 2025-04-11T00:00:00 | [
[
"Li",
"Zhong-Yu",
""
],
[
"Du",
"Ruoyi",
""
],
[
"Yan",
"Juncheng",
""
],
[
"Zhuo",
"Le",
""
],
[
"Li",
"Zhen",
""
],
[
"Gao",
"Peng",
""
],
[
"Ma",
"Zhanyu",
""
],
[
"Cheng",
"Ming-Ming",
"... | TITLE: VisualCloze: A Universal Image Generation Framework via Visual
In-Context Learning
ABSTRACT: Recent progress in diffusion models significantly advances various image
generation tasks. However, the current mainstream approach remains focused on
building task-specific models, which have limited efficiency wh... |
2304.04884 | Jie Zhang | Jie Zhang, Minghui Nie, Changqing Zou, Jian Liu, Ligang Liu and Junjie
Cao | PointNorm-Net: Self-Supervised Normal Prediction of 3D Point Clouds via
Multi-Modal Distribution Estimation | null | null | null | null | cs.CV | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Although supervised deep normal estimators have recently shown impressive
results on synthetic benchmarks, their performance deteriorates significantly
in real-world scenarios due to the domain gap between synthetic and real data.
Building high-quality real training data to boost those supervised methods is
not trivi... | [
{
"version": "v1",
"created": "Mon, 10 Apr 2023 22:11:13 GMT"
},
{
"version": "v2",
"created": "Wed, 9 Apr 2025 11:21:48 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Zhang",
"Jie",
""
],
[
"Nie",
"Minghui",
""
],
[
"Zou",
"Changqing",
""
],
[
"Liu",
"Jian",
""
],
[
"Liu",
"Ligang",
""
],
[
"Cao",
"Junjie",
""
]
] | TITLE: PointNorm-Net: Self-Supervised Normal Prediction of 3D Point Clouds via
Multi-Modal Distribution Estimation
ABSTRACT: Although supervised deep normal estimators have recently shown impressive
results on synthetic benchmarks, their performance deteriorates significantly
in real-world scenarios due to the do... |
2304.14765 | Maruf Ahmed Dhali | Andrei Voinea, Robin Kock, Maruf A. Dhali | LostPaw: Finding Lost Pets using a Contrastive Learning-based
Transformer with Visual Input | 7 Pages, 7 figures | In Proceedings of the 14th International Conference on Pattern
Recognition Applications and Methods ICPRAM - Volume 1, 757-763, 2025 ,
Porto, Portugal | 10.5220/0013261600003905 | null | cs.CV cs.AI | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Losing pets can be highly distressing for pet owners, and finding a lost pet
is often challenging and time-consuming. An artificial intelligence-based
application can significantly improve the speed and accuracy of finding lost
pets. To facilitate such an application, this study introduces a contrastive
neural networ... | [
{
"version": "v1",
"created": "Fri, 28 Apr 2023 11:23:44 GMT"
},
{
"version": "v2",
"created": "Wed, 9 Apr 2025 11:17:26 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Voinea",
"Andrei",
""
],
[
"Kock",
"Robin",
""
],
[
"Dhali",
"Maruf A.",
""
]
] | TITLE: LostPaw: Finding Lost Pets using a Contrastive Learning-based
Transformer with Visual Input
ABSTRACT: Losing pets can be highly distressing for pet owners, and finding a lost pet
is often challenging and time-consuming. An artificial intelligence-based
application can significantly improve the speed and ac... |
2305.09958 | Haoyu Liu | Haoyu Liu, Ningyi Liao, Siqiang Luo | SIGMA: An Efficient Heterophilous Graph Neural Network with Fast Global
Aggregation | Acceptted to ICDE 2025 | null | null | null | cs.LG cs.SI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Graph neural networks (GNNs) realize great success in graph learning but
suffer from performance loss when meeting heterophily, i.e. neighboring nodes
are dissimilar, due to their local and uniform aggregation. Existing attempts
of heterophilous GNNs incorporate long-range or global aggregations to
distinguish nodes ... | [
{
"version": "v1",
"created": "Wed, 17 May 2023 05:35:49 GMT"
},
{
"version": "v2",
"created": "Mon, 5 Aug 2024 10:24:09 GMT"
},
{
"version": "v3",
"created": "Tue, 6 Aug 2024 02:32:05 GMT"
},
{
"version": "v4",
"created": "Wed, 9 Apr 2025 07:19:32 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Liu",
"Haoyu",
""
],
[
"Liao",
"Ningyi",
""
],
[
"Luo",
"Siqiang",
""
]
] | TITLE: SIGMA: An Efficient Heterophilous Graph Neural Network with Fast Global
Aggregation
ABSTRACT: Graph neural networks (GNNs) realize great success in graph learning but
suffer from performance loss when meeting heterophily, i.e. neighboring nodes
are dissimilar, due to their local and uniform aggregation. Ex... |
2305.18450 | Qin Xie | Qin Xie, Qinghua Zhang, Shuyin Xia, Fan Zhao, Chengying Wu, Guoyin
Wang and Weiping Ding | GBG++: A Fast and Stable Granular Ball Generation Method for
Classification | null | null | null | null | cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Granular ball computing (GBC), as an efficient, robust, and scalable learning
method, has become a popular research topic of granular computing. GBC includes
two stages: granular ball generation (GBG) and multi-granularity learning based
on the granular ball (GB). However, the stability and efficiency of existing
GBG... | [
{
"version": "v1",
"created": "Mon, 29 May 2023 04:00:19 GMT"
},
{
"version": "v2",
"created": "Mon, 13 Nov 2023 15:09:49 GMT"
},
{
"version": "v3",
"created": "Wed, 9 Apr 2025 02:25:03 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Xie",
"Qin",
""
],
[
"Zhang",
"Qinghua",
""
],
[
"Xia",
"Shuyin",
""
],
[
"Zhao",
"Fan",
""
],
[
"Wu",
"Chengying",
""
],
[
"Wang",
"Guoyin",
""
],
[
"Ding",
"Weiping",
""
]
] | TITLE: GBG++: A Fast and Stable Granular Ball Generation Method for
Classification
ABSTRACT: Granular ball computing (GBC), as an efficient, robust, and scalable learning
method, has become a popular research topic of granular computing. GBC includes
two stages: granular ball generation (GBG) and multi-granularit... |
2309.02583 | Md Ferdous Alam | Md Ferdous Alam, Yi Wang, Chin-Yi Cheng, Jieliang Luo | Representation Learning for Sequential Volumetric Design Tasks | 12 pages, 12 figures | null | 10.1115/1.4066686 | null | cs.LG cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Volumetric design, also called massing design, is the first and critical step
in professional building design which is sequential in nature. As the
volumetric design process requires careful design decisions and iterative
adjustments, the underlying sequential design process encodes valuable
information for designers... | [
{
"version": "v1",
"created": "Tue, 5 Sep 2023 21:21:06 GMT"
},
{
"version": "v2",
"created": "Tue, 24 Sep 2024 17:28:47 GMT"
},
{
"version": "v3",
"created": "Mon, 2 Dec 2024 22:33:40 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Alam",
"Md Ferdous",
""
],
[
"Wang",
"Yi",
""
],
[
"Cheng",
"Chin-Yi",
""
],
[
"Luo",
"Jieliang",
""
]
] | TITLE: Representation Learning for Sequential Volumetric Design Tasks
ABSTRACT: Volumetric design, also called massing design, is the first and critical step
in professional building design which is sequential in nature. As the
volumetric design process requires careful design decisions and iterative
adjustments, t... |
2310.01038 | Jiahao Wu | Jiahao Wu and Wenqi Fan and Jingfan Chen and Shengcai Liu and Qijiong
Liu and Rui He and Qing Li and Ke Tang | Dataset Condensation for Recommendation | Accepted by IEEE TKDE. Previously titled as "Condensing Pre-augmented
Recommendation Data via Lightweight Policy Gradient Estimation" | null | null | null | cs.IR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Training recommendation models on large datasets requires significant time
and resources. It is desired to construct concise yet informative datasets for
efficient training. Recent advances in dataset condensation show promise in
addressing this problem by synthesizing small datasets. However, applying
existing metho... | [
{
"version": "v1",
"created": "Mon, 2 Oct 2023 09:30:11 GMT"
},
{
"version": "v2",
"created": "Thu, 17 Oct 2024 18:35:41 GMT"
},
{
"version": "v3",
"created": "Wed, 9 Apr 2025 07:41:22 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Wu",
"Jiahao",
""
],
[
"Fan",
"Wenqi",
""
],
[
"Chen",
"Jingfan",
""
],
[
"Liu",
"Shengcai",
""
],
[
"Liu",
"Qijiong",
""
],
[
"He",
"Rui",
""
],
[
"Li",
"Qing",
""
],
[
"Tang",
"Ke",
""
... | TITLE: Dataset Condensation for Recommendation
ABSTRACT: Training recommendation models on large datasets requires significant time
and resources. It is desired to construct concise yet informative datasets for
efficient training. Recent advances in dataset condensation show promise in
addressing this problem by sy... |
2311.12047 | Jiali Cheng | Jiali Cheng, Hadi Amiri | MultiDelete for Multimodal Machine Unlearning | ECCV 2024 | null | null | null | cs.AI cs.CL cs.CV cs.LG | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Machine Unlearning removes specific knowledge about training data samples
from an already trained model. It has significant practical benefits, such as
purging private, inaccurate, or outdated information from trained models
without the need for complete re-training. Unlearning within a multimodal
setting presents un... | [
{
"version": "v1",
"created": "Sat, 18 Nov 2023 08:30:38 GMT"
},
{
"version": "v2",
"created": "Mon, 15 Jul 2024 01:40:54 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Cheng",
"Jiali",
""
],
[
"Amiri",
"Hadi",
""
]
] | TITLE: MultiDelete for Multimodal Machine Unlearning
ABSTRACT: Machine Unlearning removes specific knowledge about training data samples
from an already trained model. It has significant practical benefits, such as
purging private, inaccurate, or outdated information from trained models
without the need for complet... |
2402.00786 | Manuel Faysse | Manuel Faysse, Patrick Fernandes, Nuno M. Guerreiro, Ant\'onio Loison,
Duarte M. Alves, Caio Corro, Nicolas Boizard, Jo\~ao Alves, Ricardo Rei,
Pedro H. Martins, Antoni Bigata Casademunt, Fran\c{c}ois Yvon, Andr\'e F.T.
Martins, Gautier Viaud, C\'eline Hudelot, Pierre Colombo | CroissantLLM: A Truly Bilingual French-English Language Model | null | null | null | null | cs.CL cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We introduce CroissantLLM, a 1.3B language model pretrained on a set of 3T
English and French tokens, to bring to the research and industrial community a
high-performance, fully open-sourced bilingual model that runs swiftly on
consumer-grade local hardware. To that end, we pioneer the approach of training
an intrins... | [
{
"version": "v1",
"created": "Thu, 1 Feb 2024 17:17:55 GMT"
},
{
"version": "v2",
"created": "Fri, 2 Feb 2024 17:43:41 GMT"
},
{
"version": "v3",
"created": "Tue, 13 Feb 2024 17:12:26 GMT"
},
{
"version": "v4",
"created": "Fri, 29 Mar 2024 14:56:42 GMT"
},
{
"ver... | 2025-04-10T00:00:00 | [
[
"Faysse",
"Manuel",
""
],
[
"Fernandes",
"Patrick",
""
],
[
"Guerreiro",
"Nuno M.",
""
],
[
"Loison",
"António",
""
],
[
"Alves",
"Duarte M.",
""
],
[
"Corro",
"Caio",
""
],
[
"Boizard",
"Nicolas",
""
],
... | TITLE: CroissantLLM: A Truly Bilingual French-English Language Model
ABSTRACT: We introduce CroissantLLM, a 1.3B language model pretrained on a set of 3T
English and French tokens, to bring to the research and industrial community a
high-performance, fully open-sourced bilingual model that runs swiftly on
consumer-... |
2402.01359 | Shae McFadden | Zeliang Kan, Shae McFadden, Daniel Arp, Feargus Pendlebury, Roberto
Jordaney, Johannes Kinder, Fabio Pierazzi, Lorenzo Cavallaro | TESSERACT: Eliminating Experimental Bias in Malware Classification
across Space and Time (Extended Version) | 30 pages. arXiv admin note: text overlap with arXiv:1807.07838 | null | null | null | cs.LG cs.CR cs.PF | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Machine learning (ML) plays a pivotal role in detecting malicious software.
Despite the high F1-scores reported in numerous studies reaching upwards of
0.99, the issue is not completely solved. Malware detectors often experience
performance decay due to constantly evolving operating systems and attack
methods, which ... | [
{
"version": "v1",
"created": "Fri, 2 Feb 2024 12:27:32 GMT"
},
{
"version": "v2",
"created": "Wed, 9 Apr 2025 12:32:21 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Kan",
"Zeliang",
""
],
[
"McFadden",
"Shae",
""
],
[
"Arp",
"Daniel",
""
],
[
"Pendlebury",
"Feargus",
""
],
[
"Jordaney",
"Roberto",
""
],
[
"Kinder",
"Johannes",
""
],
[
"Pierazzi",
"Fabio",
""
],
[
... | TITLE: TESSERACT: Eliminating Experimental Bias in Malware Classification
across Space and Time (Extended Version)
ABSTRACT: Machine learning (ML) plays a pivotal role in detecting malicious software.
Despite the high F1-scores reported in numerous studies reaching upwards of
0.99, the issue is not completely sol... |
2402.07601 | Long Teng | Long Teng and Yanhao Wang and Zhe Lin and Fei Yu | Topic-aware Most Influential Community Search in Social Networks | Accepted by Neurocomputing | null | 10.1016/j.neucom.2025.130173 | null | cs.SI | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Influential community search (ICS) finds a set of densely connected and
high-impact vertices from a social network. Although great effort has been
devoted to ICS problems, most existing methods do not consider how relevant the
influential community found is to specific topics. A few attempts at
topic-aware ICS proble... | [
{
"version": "v1",
"created": "Mon, 12 Feb 2024 11:59:47 GMT"
},
{
"version": "v2",
"created": "Tue, 8 Apr 2025 16:51:19 GMT"
},
{
"version": "v3",
"created": "Wed, 9 Apr 2025 04:13:54 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Teng",
"Long",
""
],
[
"Wang",
"Yanhao",
""
],
[
"Lin",
"Zhe",
""
],
[
"Yu",
"Fei",
""
]
] | TITLE: Topic-aware Most Influential Community Search in Social Networks
ABSTRACT: Influential community search (ICS) finds a set of densely connected and
high-impact vertices from a social network. Although great effort has been
devoted to ICS problems, most existing methods do not consider how relevant the
influen... |
2402.12513 | Usama Muneeb | Usama Muneeb and Mesrob I. Ohannessian | Induced Model Matching: Restricted Models Help Train Full-Featured
Models | null | null | null | null | cs.LG cs.CL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We consider scenarios where a very accurate (often small) predictive model
using restricted features is available when training a full-featured (often
larger) model. This restricted model may be thought of as side-information'',
and can come either from an auxiliary dataset or from the same dataset by
forcing the res... | [
{
"version": "v1",
"created": "Mon, 19 Feb 2024 20:21:09 GMT"
},
{
"version": "v2",
"created": "Tue, 8 Apr 2025 19:27:14 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Muneeb",
"Usama",
""
],
[
"Ohannessian",
"Mesrob I.",
""
]
] | TITLE: Induced Model Matching: Restricted Models Help Train Full-Featured
Models
ABSTRACT: We consider scenarios where a very accurate (often small) predictive model
using restricted features is available when training a full-featured (often
larger) model. This restricted model may be thought of as side-informati... |
2403.04821 | Gilles Dejaegere | Gilles Dejaegere, Mahmoud Sakr | New algorithms for the simplification of multiple trajectories under
bandwidth constraints | Preprint, To be published as a proceeding of Workshop on Big Mobility
Data Analytics (BMDA) co-located with EDBT/ICDT 2024 Joint Conference | null | null | null | cs.OH | http://creativecommons.org/licenses/by-nc-nd/4.0/ | This study introduces time-windowed variations of three established
trajectory simplification algorithms. These new algorithms are specifically
designed to be used in contexts with bandwidth limitations. We present the
details of these algorithms and highlight the differences compared to their
classical counterparts.... | [
{
"version": "v1",
"created": "Thu, 7 Mar 2024 15:39:48 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Dejaegere",
"Gilles",
""
],
[
"Sakr",
"Mahmoud",
""
]
] | TITLE: New algorithms for the simplification of multiple trajectories under
bandwidth constraints
ABSTRACT: This study introduces time-windowed variations of three established
trajectory simplification algorithms. These new algorithms are specifically
designed to be used in contexts with bandwidth limitations. We... |
2403.05821 | Shu Liu | Shu Liu, Asim Biswal, Amog Kamsetty, Audrey Cheng, Luis Gaspar
Schroeder, Liana Patel, Shiyi Cao, Xiangxi Mo, Ion Stoica, Joseph E.
Gonzalez, Matei Zaharia | Optimizing LLM Queries in Relational Data Analytics Workloads | null | null | null | null | cs.LG cs.DB | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Batch data analytics is a growing application for Large Language Models
(LLMs). LLMs enable users to perform a wide range of natural language tasks,
such as classification, entity extraction, and translation, over large
datasets. However, LLM inference is highly costly and slow: for example, an
NVIDIA L4 GPU running ... | [
{
"version": "v1",
"created": "Sat, 9 Mar 2024 07:01:44 GMT"
},
{
"version": "v2",
"created": "Wed, 9 Apr 2025 10:23:39 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Liu",
"Shu",
""
],
[
"Biswal",
"Asim",
""
],
[
"Kamsetty",
"Amog",
""
],
[
"Cheng",
"Audrey",
""
],
[
"Schroeder",
"Luis Gaspar",
""
],
[
"Patel",
"Liana",
""
],
[
"Cao",
"Shiyi",
""
],
[
"Mo",
... | TITLE: Optimizing LLM Queries in Relational Data Analytics Workloads
ABSTRACT: Batch data analytics is a growing application for Large Language Models
(LLMs). LLMs enable users to perform a wide range of natural language tasks,
such as classification, entity extraction, and translation, over large
datasets. However... |
2403.12072 | Eduardo R. B. Marques | Ant\'onio Filgueiras, Eduardo R. B. Marques, Lu\'is M. B. Lopes,
Miguel Marques, Hugo Silva | Floralens: a Deep Learning Model for the Portuguese Native Flora | null | null | null | null | cs.CV cs.LG | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Machine-learning techniques, especially deep convolutional neural networks,
are pivotal for image-based identification of biological species in many
Citizen Science platforms. In this paper, we describe the construction of a
dataset for the Portuguese native flora based on publicly available
research-grade datasets, ... | [
{
"version": "v1",
"created": "Tue, 13 Feb 2024 15:23:21 GMT"
},
{
"version": "v2",
"created": "Fri, 25 Oct 2024 10:00:15 GMT"
},
{
"version": "v3",
"created": "Wed, 9 Apr 2025 10:12:38 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Filgueiras",
"António",
""
],
[
"Marques",
"Eduardo R. B.",
""
],
[
"Lopes",
"Luís M. B.",
""
],
[
"Marques",
"Miguel",
""
],
[
"Silva",
"Hugo",
""
]
] | TITLE: Floralens: a Deep Learning Model for the Portuguese Native Flora
ABSTRACT: Machine-learning techniques, especially deep convolutional neural networks,
are pivotal for image-based identification of biological species in many
Citizen Science platforms. In this paper, we describe the construction of a
dataset f... |
2404.01663 | Meiling Tao | Xuechen Liang, Meiling Tao, Yinghui Xia, Tianyu Shi, Jun Wang,
JingSong Yang | CMAT: A Multi-Agent Collaboration Tuning Framework for Enhancing Small
Language Models | null | null | null | null | cs.CL cs.AI cs.CC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Open large language models (LLMs) have significantly advanced the field of
natural language processing, showcasing impressive performance across various
tasks.Despite the significant advancements in LLMs, their effective operation
still relies heavily on human input to accurately guide the dialogue flow, with
agent t... | [
{
"version": "v1",
"created": "Tue, 2 Apr 2024 06:07:35 GMT"
},
{
"version": "v2",
"created": "Thu, 4 Apr 2024 12:40:03 GMT"
},
{
"version": "v3",
"created": "Mon, 26 Aug 2024 20:30:40 GMT"
},
{
"version": "v4",
"created": "Sun, 1 Sep 2024 22:02:32 GMT"
},
{
"vers... | 2025-04-10T00:00:00 | [
[
"Liang",
"Xuechen",
""
],
[
"Tao",
"Meiling",
""
],
[
"Xia",
"Yinghui",
""
],
[
"Shi",
"Tianyu",
""
],
[
"Wang",
"Jun",
""
],
[
"Yang",
"JingSong",
""
]
] | TITLE: CMAT: A Multi-Agent Collaboration Tuning Framework for Enhancing Small
Language Models
ABSTRACT: Open large language models (LLMs) have significantly advanced the field of
natural language processing, showcasing impressive performance across various
tasks.Despite the significant advancements in LLMs, their... |
2404.16323 | Jiamin Wu | Jiamin Wu, Kenkun Liu, Han Gao, Xiaoke Jiang, Yao Yuan, Lei Zhang | LeanGaussian: Breaking Pixel or Point Cloud Correspondence in Modeling
3D Gaussians | null | null | null | null | cs.CV | http://creativecommons.org/licenses/by/4.0/ | Recently, Gaussian splatting has demonstrated significant success in novel
view synthesis. Current methods often regress Gaussians with pixel or point
cloud correspondence, linking each Gaussian with a pixel or a 3D point. This
leads to the redundancy of Gaussians being used to overfit the correspondence
rather than ... | [
{
"version": "v1",
"created": "Thu, 25 Apr 2024 04:18:59 GMT"
},
{
"version": "v2",
"created": "Mon, 2 Dec 2024 03:11:06 GMT"
},
{
"version": "v3",
"created": "Mon, 7 Apr 2025 08:14:57 GMT"
},
{
"version": "v4",
"created": "Wed, 9 Apr 2025 07:00:32 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Wu",
"Jiamin",
""
],
[
"Liu",
"Kenkun",
""
],
[
"Gao",
"Han",
""
],
[
"Jiang",
"Xiaoke",
""
],
[
"Yuan",
"Yao",
""
],
[
"Zhang",
"Lei",
""
]
] | TITLE: LeanGaussian: Breaking Pixel or Point Cloud Correspondence in Modeling
3D Gaussians
ABSTRACT: Recently, Gaussian splatting has demonstrated significant success in novel
view synthesis. Current methods often regress Gaussians with pixel or point
cloud correspondence, linking each Gaussian with a pixel or a ... |
2405.15868 | Marco Paul E. Apolinario | Marco Paul E. Apolinario, Arani Roy, Kaushik Roy | LLS: Local Learning Rule for Deep Neural Networks Inspired by Neural
Activity Synchronization | 12 pages, 4 figures | Proceedings of the Winter Conference on Applications of Computer
Vision (WACV), 2025 | 10.1109/WACV61041.2025.00758 | null | cs.NE cs.AI cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Training deep neural networks (DNNs) using traditional backpropagation (BP)
presents challenges in terms of computational complexity and energy
consumption, particularly for on-device learning where computational resources
are limited. Various alternatives to BP, including random feedback alignment,
forward-forward, ... | [
{
"version": "v1",
"created": "Fri, 24 May 2024 18:24:24 GMT"
},
{
"version": "v2",
"created": "Tue, 29 Oct 2024 16:35:59 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Apolinario",
"Marco Paul E.",
""
],
[
"Roy",
"Arani",
""
],
[
"Roy",
"Kaushik",
""
]
] | TITLE: LLS: Local Learning Rule for Deep Neural Networks Inspired by Neural
Activity Synchronization
ABSTRACT: Training deep neural networks (DNNs) using traditional backpropagation (BP)
presents challenges in terms of computational complexity and energy
consumption, particularly for on-device learning where comp... |
2406.06650 | Geongyu Lee | Geongyu Lee, Joonho Lee, Tae-Yeong Kwak, Sun Woo Kim, Youngmee Kwon,
Chungyeul Kim, Hyeyoon Chang | Assessing the risk of recurrence in early-stage breast cancer through
H&E stained whole slide images | 20 pages, 9 figures | null | null | null | eess.IV cs.CV | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Accurate prediction of the likelihood of recurrence is important in the
selection of postoperative treatment for patients with early-stage breast
cancer. In this study, we investigated whether deep learning algorithms can
predict patients' risk of recurrence by analyzing the pathology images of their
cancer histology... | [
{
"version": "v1",
"created": "Mon, 10 Jun 2024 08:51:59 GMT"
},
{
"version": "v2",
"created": "Wed, 9 Apr 2025 08:51:52 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Lee",
"Geongyu",
""
],
[
"Lee",
"Joonho",
""
],
[
"Kwak",
"Tae-Yeong",
""
],
[
"Kim",
"Sun Woo",
""
],
[
"Kwon",
"Youngmee",
""
],
[
"Kim",
"Chungyeul",
""
],
[
"Chang",
"Hyeyoon",
""
]
] | TITLE: Assessing the risk of recurrence in early-stage breast cancer through
H&E stained whole slide images
ABSTRACT: Accurate prediction of the likelihood of recurrence is important in the
selection of postoperative treatment for patients with early-stage breast
cancer. In this study, we investigated whether dee... |
2406.10999 | Liman Wang | Hanyang Zhong, Liman Wang, Wenting Cao, Zeyuan Sun | Balancing Rigor and Utility: Mitigating Cognitive Biases in Large
Language Models for Multiple-Choice Questions | This work has been accepted as a full paper at the 2025 Annual
Conference of the Cognitive Science Society (CogSci 2025) and will be
presented in the form of a poster. The associated public dataset and project
website are available at: https://hanyangzhong.github.io/BRU-website/ | null | null | null | cs.CL cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper examines the role of cognitive biases in the decision-making
processes of large language models (LLMs), challenging the conventional goal of
eliminating all biases. When properly balanced, we show that certain cognitive
biases can enhance decision-making efficiency through rational deviations and
heuristic... | [
{
"version": "v1",
"created": "Sun, 16 Jun 2024 16:25:22 GMT"
},
{
"version": "v2",
"created": "Mon, 2 Sep 2024 20:26:30 GMT"
},
{
"version": "v3",
"created": "Mon, 9 Sep 2024 16:28:09 GMT"
},
{
"version": "v4",
"created": "Tue, 8 Apr 2025 23:59:08 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Zhong",
"Hanyang",
""
],
[
"Wang",
"Liman",
""
],
[
"Cao",
"Wenting",
""
],
[
"Sun",
"Zeyuan",
""
]
] | TITLE: Balancing Rigor and Utility: Mitigating Cognitive Biases in Large
Language Models for Multiple-Choice Questions
ABSTRACT: This paper examines the role of cognitive biases in the decision-making
processes of large language models (LLMs), challenging the conventional goal of
eliminating all biases. When prop... |
2406.16899 | Yuni Susanti | Yuni Susanti, Nina Holsmoelle | Prompting or Fine-tuning? Exploring Large Language Models for Causal
Graph Validation | null | null | null | null | cs.CL cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This study explores the capability of Large Language Models (LLMs) to
evaluate causality in causal graphs generated by conventional statistical
causal discovery methods-a task traditionally reliant on manual assessment by
human subject matter experts. To bridge this gap in causality assessment, LLMs
are employed to e... | [
{
"version": "v1",
"created": "Wed, 29 May 2024 09:06:18 GMT"
},
{
"version": "v2",
"created": "Wed, 9 Apr 2025 04:44:48 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Susanti",
"Yuni",
""
],
[
"Holsmoelle",
"Nina",
""
]
] | TITLE: Prompting or Fine-tuning? Exploring Large Language Models for Causal
Graph Validation
ABSTRACT: This study explores the capability of Large Language Models (LLMs) to
evaluate causality in causal graphs generated by conventional statistical
causal discovery methods-a task traditionally reliant on manual ass... |
2407.00742 | Dazhou Yu | Dazhou Yu, Yuntong Hu, Yun Li, Liang Zhao | PolygonGNN: Representation Learning for Polygonal Geometries with
Heterogeneous Visibility Graph | null | null | 10.1145/3637528.3671738 | null | cs.CV cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Polygon representation learning is essential for diverse applications,
encompassing tasks such as shape coding, building pattern classification, and
geographic question answering. While recent years have seen considerable
advancements in this field, much of the focus has been on single polygons,
overlooking the intri... | [
{
"version": "v1",
"created": "Sun, 30 Jun 2024 16:07:49 GMT"
},
{
"version": "v2",
"created": "Wed, 9 Apr 2025 06:17:32 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Yu",
"Dazhou",
""
],
[
"Hu",
"Yuntong",
""
],
[
"Li",
"Yun",
""
],
[
"Zhao",
"Liang",
""
]
] | TITLE: PolygonGNN: Representation Learning for Polygonal Geometries with
Heterogeneous Visibility Graph
ABSTRACT: Polygon representation learning is essential for diverse applications,
encompassing tasks such as shape coding, building pattern classification, and
geographic question answering. While recent years h... |
2407.03038 | Feijie Wu | Feijie Wu, Xiaoze Liu, Haoyu Wang, Xingchen Wang, Lu Su, Jing Gao | Towards Federated RLHF with Aggregated Client Preference for LLMs | ICLR'25 | null | null | null | cs.CL cs.DC cs.LG | http://creativecommons.org/licenses/by/4.0/ | Reinforcement learning with human feedback (RLHF) fine-tunes a pretrained
large language model (LLM) using user preference data, enabling it to generate
content aligned with human preferences. However, due to privacy concerns, users
may be reluctant to share sensitive preference data. To address this, we
propose util... | [
{
"version": "v1",
"created": "Wed, 3 Jul 2024 12:02:24 GMT"
},
{
"version": "v2",
"created": "Mon, 27 Jan 2025 20:14:32 GMT"
},
{
"version": "v3",
"created": "Tue, 8 Apr 2025 18:13:57 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Wu",
"Feijie",
""
],
[
"Liu",
"Xiaoze",
""
],
[
"Wang",
"Haoyu",
""
],
[
"Wang",
"Xingchen",
""
],
[
"Su",
"Lu",
""
],
[
"Gao",
"Jing",
""
]
] | TITLE: Towards Federated RLHF with Aggregated Client Preference for LLMs
ABSTRACT: Reinforcement learning with human feedback (RLHF) fine-tunes a pretrained
large language model (LLM) using user preference data, enabling it to generate
content aligned with human preferences. However, due to privacy concerns, users
... |
2407.06204 | Weilin Cai | Weilin Cai, Juyong Jiang, Fan Wang, Jing Tang, Sunghun Kim, Jiayi
Huang | A Survey on Mixture of Experts in Large Language Models | The first three authors contributed equally to this work; Accepted by
TKDE | IEEE Transactions on Knowledge and Data Engineering (TKDE) 2025 | 10.1109/TKDE.2025.3554028 | null | cs.LG cs.CL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Large language models (LLMs) have garnered unprecedented advancements across
diverse fields, ranging from natural language processing to computer vision and
beyond. The prowess of LLMs is underpinned by their substantial model size,
extensive and diverse datasets, and the vast computational power harnessed
during tra... | [
{
"version": "v1",
"created": "Wed, 26 Jun 2024 16:34:33 GMT"
},
{
"version": "v2",
"created": "Thu, 8 Aug 2024 07:13:37 GMT"
},
{
"version": "v3",
"created": "Wed, 9 Apr 2025 13:54:59 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Cai",
"Weilin",
""
],
[
"Jiang",
"Juyong",
""
],
[
"Wang",
"Fan",
""
],
[
"Tang",
"Jing",
""
],
[
"Kim",
"Sunghun",
""
],
[
"Huang",
"Jiayi",
""
]
] | TITLE: A Survey on Mixture of Experts in Large Language Models
ABSTRACT: Large language models (LLMs) have garnered unprecedented advancements across
diverse fields, ranging from natural language processing to computer vision and
beyond. The prowess of LLMs is underpinned by their substantial model size,
extensive ... |
2407.17378 | Nan Peng | Nan Peng, Xun Zhou, Mingming Wang, Xiaojun Yang, Songming Chen,
Guisong Chen | PrevPredMap: Exploring Temporal Modeling with Previous Predictions for
Online Vectorized HD Map Construction | null | null | 10.1109/WACV61041.2025.00789 | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Temporal information is crucial for detecting occluded instances. Existing
temporal representations have progressed from BEV or PV features to more
compact query features. Compared to these aforementioned features, predictions
offer the highest level of abstraction, providing explicit information. In the
context of o... | [
{
"version": "v1",
"created": "Wed, 24 Jul 2024 15:58:24 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Peng",
"Nan",
""
],
[
"Zhou",
"Xun",
""
],
[
"Wang",
"Mingming",
""
],
[
"Yang",
"Xiaojun",
""
],
[
"Chen",
"Songming",
""
],
[
"Chen",
"Guisong",
""
]
] | TITLE: PrevPredMap: Exploring Temporal Modeling with Previous Predictions for
Online Vectorized HD Map Construction
ABSTRACT: Temporal information is crucial for detecting occluded instances. Existing
temporal representations have progressed from BEV or PV features to more
compact query features. Compared to thes... |
2408.13230 | Daniel Habermann | Daniel Habermann, Marvin Schmitt, Lars K\"uhmichel, Andreas Bulling,
Stefan T. Radev, Paul-Christian B\"urkner | Amortized Bayesian Multilevel Models | 24 pages, 13 figures | null | null | null | stat.ML cs.LG stat.CO | http://creativecommons.org/licenses/by-sa/4.0/ | Multilevel models (MLMs) are a central building block of the Bayesian
workflow. They enable joint, interpretable modeling of data across hierarchical
levels and provide a fully probabilistic quantification of uncertainty. Despite
their well-recognized advantages, MLMs pose significant computational
challenges, often ... | [
{
"version": "v1",
"created": "Fri, 23 Aug 2024 17:11:04 GMT"
},
{
"version": "v2",
"created": "Wed, 9 Apr 2025 13:38:39 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Habermann",
"Daniel",
""
],
[
"Schmitt",
"Marvin",
""
],
[
"Kühmichel",
"Lars",
""
],
[
"Bulling",
"Andreas",
""
],
[
"Radev",
"Stefan T.",
""
],
[
"Bürkner",
"Paul-Christian",
""
]
] | TITLE: Amortized Bayesian Multilevel Models
ABSTRACT: Multilevel models (MLMs) are a central building block of the Bayesian
workflow. They enable joint, interpretable modeling of data across hierarchical
levels and provide a fully probabilistic quantification of uncertainty. Despite
their well-recognized advantages... |
2409.03025 | Manu Gaur | Manu Gaur and Darshan Singh and Makarand Tapaswi | No Detail Left Behind: Revisiting Self-Retrieval for Fine-Grained Image
Captioning | Published at Transactions on Machine Learning Research (TMLR)
https://openreview.net/forum?id=gqh0yzPYdo | null | null | null | cs.CV | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Image captioning systems are unable to generate fine-grained captions as they
are trained on data that is either noisy (alt-text) or generic (human
annotations). This is further exacerbated by maximum likelihood training that
encourages generation of frequently occurring phrases. Previous works have
tried to address ... | [
{
"version": "v1",
"created": "Wed, 4 Sep 2024 18:32:39 GMT"
},
{
"version": "v2",
"created": "Wed, 9 Apr 2025 04:34:41 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Gaur",
"Manu",
""
],
[
"Singh",
"Darshan",
""
],
[
"Tapaswi",
"Makarand",
""
]
] | TITLE: No Detail Left Behind: Revisiting Self-Retrieval for Fine-Grained Image
Captioning
ABSTRACT: Image captioning systems are unable to generate fine-grained captions as they
are trained on data that is either noisy (alt-text) or generic (human
annotations). This is further exacerbated by maximum likelihood tr... |
2409.13415 | Raghunath Sahoo | Kamaljeet Singh, Kangkan Goswami, Raghunath Sahoo, and Sumanta Samal | Design and development of advanced Al-Ti-V alloys for beampipe
applications in particle accelerators | Same as the published version | Phys. Rev. Accel. Beams 28, 043101 (2025) | 10.1103/PhysRevAccelBeams.28.043101 | null | physics.acc-ph cond-mat.mtrl-sci hep-ex nucl-ex | http://creativecommons.org/licenses/by-sa/4.0/ | The present investigation reports the design and development of an advanced
material with a high figure of merit (FoM) for beampipe applications in
particle accelerators by bringing synergy between computational and
experimental approaches. Machine learning algorithms have been used to predict
the phase(s), low densi... | [
{
"version": "v1",
"created": "Fri, 20 Sep 2024 11:27:13 GMT"
},
{
"version": "v2",
"created": "Wed, 9 Apr 2025 09:58:04 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Singh",
"Kamaljeet",
""
],
[
"Goswami",
"Kangkan",
""
],
[
"Sahoo",
"Raghunath",
""
],
[
"Samal",
"Sumanta",
""
]
] | TITLE: Design and development of advanced Al-Ti-V alloys for beampipe
applications in particle accelerators
ABSTRACT: The present investigation reports the design and development of an advanced
material with a high figure of merit (FoM) for beampipe applications in
particle accelerators by bringing synergy betwee... |
2409.16507 | Ryan Lagerquist | Ryan Lagerquist, Galina Chirokova, Robert DeMaria, Mark DeMaria, Imme
Ebert-Uphoff | Center-fixing of tropical cyclones using uncertainty-aware deep learning
applied to high-temporal-resolution geostationary satellite imagery | Submitted to AMS journal Weather and Forecasting. Main body is 64
pages and 17 figures; supplement is another 33 pages and 31 figures | null | null | null | physics.ao-ph cs.AI | http://creativecommons.org/licenses/by/4.0/ | Determining the location of a tropical cyclone's (TC) surface circulation
center -- "center-fixing" -- is a critical first step in the TC-forecasting
process, affecting current and future estimates of track, intensity, and
structure. Despite a recent increase in automated center-fixing methods, only
one such method (... | [
{
"version": "v1",
"created": "Tue, 24 Sep 2024 23:39:56 GMT"
},
{
"version": "v2",
"created": "Tue, 8 Apr 2025 18:34:36 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Lagerquist",
"Ryan",
""
],
[
"Chirokova",
"Galina",
""
],
[
"DeMaria",
"Robert",
""
],
[
"DeMaria",
"Mark",
""
],
[
"Ebert-Uphoff",
"Imme",
""
]
] | TITLE: Center-fixing of tropical cyclones using uncertainty-aware deep learning
applied to high-temporal-resolution geostationary satellite imagery
ABSTRACT: Determining the location of a tropical cyclone's (TC) surface circulation
center -- "center-fixing" -- is a critical first step in the TC-forecasting
proces... |
2410.00876 | Sharmishtha Dutta | Sharmishtha Dutta, Alex Gittens, Mohammed J. Zaki, Charu C. Aggarwal | Replacing Paths with Connection-Biased Attention for Knowledge Graph
Completion | null | null | null | null | cs.LG | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Knowledge graph (KG) completion aims to identify additional facts that can be
inferred from the existing facts in the KG. Recent developments in this field
have explored this task in the inductive setting, where at test time one sees
entities that were not present during training; the most performant models in
the in... | [
{
"version": "v1",
"created": "Tue, 1 Oct 2024 17:12:41 GMT"
},
{
"version": "v2",
"created": "Thu, 19 Dec 2024 20:34:15 GMT"
},
{
"version": "v3",
"created": "Sun, 23 Feb 2025 22:52:22 GMT"
},
{
"version": "v4",
"created": "Wed, 9 Apr 2025 02:12:28 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Dutta",
"Sharmishtha",
""
],
[
"Gittens",
"Alex",
""
],
[
"Zaki",
"Mohammed J.",
""
],
[
"Aggarwal",
"Charu C.",
""
]
] | TITLE: Replacing Paths with Connection-Biased Attention for Knowledge Graph
Completion
ABSTRACT: Knowledge graph (KG) completion aims to identify additional facts that can be
inferred from the existing facts in the KG. Recent developments in this field
have explored this task in the inductive setting, where at te... |
2410.07991 | Lorenzo Cima | Tommaso Giorgi, Lorenzo Cima, Tiziano Fagni, Marco Avvenuti, Stefano
Cresci | Human and LLM Biases in Hate Speech Annotations: A Socio-Demographic
Analysis of Annotators and Targets | null | null | null | null | cs.CL cs.AI cs.HC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The rise of online platforms exacerbated the spread of hate speech, demanding
scalable and effective detection. However, the accuracy of hate speech
detection systems heavily relies on human-labeled data, which is inherently
susceptible to biases. While previous work has examined the issue, the
interplay between the ... | [
{
"version": "v1",
"created": "Thu, 10 Oct 2024 14:48:57 GMT"
},
{
"version": "v2",
"created": "Thu, 17 Oct 2024 14:44:45 GMT"
},
{
"version": "v3",
"created": "Sun, 20 Oct 2024 08:13:18 GMT"
},
{
"version": "v4",
"created": "Thu, 19 Dec 2024 15:16:49 GMT"
},
{
"v... | 2025-04-10T00:00:00 | [
[
"Giorgi",
"Tommaso",
""
],
[
"Cima",
"Lorenzo",
""
],
[
"Fagni",
"Tiziano",
""
],
[
"Avvenuti",
"Marco",
""
],
[
"Cresci",
"Stefano",
""
]
] | TITLE: Human and LLM Biases in Hate Speech Annotations: A Socio-Demographic
Analysis of Annotators and Targets
ABSTRACT: The rise of online platforms exacerbated the spread of hate speech, demanding
scalable and effective detection. However, the accuracy of hate speech
detection systems heavily relies on human-la... |
2410.08427 | Jens Dietrich | Jens Dietrich, Tim White, Behnaz Hassanshahi, Paddy Krishnan | Levels of Binary Equivalence for the Comparison of Binaries from
Alternative Builds | 20 pages, 1 figure, 10 tables | null | null | null | cs.CR cs.SE | http://creativecommons.org/licenses/by-sa/4.0/ | In response to challenges in software supply chain security, several
organisations have created infrastructures to independently build commodity
open source projects and release the resulting binaries. Build platform
variability can strengthen security as it facilitates the detection of
compromised build environments... | [
{
"version": "v1",
"created": "Fri, 11 Oct 2024 00:16:26 GMT"
},
{
"version": "v2",
"created": "Wed, 9 Apr 2025 08:55:38 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Dietrich",
"Jens",
""
],
[
"White",
"Tim",
""
],
[
"Hassanshahi",
"Behnaz",
""
],
[
"Krishnan",
"Paddy",
""
]
] | TITLE: Levels of Binary Equivalence for the Comparison of Binaries from
Alternative Builds
ABSTRACT: In response to challenges in software supply chain security, several
organisations have created infrastructures to independently build commodity
open source projects and release the resulting binaries. Build platf... |
2410.12695 | Phoenix Yu | Phoenix Yu, Tilo Burghardt, Andrew W Dowsey, Neill W Campbell | Holstein-Friesian Re-Identification using Multiple Cameras and
Self-Supervision on a Working Farm | 24 pages, 10 figures | null | null | null | cs.CV | http://creativecommons.org/licenses/by/4.0/ | We present MultiCamCows2024, a farm-scale image dataset filmed across
multiple cameras for the biometric identification of individual
Holstein-Friesian cattle exploiting their unique black and white coat-patterns.
Captured by three ceiling-mounted visual sensors covering adjacent barn areas
over seven days on a worki... | [
{
"version": "v1",
"created": "Wed, 16 Oct 2024 15:58:47 GMT"
},
{
"version": "v2",
"created": "Wed, 9 Apr 2025 17:01:38 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Yu",
"Phoenix",
""
],
[
"Burghardt",
"Tilo",
""
],
[
"Dowsey",
"Andrew W",
""
],
[
"Campbell",
"Neill W",
""
]
] | TITLE: Holstein-Friesian Re-Identification using Multiple Cameras and
Self-Supervision on a Working Farm
ABSTRACT: We present MultiCamCows2024, a farm-scale image dataset filmed across
multiple cameras for the biometric identification of individual
Holstein-Friesian cattle exploiting their unique black and white ... |
2410.15198 | Md Elias Hossain | Elias Hossain, Tasfia Nuzhat, Shamsul Masum, Shahram Rahimi and
Noorbakhsh Amiri Golilarz | Medical-GAT: Cancer Document Classification Leveraging Graph-Based
Residual Network for Scenarios with Limited Data | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Accurate classification of cancer-related medical abstracts is crucial for
healthcare management and research. However, obtaining large, labeled datasets
in the medical domain is challenging due to privacy concerns and the complexity
of clinical data. This scarcity of annotated data impedes the development of
effecti... | [
{
"version": "v1",
"created": "Sat, 19 Oct 2024 20:07:40 GMT"
},
{
"version": "v2",
"created": "Thu, 24 Oct 2024 14:42:30 GMT"
},
{
"version": "v3",
"created": "Wed, 26 Mar 2025 02:20:22 GMT"
},
{
"version": "v4",
"created": "Tue, 8 Apr 2025 22:53:41 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Hossain",
"Elias",
""
],
[
"Nuzhat",
"Tasfia",
""
],
[
"Masum",
"Shamsul",
""
],
[
"Rahimi",
"Shahram",
""
],
[
"Golilarz",
"Noorbakhsh Amiri",
""
]
] | TITLE: Medical-GAT: Cancer Document Classification Leveraging Graph-Based
Residual Network for Scenarios with Limited Data
ABSTRACT: Accurate classification of cancer-related medical abstracts is crucial for
healthcare management and research. However, obtaining large, labeled datasets
in the medical domain is ch... |
2410.18388 | Bo Han | Bo Han, Yuheng Jia, Hui Liu, Junhui Hou | Irregular Tensor Low-Rank Representation for Hyperspectral Image
Representation | null | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Spectral variations pose a common challenge in analyzing hyperspectral images
(HSI). To address this, low-rank tensor representation has emerged as a robust
strategy, leveraging inherent correlations within HSI data. However, the
spatial distribution of ground objects in HSIs is inherently irregular,
existing natural... | [
{
"version": "v1",
"created": "Thu, 24 Oct 2024 02:56:22 GMT"
},
{
"version": "v2",
"created": "Sat, 15 Feb 2025 13:44:29 GMT"
},
{
"version": "v3",
"created": "Wed, 9 Apr 2025 02:24:14 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Han",
"Bo",
""
],
[
"Jia",
"Yuheng",
""
],
[
"Liu",
"Hui",
""
],
[
"Hou",
"Junhui",
""
]
] | TITLE: Irregular Tensor Low-Rank Representation for Hyperspectral Image
Representation
ABSTRACT: Spectral variations pose a common challenge in analyzing hyperspectral images
(HSI). To address this, low-rank tensor representation has emerged as a robust
strategy, leveraging inherent correlations within HSI data. ... |
2410.21591 | Zifeng Wang | Zifeng Wang, Benjamin Danek, Ziwei Yang, Zheng Chen, Jimeng Sun | Can Large Language Models Replace Data Scientists in Biomedical
Research? | null | null | null | null | cs.AI cs.CL q-bio.GN q-bio.QM | http://creativecommons.org/licenses/by/4.0/ | Data science plays a critical role in biomedical research, but it requires
professionals with expertise in coding and medical data analysis. Large
language models (LLMs) have shown great potential in supporting medical tasks
and performing well in general coding tests. However, existing evaluations fail
to assess the... | [
{
"version": "v1",
"created": "Mon, 28 Oct 2024 22:48:06 GMT"
},
{
"version": "v2",
"created": "Tue, 8 Apr 2025 21:48:54 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Wang",
"Zifeng",
""
],
[
"Danek",
"Benjamin",
""
],
[
"Yang",
"Ziwei",
""
],
[
"Chen",
"Zheng",
""
],
[
"Sun",
"Jimeng",
""
]
] | TITLE: Can Large Language Models Replace Data Scientists in Biomedical
Research?
ABSTRACT: Data science plays a critical role in biomedical research, but it requires
professionals with expertise in coding and medical data analysis. Large
language models (LLMs) have shown great potential in supporting medical task... |
2410.22622 | Dung Nguyen | Dung Thuy Nguyen, Taylor T. Johnson, Kevin Leach | PARDON: Privacy-Aware and Robust Federated Domain Generalization | 2025 IEEE 45th International Conference on Distributed Computing
Systems (ICDCS) | null | null | null | cs.LG cs.CV cs.DC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Federated Learning (FL) shows promise in preserving privacy and enabling
collaborative learning. However, most current solutions focus on private data
collected from a single domain. A significant challenge arises when client data
comes from diverse domains (i.e., domain shift), leading to poor performance on
unseen ... | [
{
"version": "v1",
"created": "Wed, 30 Oct 2024 00:50:23 GMT"
},
{
"version": "v2",
"created": "Tue, 8 Apr 2025 22:15:47 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Nguyen",
"Dung Thuy",
""
],
[
"Johnson",
"Taylor T.",
""
],
[
"Leach",
"Kevin",
""
]
] | TITLE: PARDON: Privacy-Aware and Robust Federated Domain Generalization
ABSTRACT: Federated Learning (FL) shows promise in preserving privacy and enabling
collaborative learning. However, most current solutions focus on private data
collected from a single domain. A significant challenge arises when client data
com... |
2411.03299 | Roodabeh Safavi | Monika Henzinger, Roodabeh Safavi, Salil Vadhan | Concurrent Composition for Differentially Private Continual Mechanisms | null | null | null | null | cs.DS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Many intended uses of differential privacy involve a $\textit{continual
mechanism}$ that is set up to run continuously over a long period of time,
making more statistical releases as either queries come in or the dataset is
updated. In this paper, we give the first general treatment of privacy against
$\textit{adapti... | [
{
"version": "v1",
"created": "Tue, 5 Nov 2024 17:50:39 GMT"
},
{
"version": "v2",
"created": "Tue, 8 Apr 2025 18:47:59 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Henzinger",
"Monika",
""
],
[
"Safavi",
"Roodabeh",
""
],
[
"Vadhan",
"Salil",
""
]
] | TITLE: Concurrent Composition for Differentially Private Continual Mechanisms
ABSTRACT: Many intended uses of differential privacy involve a $\textit{continual
mechanism}$ that is set up to run continuously over a long period of time,
making more statistical releases as either queries come in or the dataset is
upda... |
2411.03861 | Joseph Geo Benjamin | Joseph Geo Benjamin, Mothilal Asokan, Mohammad Yaqub, Karthik
Nandakumar | FedSECA: Sign Election and Coordinate-wise Aggregation of Gradients for
Byzantine Tolerant Federated Learning | Accepted in 4th Workshop on Federated Learning for Computer Vision
(FedVision-2025), held in conjunction with CVPR-2025 | null | null | null | cs.CV cs.CR | http://creativecommons.org/licenses/by/4.0/ | One of the most common defense strategies against Byzantine clients in
federated learning (FL) is to employ a robust aggregator mechanism that makes
the training more resilient. While many existing Byzantine robust aggregators
provide theoretical convergence guarantees and are empirically effective
against certain ca... | [
{
"version": "v1",
"created": "Wed, 6 Nov 2024 12:14:11 GMT"
},
{
"version": "v2",
"created": "Tue, 8 Apr 2025 21:19:40 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Benjamin",
"Joseph Geo",
""
],
[
"Asokan",
"Mothilal",
""
],
[
"Yaqub",
"Mohammad",
""
],
[
"Nandakumar",
"Karthik",
""
]
] | TITLE: FedSECA: Sign Election and Coordinate-wise Aggregation of Gradients for
Byzantine Tolerant Federated Learning
ABSTRACT: One of the most common defense strategies against Byzantine clients in
federated learning (FL) is to employ a robust aggregator mechanism that makes
the training more resilient. While man... |
2411.04502 | Sunan Zhao | Sunan Zhao, Zhijie Li, Boyu Fan, Yunpeng Wang, Huiyu Yang, Jianchun
Wang | LESnets (Large-Eddy Simulation nets): Physics-informed neural operator
for large-eddy simulation of turbulence | 37 pages, 28 figures, 73 conferences | null | null | null | physics.flu-dyn | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Acquisition of large datasets for three-dimensional (3D) partial differential
equations (PDE) is usually very expensive. Physics-informed neural operator
(PINO) eliminates the high costs associated with generation of training
datasets, and shows great potential in a variety of partial differential
equations. In this ... | [
{
"version": "v1",
"created": "Thu, 7 Nov 2024 07:53:01 GMT"
},
{
"version": "v2",
"created": "Tue, 8 Apr 2025 07:31:17 GMT"
},
{
"version": "v3",
"created": "Wed, 9 Apr 2025 05:25:43 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Zhao",
"Sunan",
""
],
[
"Li",
"Zhijie",
""
],
[
"Fan",
"Boyu",
""
],
[
"Wang",
"Yunpeng",
""
],
[
"Yang",
"Huiyu",
""
],
[
"Wang",
"Jianchun",
""
]
] | TITLE: LESnets (Large-Eddy Simulation nets): Physics-informed neural operator
for large-eddy simulation of turbulence
ABSTRACT: Acquisition of large datasets for three-dimensional (3D) partial differential
equations (PDE) is usually very expensive. Physics-informed neural operator
(PINO) eliminates the high costs... |
2411.06565 | Ting-Ju Wei | Ting-Ju Wei and Chuin-Shan Chen | Foundation Model for Composite Microstructures: Reconstruction,
Stiffness, and Nonlinear Behavior Prediction | null | null | null | null | cs.CE cs.AI | http://creativecommons.org/licenses/by/4.0/ | The rapid advancement of machine learning has unlocked numerous opportunities
for materials science, particularly in accelerating the design and analysis of
materials. However, a significant challenge lies in the scarcity and high cost
of obtaining high-quality materials datasets. While foundation models
pre-trained ... | [
{
"version": "v1",
"created": "Sun, 10 Nov 2024 19:06:25 GMT"
},
{
"version": "v2",
"created": "Tue, 4 Feb 2025 14:57:37 GMT"
},
{
"version": "v3",
"created": "Tue, 8 Apr 2025 19:00:34 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Wei",
"Ting-Ju",
""
],
[
"Chen",
"Chuin-Shan",
""
]
] | TITLE: Foundation Model for Composite Microstructures: Reconstruction,
Stiffness, and Nonlinear Behavior Prediction
ABSTRACT: The rapid advancement of machine learning has unlocked numerous opportunities
for materials science, particularly in accelerating the design and analysis of
materials. However, a significa... |
2411.07413 | Futoon M. Abushaqra PhD | Futoon M.Abushaqra, Hao Xue, Yongli Ren and Flora D.Salim | ODEStream: A Buffer-Free Online Learning Framework with ODE-based
Adaptor for Streaming Time Series Forecasting | null | null | null | null | cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Addressing the challenges of irregularity and concept drift in streaming time
series is crucial for real-world predictive modelling. Previous studies in time
series continual learning often propose models that require buffering long
sequences, potentially restricting the responsiveness of the inference system.
Moreov... | [
{
"version": "v1",
"created": "Mon, 11 Nov 2024 22:36:33 GMT"
},
{
"version": "v2",
"created": "Wed, 9 Apr 2025 13:29:09 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Abushaqra",
"Futoon M.",
""
],
[
"Xue",
"Hao",
""
],
[
"Ren",
"Yongli",
""
],
[
"Salim",
"Flora D.",
""
]
] | TITLE: ODEStream: A Buffer-Free Online Learning Framework with ODE-based
Adaptor for Streaming Time Series Forecasting
ABSTRACT: Addressing the challenges of irregularity and concept drift in streaming time
series is crucial for real-world predictive modelling. Previous studies in time
series continual learning o... |
2411.08397 | Aoi Ito | Aoi Ito, Kota Dohi, Yohei Kawaguchi | CLaSP: Learning Concepts for Time-Series Signals from Natural Language
Supervision | null | null | null | null | cs.CL cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper presents CLaSP, a novel model for retrieving time-series signals
using natural language queries that describe signal characteristics. The
ability to search time-series signals based on descriptive queries is essential
in domains such as industrial diagnostics, where data scientists often need to
find signa... | [
{
"version": "v1",
"created": "Wed, 13 Nov 2024 07:32:58 GMT"
},
{
"version": "v2",
"created": "Wed, 9 Apr 2025 08:01:55 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Ito",
"Aoi",
""
],
[
"Dohi",
"Kota",
""
],
[
"Kawaguchi",
"Yohei",
""
]
] | TITLE: CLaSP: Learning Concepts for Time-Series Signals from Natural Language
Supervision
ABSTRACT: This paper presents CLaSP, a novel model for retrieving time-series signals
using natural language queries that describe signal characteristics. The
ability to search time-series signals based on descriptive querie... |
2411.09216 | Ryan Krueger | Ryan K. Krueger, Megan C. Engel, Ryan Hausen, Michael P. Brenner | Fitting Coarse-Grained Models to Macroscopic Experimental Data via
Automatic Differentiation | null | null | null | null | physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Developing physics-based models for molecular simulation requires fitting
many unknown parameters to diverse experimental datasets. Traditionally, this
process is piecemeal and difficult to reproduce, leading to a fragmented
landscape of models. Here, we establish a systematic, extensible framework for
fitting coarse... | [
{
"version": "v1",
"created": "Thu, 14 Nov 2024 06:28:05 GMT"
},
{
"version": "v2",
"created": "Wed, 9 Apr 2025 03:09:39 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Krueger",
"Ryan K.",
""
],
[
"Engel",
"Megan C.",
""
],
[
"Hausen",
"Ryan",
""
],
[
"Brenner",
"Michael P.",
""
]
] | TITLE: Fitting Coarse-Grained Models to Macroscopic Experimental Data via
Automatic Differentiation
ABSTRACT: Developing physics-based models for molecular simulation requires fitting
many unknown parameters to diverse experimental datasets. Traditionally, this
process is piecemeal and difficult to reproduce, lea... |
2411.12556 | Xiang Li | Xiang Li, Jianpeng Qi, Zhongying Zhao, Guanjie Zheng, Lei Cao, Junyu
Dong, Yanwei Yu | UMGAD: Unsupervised Multiplex Graph Anomaly Detection | null | null | null | null | cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Graph anomaly detection (GAD) is a critical task in graph machine learning,
with the primary objective of identifying anomalous nodes that deviate
significantly from the majority. This task is widely applied in various
real-world scenarios, including fraud detection and social network analysis.
However, existing GAD ... | [
{
"version": "v1",
"created": "Tue, 19 Nov 2024 15:15:45 GMT"
},
{
"version": "v2",
"created": "Sun, 2 Mar 2025 13:29:03 GMT"
},
{
"version": "v3",
"created": "Tue, 4 Mar 2025 09:56:09 GMT"
},
{
"version": "v4",
"created": "Wed, 9 Apr 2025 04:11:23 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Li",
"Xiang",
""
],
[
"Qi",
"Jianpeng",
""
],
[
"Zhao",
"Zhongying",
""
],
[
"Zheng",
"Guanjie",
""
],
[
"Cao",
"Lei",
""
],
[
"Dong",
"Junyu",
""
],
[
"Yu",
"Yanwei",
""
]
] | TITLE: UMGAD: Unsupervised Multiplex Graph Anomaly Detection
ABSTRACT: Graph anomaly detection (GAD) is a critical task in graph machine learning,
with the primary objective of identifying anomalous nodes that deviate
significantly from the majority. This task is widely applied in various
real-world scenarios, incl... |
2411.12946 | Gabriel Chua | Gabriel Chua, Shing Yee Chan, Shaun Khoo | A Flexible Large Language Models Guardrail Development Methodology
Applied to Off-Topic Prompt Detection | 8 pages, 5 figures | null | null | null | cs.CL cs.LG | http://creativecommons.org/licenses/by/4.0/ | Large Language Models (LLMs) are prone to off-topic misuse, where users may
prompt these models to perform tasks beyond their intended scope. Current
guardrails, which often rely on curated examples or custom classifiers, suffer
from high false-positive rates, limited adaptability, and the impracticality of
requiring... | [
{
"version": "v1",
"created": "Wed, 20 Nov 2024 00:31:23 GMT"
},
{
"version": "v2",
"created": "Wed, 9 Apr 2025 08:59:26 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Chua",
"Gabriel",
""
],
[
"Chan",
"Shing Yee",
""
],
[
"Khoo",
"Shaun",
""
]
] | TITLE: A Flexible Large Language Models Guardrail Development Methodology
Applied to Off-Topic Prompt Detection
ABSTRACT: Large Language Models (LLMs) are prone to off-topic misuse, where users may
prompt these models to perform tasks beyond their intended scope. Current
guardrails, which often rely on curated ex... |
2411.15209 | Xinye Chen | Erin Carson, Xinye Chen, and Cheng Kang | Quantized symbolic time series approximation | null | null | null | null | cs.LG eess.SP stat.ML | http://creativecommons.org/licenses/by/4.0/ | Time series are ubiquitous in numerous science and engineering domains, e.g.,
signal processing, bioinformatics, and astronomy. Previous work has verified
the efficacy of symbolic time series representation in a variety of engineering
applications due to its storage efficiency and numerosity reduction. The most
recen... | [
{
"version": "v1",
"created": "Wed, 20 Nov 2024 10:32:22 GMT"
},
{
"version": "v2",
"created": "Wed, 9 Apr 2025 13:46:27 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Carson",
"Erin",
""
],
[
"Chen",
"Xinye",
""
],
[
"Kang",
"Cheng",
""
]
] | TITLE: Quantized symbolic time series approximation
ABSTRACT: Time series are ubiquitous in numerous science and engineering domains, e.g.,
signal processing, bioinformatics, and astronomy. Previous work has verified
the efficacy of symbolic time series representation in a variety of engineering
applications due to... |
2411.18923 | Dennis Singh Moirangthem Dr | Meher Bhardwaj, Hrishikesh Ethari, and Dennis Singh Moirangthem | EzSQL: An SQL intermediate representation for improving SQL-to-text
Generation | Under revision and review at Expert System With Applications Journal
after first review | null | null | null | cs.CL cs.AI | http://creativecommons.org/licenses/by/4.0/ | The SQL-to-text generation task traditionally uses template base, Seq2Seq,
tree-to-sequence, and graph-to-sequence models. Recent models take advantage of
pre-trained generative language models for this task in the Seq2Seq framework.
However, treating SQL as a sequence of inputs to the pre-trained models is not
optim... | [
{
"version": "v1",
"created": "Thu, 28 Nov 2024 05:24:46 GMT"
},
{
"version": "v2",
"created": "Wed, 9 Apr 2025 05:40:29 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Bhardwaj",
"Meher",
""
],
[
"Ethari",
"Hrishikesh",
""
],
[
"Moirangthem",
"Dennis Singh",
""
]
] | TITLE: EzSQL: An SQL intermediate representation for improving SQL-to-text
Generation
ABSTRACT: The SQL-to-text generation task traditionally uses template base, Seq2Seq,
tree-to-sequence, and graph-to-sequence models. Recent models take advantage of
pre-trained generative language models for this task in the Seq... |
2411.19942 | Hang Ye | Hang Ye, Xiaoxuan Ma, Hai Ci, Wentao Zhu, Yizhou Wang | FreeCloth: Free-form Generation Enhances Challenging Clothed Human
Modeling | 23 pages, 26 figures | null | null | null | cs.CV cs.GR cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Achieving realistic animated human avatars requires accurate modeling of
pose-dependent clothing deformations. Existing learning-based methods heavily
rely on the Linear Blend Skinning (LBS) of minimally-clothed human models like
SMPL to model deformation. However, they struggle to handle loose clothing,
such as long... | [
{
"version": "v1",
"created": "Fri, 29 Nov 2024 18:58:17 GMT"
},
{
"version": "v2",
"created": "Thu, 20 Mar 2025 07:24:19 GMT"
},
{
"version": "v3",
"created": "Wed, 9 Apr 2025 12:48:01 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Ye",
"Hang",
""
],
[
"Ma",
"Xiaoxuan",
""
],
[
"Ci",
"Hai",
""
],
[
"Zhu",
"Wentao",
""
],
[
"Wang",
"Yizhou",
""
]
] | TITLE: FreeCloth: Free-form Generation Enhances Challenging Clothed Human
Modeling
ABSTRACT: Achieving realistic animated human avatars requires accurate modeling of
pose-dependent clothing deformations. Existing learning-based methods heavily
rely on the Linear Blend Skinning (LBS) of minimally-clothed human mod... |
2412.02993 | Jiongtong Hu | Jiongtong Hu, Wei Zhuo, Jun Cheng, Yingying Liu, Wufeng Xue and Dong
Ni | EchoONE: Segmenting Multiple echocardiography Planes in One Model | Accepted by CVPR 2025 | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In clinical practice of echocardiography examinations, multiple planes
containing the heart structures of different view are usually required in
screening, diagnosis and treatment of cardiac disease. AI models for
echocardiography have to be tailored for each specific plane due to the
dramatic structure differences, ... | [
{
"version": "v1",
"created": "Wed, 4 Dec 2024 03:19:43 GMT"
},
{
"version": "v2",
"created": "Mon, 3 Mar 2025 13:59:01 GMT"
},
{
"version": "v3",
"created": "Wed, 9 Apr 2025 03:11:43 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Hu",
"Jiongtong",
""
],
[
"Zhuo",
"Wei",
""
],
[
"Cheng",
"Jun",
""
],
[
"Liu",
"Yingying",
""
],
[
"Xue",
"Wufeng",
""
],
[
"Ni",
"Dong",
""
]
] | TITLE: EchoONE: Segmenting Multiple echocardiography Planes in One Model
ABSTRACT: In clinical practice of echocardiography examinations, multiple planes
containing the heart structures of different view are usually required in
screening, diagnosis and treatment of cardiac disease. AI models for
echocardiography ha... |
2412.04244 | Dingxi Zhang | Rao Fu, Dingxi Zhang, Alex Jiang, Wanjia Fu, Austin Funk, Daniel
Ritchie, Srinath Sridhar | GigaHands: A Massive Annotated Dataset of Bimanual Hand Activities | CVPR 2025 Highlight | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Understanding bimanual human hand activities is a critical problem in AI and
robotics. We cannot build large models of bimanual activities because existing
datasets lack the scale, coverage of diverse hand activities, and detailed
annotations. We introduce GigaHands, a massive annotated dataset capturing 34
hours of ... | [
{
"version": "v1",
"created": "Thu, 5 Dec 2024 15:26:51 GMT"
},
{
"version": "v2",
"created": "Fri, 13 Dec 2024 22:20:30 GMT"
},
{
"version": "v3",
"created": "Wed, 9 Apr 2025 10:18:05 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Fu",
"Rao",
""
],
[
"Zhang",
"Dingxi",
""
],
[
"Jiang",
"Alex",
""
],
[
"Fu",
"Wanjia",
""
],
[
"Funk",
"Austin",
""
],
[
"Ritchie",
"Daniel",
""
],
[
"Sridhar",
"Srinath",
""
]
] | TITLE: GigaHands: A Massive Annotated Dataset of Bimanual Hand Activities
ABSTRACT: Understanding bimanual human hand activities is a critical problem in AI and
robotics. We cannot build large models of bimanual activities because existing
datasets lack the scale, coverage of diverse hand activities, and detailed
a... |
2412.10972 | Luis Wiedmann | Luis Wiedmann, Luca Wiehe, David Rozenberszki | DCSEG: Decoupled 3D Open-Set Segmentation using Gaussian Splatting | To be published in CVPR Workshop on Open-World 3D Scene Understanding
with Foundation Models | null | null | null | cs.CV | http://creativecommons.org/licenses/by/4.0/ | Open-set 3D segmentation represents a major point of interest for multiple
downstream robotics and augmented/virtual reality applications. We present a
decoupled 3D segmentation pipeline to ensure modularity and adaptability to
novel 3D representations as well as semantic segmentation foundation models. We
first reco... | [
{
"version": "v1",
"created": "Sat, 14 Dec 2024 21:26:44 GMT"
},
{
"version": "v2",
"created": "Tue, 8 Apr 2025 22:38:24 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Wiedmann",
"Luis",
""
],
[
"Wiehe",
"Luca",
""
],
[
"Rozenberszki",
"David",
""
]
] | TITLE: DCSEG: Decoupled 3D Open-Set Segmentation using Gaussian Splatting
ABSTRACT: Open-set 3D segmentation represents a major point of interest for multiple
downstream robotics and augmented/virtual reality applications. We present a
decoupled 3D segmentation pipeline to ensure modularity and adaptability to
nove... |
2412.11589 | Yu-Hsuan Huang | Yu-Hsuan Huang, Ling Lo, Hongxia Xie, Hong-Han Shuai, Wen-Huang Cheng | Future Sight and Tough Fights: Revolutionizing Sequential Recommendation
with FENRec | Our code is available at https://github.com/uikdwnd/FENRec | null | null | null | cs.IR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Sequential recommendation (SR) systems predict user preferences by analyzing
time-ordered interaction sequences. A common challenge for SR is data sparsity,
as users typically interact with only a limited number of items. While
contrastive learning has been employed in previous approaches to address the
challenges, t... | [
{
"version": "v1",
"created": "Mon, 16 Dec 2024 09:20:29 GMT"
},
{
"version": "v2",
"created": "Fri, 27 Dec 2024 07:36:52 GMT"
},
{
"version": "v3",
"created": "Mon, 24 Feb 2025 08:36:53 GMT"
},
{
"version": "v4",
"created": "Wed, 9 Apr 2025 03:06:59 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Huang",
"Yu-Hsuan",
""
],
[
"Lo",
"Ling",
""
],
[
"Xie",
"Hongxia",
""
],
[
"Shuai",
"Hong-Han",
""
],
[
"Cheng",
"Wen-Huang",
""
]
] | TITLE: Future Sight and Tough Fights: Revolutionizing Sequential Recommendation
with FENRec
ABSTRACT: Sequential recommendation (SR) systems predict user preferences by analyzing
time-ordered interaction sequences. A common challenge for SR is data sparsity,
as users typically interact with only a limited number ... |
2412.12225 | Pan Wang | Pan Wang, Qiang Zhou, Yawen Wu, Tianlong Chen, Jingtong Hu | DLF: Disentangled-Language-Focused Multimodal Sentiment Analysis | AAAI 2025 accepted | null | null | null | cs.LG cs.AI cs.CL cs.MM | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Multimodal Sentiment Analysis (MSA) leverages heterogeneous modalities, such
as language, vision, and audio, to enhance the understanding of human
sentiment. While existing models often focus on extracting shared information
across modalities or directly fusing heterogeneous modalities, such approaches
can introduce ... | [
{
"version": "v1",
"created": "Mon, 16 Dec 2024 10:03:44 GMT"
},
{
"version": "v2",
"created": "Thu, 26 Dec 2024 19:23:17 GMT"
},
{
"version": "v3",
"created": "Wed, 9 Apr 2025 00:52:30 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Wang",
"Pan",
""
],
[
"Zhou",
"Qiang",
""
],
[
"Wu",
"Yawen",
""
],
[
"Chen",
"Tianlong",
""
],
[
"Hu",
"Jingtong",
""
]
] | TITLE: DLF: Disentangled-Language-Focused Multimodal Sentiment Analysis
ABSTRACT: Multimodal Sentiment Analysis (MSA) leverages heterogeneous modalities, such
as language, vision, and audio, to enhance the understanding of human
sentiment. While existing models often focus on extracting shared information
across mo... |
2412.12448 | Sheng Cheng | Sheng Cheng, Ran Tao, Yuliang Gu, Shenlong Wang, Xiaofeng Wang, Naira
Hovakimyan | Task-Parameter Nexus: Task-Specific Parameter Learning for Model-Based
Control | null | null | null | null | cs.RO cs.SY eess.SY | http://creativecommons.org/licenses/by/4.0/ | This paper presents the Task-Parameter Nexus (TPN), a learning-based approach
for online determination of the (near-)optimal control parameters of
model-based controllers (MBCs) for tracking tasks. In TPN, a deep neural
network is introduced to predict the control parameters for any given tracking
task at runtime, es... | [
{
"version": "v1",
"created": "Tue, 17 Dec 2024 01:24:02 GMT"
},
{
"version": "v2",
"created": "Wed, 9 Apr 2025 16:54:38 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Cheng",
"Sheng",
""
],
[
"Tao",
"Ran",
""
],
[
"Gu",
"Yuliang",
""
],
[
"Wang",
"Shenlong",
""
],
[
"Wang",
"Xiaofeng",
""
],
[
"Hovakimyan",
"Naira",
""
]
] | TITLE: Task-Parameter Nexus: Task-Specific Parameter Learning for Model-Based
Control
ABSTRACT: This paper presents the Task-Parameter Nexus (TPN), a learning-based approach
for online determination of the (near-)optimal control parameters of
model-based controllers (MBCs) for tracking tasks. In TPN, a deep neura... |
2412.16615 | Feixiang Guo | Luo Ji, Feixiang Guo, Teng Chen, Qingqing Gu, Xiaoyu Wang, Ningyuan
Xi, Yihong Wang, Peng Yu, Yue Zhao, Hongyang Lei, Zhonglin Jiang, Yong Chen | Large Language Model Can Be a Foundation for Hidden Rationale-Based
Retrieval | 10 pages, 3 figures, ECIR 2025 | null | null | null | cs.IR cs.CL cs.LG | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Despite the recent advancement in Retrieval-Augmented Generation (RAG)
systems, most retrieval methodologies are often developed for factual
retrieval, which assumes query and positive documents are semantically similar.
In this paper, we instead propose and study a more challenging type of
retrieval task, called hid... | [
{
"version": "v1",
"created": "Sat, 21 Dec 2024 13:19:15 GMT"
},
{
"version": "v2",
"created": "Wed, 9 Apr 2025 14:08:58 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Ji",
"Luo",
""
],
[
"Guo",
"Feixiang",
""
],
[
"Chen",
"Teng",
""
],
[
"Gu",
"Qingqing",
""
],
[
"Wang",
"Xiaoyu",
""
],
[
"Xi",
"Ningyuan",
""
],
[
"Wang",
"Yihong",
""
],
[
"Yu",
"Peng",
... | TITLE: Large Language Model Can Be a Foundation for Hidden Rationale-Based
Retrieval
ABSTRACT: Despite the recent advancement in Retrieval-Augmented Generation (RAG)
systems, most retrieval methodologies are often developed for factual
retrieval, which assumes query and positive documents are semantically similar... |
2412.16742 | Yung-Hong Sun | Yung-Hong Sun, Gefei Shen, Jiangang Chen, Jayer Fernandes, Amber L.
Shada, Charles P. Heise, Hongrui Jiang, Yu Hen Hu | EasyVis2: A Real Time Multi-view 3D Visualization System for
Laparoscopic Surgery Training Enhanced by a Deep Neural Network YOLOv8-Pose | 11 pages (12 pages with citations), 12 figures | null | null | null | cs.CV | http://creativecommons.org/licenses/by/4.0/ | EasyVis2 is a system designed to provide hands-free, real-time 3D
visualization for laparoscopic surgery. It incorporates a surgical trocar
equipped with an array of micro-cameras, which can be inserted into the body
cavity to offer an enhanced field of view and a 3D perspective of the surgical
procedure. A specializ... | [
{
"version": "v1",
"created": "Sat, 21 Dec 2024 19:26:19 GMT"
},
{
"version": "v2",
"created": "Tue, 8 Apr 2025 21:14:22 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Sun",
"Yung-Hong",
""
],
[
"Shen",
"Gefei",
""
],
[
"Chen",
"Jiangang",
""
],
[
"Fernandes",
"Jayer",
""
],
[
"Shada",
"Amber L.",
""
],
[
"Heise",
"Charles P.",
""
],
[
"Jiang",
"Hongrui",
""
],
[
... | TITLE: EasyVis2: A Real Time Multi-view 3D Visualization System for
Laparoscopic Surgery Training Enhanced by a Deep Neural Network YOLOv8-Pose
ABSTRACT: EasyVis2 is a system designed to provide hands-free, real-time 3D
visualization for laparoscopic surgery. It incorporates a surgical trocar
equipped with an arr... |
2501.03225 | Yuhui Zhang | Yuhui Zhang, Yuchang Su, Yiming Liu, Xiaohan Wang, James Burgess,
Elaine Sui, Chenyu Wang, Josiah Aklilu, Alejandro Lozano, Anjiang Wei, Ludwig
Schmidt, Serena Yeung-Levy | Automated Generation of Challenging Multiple-Choice Questions for Vision
Language Model Evaluation | CVPR 2025 | null | null | null | cs.CV cs.AI cs.CL cs.CY cs.LG | http://creativecommons.org/licenses/by/4.0/ | The rapid development of vision language models (VLMs) demands rigorous and
reliable evaluation. However, current visual question answering (VQA)
benchmarks often depend on open-ended questions, making accurate evaluation
difficult due to the variability in natural language responses. To address
this, we introduce Au... | [
{
"version": "v1",
"created": "Mon, 6 Jan 2025 18:57:31 GMT"
},
{
"version": "v2",
"created": "Wed, 9 Apr 2025 17:25:07 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Zhang",
"Yuhui",
""
],
[
"Su",
"Yuchang",
""
],
[
"Liu",
"Yiming",
""
],
[
"Wang",
"Xiaohan",
""
],
[
"Burgess",
"James",
""
],
[
"Sui",
"Elaine",
""
],
[
"Wang",
"Chenyu",
""
],
[
"Aklilu",
"J... | TITLE: Automated Generation of Challenging Multiple-Choice Questions for Vision
Language Model Evaluation
ABSTRACT: The rapid development of vision language models (VLMs) demands rigorous and
reliable evaluation. However, current visual question answering (VQA)
benchmarks often depend on open-ended questions, mak... |
2501.03916 | Bo Zhang | Jiakang Yuan, Xiangchao Yan, Shiyang Feng, Bo Zhang, Tao Chen, Botian
Shi, Wanli Ouyang, Yu Qiao, Lei Bai, Bowen Zhou | Dolphin: Moving Towards Closed-loop Auto-research through Thinking,
Practice, and Feedback | 21 pages, 12 figures, and our homepage:
https://alpha-innovator.github.io/Dolphin-project-page | null | null | null | cs.AI cs.CL cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The scientific research paradigm is undergoing a profound transformation
owing to the development of Artificial Intelligence (AI). Recent works
demonstrate that various AI-assisted research methods can largely improve
research efficiency by improving data analysis, accelerating computation, and
fostering novel idea g... | [
{
"version": "v1",
"created": "Tue, 7 Jan 2025 16:31:10 GMT"
},
{
"version": "v2",
"created": "Fri, 10 Jan 2025 13:14:28 GMT"
},
{
"version": "v3",
"created": "Wed, 9 Apr 2025 16:27:02 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Yuan",
"Jiakang",
""
],
[
"Yan",
"Xiangchao",
""
],
[
"Feng",
"Shiyang",
""
],
[
"Zhang",
"Bo",
""
],
[
"Chen",
"Tao",
""
],
[
"Shi",
"Botian",
""
],
[
"Ouyang",
"Wanli",
""
],
[
"Qiao",
"Yu",
... | TITLE: Dolphin: Moving Towards Closed-loop Auto-research through Thinking,
Practice, and Feedback
ABSTRACT: The scientific research paradigm is undergoing a profound transformation
owing to the development of Artificial Intelligence (AI). Recent works
demonstrate that various AI-assisted research methods can larg... |
2501.10481 | Qinyi Tian | Qinyi Tian, Winston Lindqwister, Manolis Veveakis, Laura E. Dalton | Learning Latent Hardening (LLH): Enhancing Deep Learning with Domain
Knowledge for Material Inverse Problems | null | null | null | null | cs.LG cond-mat.mtrl-sci cs.CE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Advancements in deep learning and machine learning have improved the ability
to model complex, nonlinear relationships, such as those encountered in complex
material inverse problems. However, the effectiveness of these methods often
depends on large datasets, which are not always available. In this study, the
incorp... | [
{
"version": "v1",
"created": "Fri, 17 Jan 2025 03:09:25 GMT"
},
{
"version": "v2",
"created": "Sat, 15 Feb 2025 04:15:56 GMT"
},
{
"version": "v3",
"created": "Wed, 9 Apr 2025 03:04:57 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Tian",
"Qinyi",
""
],
[
"Lindqwister",
"Winston",
""
],
[
"Veveakis",
"Manolis",
""
],
[
"Dalton",
"Laura E.",
""
]
] | TITLE: Learning Latent Hardening (LLH): Enhancing Deep Learning with Domain
Knowledge for Material Inverse Problems
ABSTRACT: Advancements in deep learning and machine learning have improved the ability
to model complex, nonlinear relationships, such as those encountered in complex
material inverse problems. Howe... |
2501.10629 | Jiajia Guo | Jiajia Guo, Yiming Cui, Chao-Kai Wen, Shi Jin | Prompt-Enabled Large AI Models for CSI Feedback | 13 pages, 11 figures, 1 table | null | null | null | cs.IT eess.SP math.IT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Artificial intelligence (AI) has emerged as a promising tool for channel
state information (CSI) feedback. While recent research primarily focuses on
improving feedback accuracy on a specific dataset through novel architectures,
the underlying mechanism of AI-based CSI feedback remains unclear. This study
explores th... | [
{
"version": "v1",
"created": "Sat, 18 Jan 2025 02:12:47 GMT"
},
{
"version": "v2",
"created": "Sun, 30 Mar 2025 19:05:58 GMT"
},
{
"version": "v3",
"created": "Wed, 9 Apr 2025 01:26:11 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Guo",
"Jiajia",
""
],
[
"Cui",
"Yiming",
""
],
[
"Wen",
"Chao-Kai",
""
],
[
"Jin",
"Shi",
""
]
] | TITLE: Prompt-Enabled Large AI Models for CSI Feedback
ABSTRACT: Artificial intelligence (AI) has emerged as a promising tool for channel
state information (CSI) feedback. While recent research primarily focuses on
improving feedback accuracy on a specific dataset through novel architectures,
the underlying mechani... |
2501.12900 | Ido Kanter | Ella Koresh, Ronit D. Gross, Yuval Meir, Yarden Tzach, Tal Halevi, and
Ido Kanter | Unified CNNs and transformers underlying learning mechanism reveals
multi-head attention modus vivendi | 31 pages, 11 figures, A short YouTube Video describing the main
results https://www.youtube.com/watch?v=7I8bp7UAudk | Physica A, Statistical Mechanics and its Applications, 666 (2025)
130529 | 10.1016/j.physa.2025.130529 | null | cs.LG cs.CV | http://creativecommons.org/licenses/by/4.0/ | Convolutional neural networks (CNNs) evaluate short-range correlations in
input images which progress along the layers, whereas vision transformer (ViT)
architectures evaluate long-range correlations, using repeated transformer
encoders composed of fully connected layers. Both are designed to solve complex
classifica... | [
{
"version": "v1",
"created": "Wed, 22 Jan 2025 14:19:48 GMT"
},
{
"version": "v2",
"created": "Tue, 11 Mar 2025 13:41:43 GMT"
},
{
"version": "v3",
"created": "Wed, 9 Apr 2025 13:06:49 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Koresh",
"Ella",
""
],
[
"Gross",
"Ronit D.",
""
],
[
"Meir",
"Yuval",
""
],
[
"Tzach",
"Yarden",
""
],
[
"Halevi",
"Tal",
""
],
[
"Kanter",
"Ido",
""
]
] | TITLE: Unified CNNs and transformers underlying learning mechanism reveals
multi-head attention modus vivendi
ABSTRACT: Convolutional neural networks (CNNs) evaluate short-range correlations in
input images which progress along the layers, whereas vision transformer (ViT)
architectures evaluate long-range correla... |
2502.02514 | Jan Dubi\'nski | Antoni Kowalczuk, Jan Dubi\'nski, Franziska Boenisch, Adam Dziedzic | Privacy Attacks on Image AutoRegressive Models | Code: https://github.com/sprintml/privacy_attacks_against_iars | null | null | null | cs.CV cs.LG | http://creativecommons.org/licenses/by/4.0/ | Image autoregressive generation has emerged as a powerful new paradigm, with
image autoregressive models (IARs) matching state-of-the-art diffusion models
(DMs) in image quality (FID: 1.48 vs. 1.58) while allowing for higher
generation speed. However, the privacy risks associated with IARs remain
unexplored, raising ... | [
{
"version": "v1",
"created": "Tue, 4 Feb 2025 17:33:08 GMT"
},
{
"version": "v2",
"created": "Tue, 8 Apr 2025 17:28:09 GMT"
},
{
"version": "v3",
"created": "Wed, 9 Apr 2025 08:33:54 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Kowalczuk",
"Antoni",
""
],
[
"Dubiński",
"Jan",
""
],
[
"Boenisch",
"Franziska",
""
],
[
"Dziedzic",
"Adam",
""
]
] | TITLE: Privacy Attacks on Image AutoRegressive Models
ABSTRACT: Image autoregressive generation has emerged as a powerful new paradigm, with
image autoregressive models (IARs) matching state-of-the-art diffusion models
(DMs) in image quality (FID: 1.48 vs. 1.58) while allowing for higher
generation speed. However, ... |
2502.02862 | Peiyan Yue | Peiyan Yue, Die Cai, Chu Guo, Mengxing Liu, Jun Xia, Yi Wang | Learning Generalizable Features for Tibial Plateau Fracture Segmentation
Using Masked Autoencoder and Limited Annotations | 5 pages, 6 figures. Accepted to IEEE EMBC 2025 | null | null | null | eess.IV cs.AI cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Accurate automated segmentation of tibial plateau fractures (TPF) from
computed tomography (CT) requires large amounts of annotated data to train deep
learning models, but obtaining such annotations presents unique challenges. The
process demands expert knowledge to identify diverse fracture patterns, assess
severity... | [
{
"version": "v1",
"created": "Wed, 5 Feb 2025 03:44:52 GMT"
},
{
"version": "v2",
"created": "Wed, 9 Apr 2025 05:15:50 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Yue",
"Peiyan",
""
],
[
"Cai",
"Die",
""
],
[
"Guo",
"Chu",
""
],
[
"Liu",
"Mengxing",
""
],
[
"Xia",
"Jun",
""
],
[
"Wang",
"Yi",
""
]
] | TITLE: Learning Generalizable Features for Tibial Plateau Fracture Segmentation
Using Masked Autoencoder and Limited Annotations
ABSTRACT: Accurate automated segmentation of tibial plateau fractures (TPF) from
computed tomography (CT) requires large amounts of annotated data to train deep
learning models, but obt... |
2502.03307 | Yu Wang | Yu Wang and Lei Sang and Yi Zhang and Yiwen Zhang | Intent Representation Learning with Large Language Model for
Recommendation | Accepted by SIGIR 2025 Full Paper | null | null | null | cs.IR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Intent-based recommender systems have garnered significant attention for
uncovering latent fine-grained preferences. Intents, as underlying factors of
interactions, are crucial for improving recommendation interpretability. Most
methods define intents as learnable parameters updated alongside interactions.
However, e... | [
{
"version": "v1",
"created": "Wed, 5 Feb 2025 16:08:05 GMT"
},
{
"version": "v2",
"created": "Tue, 11 Feb 2025 14:29:44 GMT"
},
{
"version": "v3",
"created": "Wed, 12 Feb 2025 08:16:44 GMT"
},
{
"version": "v4",
"created": "Wed, 9 Apr 2025 07:21:18 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Wang",
"Yu",
""
],
[
"Sang",
"Lei",
""
],
[
"Zhang",
"Yi",
""
],
[
"Zhang",
"Yiwen",
""
]
] | TITLE: Intent Representation Learning with Large Language Model for
Recommendation
ABSTRACT: Intent-based recommender systems have garnered significant attention for
uncovering latent fine-grained preferences. Intents, as underlying factors of
interactions, are crucial for improving recommendation interpretabilit... |
2502.03375 | Songwen Hu | Songwen Hu, Ryan A. Rossi, Tong Yu, Junda Wu, Handong Zhao, Sungchul
Kim, Shuai Li | Interactive Visualization Recommendation with Hier-SUCB | null | null | 10.1145/3696410.3714697 | null | cs.IR | http://creativecommons.org/licenses/by/4.0/ | Visualization recommendation aims to enable rapid visual analysis of massive
datasets. In real-world scenarios, it is essential to quickly gather and
comprehend user preferences to cover users from diverse backgrounds, including
varying skill levels and analytical tasks. Previous approaches to personalized
visualizat... | [
{
"version": "v1",
"created": "Wed, 5 Feb 2025 17:14:45 GMT"
},
{
"version": "v2",
"created": "Thu, 6 Feb 2025 03:46:29 GMT"
},
{
"version": "v3",
"created": "Thu, 13 Feb 2025 02:17:49 GMT"
},
{
"version": "v4",
"created": "Sun, 9 Mar 2025 04:14:14 GMT"
},
{
"vers... | 2025-04-10T00:00:00 | [
[
"Hu",
"Songwen",
""
],
[
"Rossi",
"Ryan A.",
""
],
[
"Yu",
"Tong",
""
],
[
"Wu",
"Junda",
""
],
[
"Zhao",
"Handong",
""
],
[
"Kim",
"Sungchul",
""
],
[
"Li",
"Shuai",
""
]
] | TITLE: Interactive Visualization Recommendation with Hier-SUCB
ABSTRACT: Visualization recommendation aims to enable rapid visual analysis of massive
datasets. In real-world scenarios, it is essential to quickly gather and
comprehend user preferences to cover users from diverse backgrounds, including
varying skill ... |
2502.12063 | Lester Mackey | Annabelle Michael Carrell, Albert Gong, Abhishek Shetty, Raaz Dwivedi,
Lester Mackey | Low-Rank Thinning | null | null | null | null | stat.ML cs.LG math.OC math.ST stat.ME stat.TH | http://creativecommons.org/licenses/by/4.0/ | The goal in thinning is to summarize a dataset using a small set of
representative points. Remarkably, sub-Gaussian thinning algorithms like Kernel
Halving and Compress can match the quality of uniform subsampling while
substantially reducing the number of summary points. However, existing
guarantees cover only a res... | [
{
"version": "v1",
"created": "Mon, 17 Feb 2025 17:30:14 GMT"
},
{
"version": "v2",
"created": "Fri, 21 Mar 2025 14:13:04 GMT"
},
{
"version": "v3",
"created": "Tue, 1 Apr 2025 17:36:49 GMT"
},
{
"version": "v4",
"created": "Tue, 8 Apr 2025 21:57:48 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Carrell",
"Annabelle Michael",
""
],
[
"Gong",
"Albert",
""
],
[
"Shetty",
"Abhishek",
""
],
[
"Dwivedi",
"Raaz",
""
],
[
"Mackey",
"Lester",
""
]
] | TITLE: Low-Rank Thinning
ABSTRACT: The goal in thinning is to summarize a dataset using a small set of
representative points. Remarkably, sub-Gaussian thinning algorithms like Kernel
Halving and Compress can match the quality of uniform subsampling while
substantially reducing the number of summary points. However,... |
2502.18389 | Nicola Cecere | Nicola Cecere, Andrea Bacciu, Ignacio Fern\'andez Tob\'ias, Amin
Mantrach | Monte Carlo Temperature: a robust sampling strategy for LLM's
uncertainty quantification methods | null | null | null | null | cs.CL | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Uncertainty quantification (UQ) in Large Language Models (LLMs) is essential
for their safe and reliable deployment, particularly in critical applications
where incorrect outputs can have serious consequences. Current UQ methods
typically rely on querying the model multiple times using non-zero temperature
sampling t... | [
{
"version": "v1",
"created": "Tue, 25 Feb 2025 17:33:20 GMT"
},
{
"version": "v2",
"created": "Wed, 9 Apr 2025 16:40:21 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Cecere",
"Nicola",
""
],
[
"Bacciu",
"Andrea",
""
],
[
"Tobías",
"Ignacio Fernández",
""
],
[
"Mantrach",
"Amin",
""
]
] | TITLE: Monte Carlo Temperature: a robust sampling strategy for LLM's
uncertainty quantification methods
ABSTRACT: Uncertainty quantification (UQ) in Large Language Models (LLMs) is essential
for their safe and reliable deployment, particularly in critical applications
where incorrect outputs can have serious cons... |
2502.19217 | Nikita Shvetsov | Nikita Shvetsov, Thomas K. Kilvaer, Masoud Tafavvoghi, Anders Sildnes,
Kajsa M{\o}llersen, Lill-Tove Rasmussen Busund, Lars Ailo Bongo | A Lightweight and Extensible Cell Segmentation and Classification Model
for Whole Slide Images | 30 pages, 11 figures | null | null | null | cs.CV cs.AI | http://creativecommons.org/licenses/by/4.0/ | Developing clinically useful cell-level analysis tools in digital pathology
remains challenging due to limitations in dataset granularity, inconsistent
annotations, high computational demands, and difficulties integrating new
technologies into workflows. To address these issues, we propose a solution
that enhances da... | [
{
"version": "v1",
"created": "Wed, 26 Feb 2025 15:19:52 GMT"
},
{
"version": "v2",
"created": "Wed, 9 Apr 2025 11:06:08 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Shvetsov",
"Nikita",
""
],
[
"Kilvaer",
"Thomas K.",
""
],
[
"Tafavvoghi",
"Masoud",
""
],
[
"Sildnes",
"Anders",
""
],
[
"Møllersen",
"Kajsa",
""
],
[
"Busund",
"Lill-Tove Rasmussen",
""
],
[
"Bongo",
"Lars A... | TITLE: A Lightweight and Extensible Cell Segmentation and Classification Model
for Whole Slide Images
ABSTRACT: Developing clinically useful cell-level analysis tools in digital pathology
remains challenging due to limitations in dataset granularity, inconsistent
annotations, high computational demands, and diffi... |
2503.05639 | Yuxuan Bian | Yuxuan Bian, Zhaoyang Zhang, Xuan Ju, Mingdeng Cao, Liangbin Xie, Ying
Shan, Qiang Xu | VideoPainter: Any-length Video Inpainting and Editing with Plug-and-Play
Context Control | Project page available at
https://yxbian23.github.io/project/video-painter | null | null | null | cs.CV cs.AI cs.MM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Video inpainting, which aims to restore corrupted video content, has
experienced substantial progress. Despite these advances, existing methods,
whether propagating unmasked region pixels through optical flow and receptive
field priors, or extending image-inpainting models temporally, face challenges
in generating fu... | [
{
"version": "v1",
"created": "Fri, 7 Mar 2025 17:59:46 GMT"
},
{
"version": "v2",
"created": "Mon, 10 Mar 2025 18:56:32 GMT"
},
{
"version": "v3",
"created": "Wed, 9 Apr 2025 02:05:33 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Bian",
"Yuxuan",
""
],
[
"Zhang",
"Zhaoyang",
""
],
[
"Ju",
"Xuan",
""
],
[
"Cao",
"Mingdeng",
""
],
[
"Xie",
"Liangbin",
""
],
[
"Shan",
"Ying",
""
],
[
"Xu",
"Qiang",
""
]
] | TITLE: VideoPainter: Any-length Video Inpainting and Editing with Plug-and-Play
Context Control
ABSTRACT: Video inpainting, which aims to restore corrupted video content, has
experienced substantial progress. Despite these advances, existing methods,
whether propagating unmasked region pixels through optical flow... |
2503.08688 | Ariba Khan | Ariba Khan, Stephen Casper, Dylan Hadfield-Menell | Randomness, Not Representation: The Unreliability of Evaluating Cultural
Alignment in LLMs | null | null | null | null | cs.CY | http://creativecommons.org/licenses/by/4.0/ | Research on the 'cultural alignment' of Large Language Models (LLMs) has
emerged in response to growing interest in understanding representation across
diverse stakeholders. Current approaches to evaluating cultural alignment
through survey-based assessments that borrow from social science methodologies
often overloo... | [
{
"version": "v1",
"created": "Tue, 11 Mar 2025 17:59:53 GMT"
},
{
"version": "v2",
"created": "Tue, 8 Apr 2025 21:11:19 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Khan",
"Ariba",
""
],
[
"Casper",
"Stephen",
""
],
[
"Hadfield-Menell",
"Dylan",
""
]
] | TITLE: Randomness, Not Representation: The Unreliability of Evaluating Cultural
Alignment in LLMs
ABSTRACT: Research on the 'cultural alignment' of Large Language Models (LLMs) has
emerged in response to growing interest in understanding representation across
diverse stakeholders. Current approaches to evaluating... |
2503.12978 | Yang Ji | Yang Ji, Ying Sun, Hengshu Zhu | Enhancing Job Salary Prediction with Disentangled Composition Effect
Modeling: A Neural Prototyping Approach | null | null | null | null | cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In the era of the knowledge economy, understanding how job skills influence
salary is crucial for promoting recruitment with competitive salary systems and
aligned salary expectations. Despite efforts on salary prediction based on job
positions and talent demographics, there still lacks methods to effectively
discern... | [
{
"version": "v1",
"created": "Mon, 17 Mar 2025 09:36:07 GMT"
},
{
"version": "v2",
"created": "Sat, 22 Mar 2025 03:28:19 GMT"
},
{
"version": "v3",
"created": "Wed, 9 Apr 2025 02:23:34 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Ji",
"Yang",
""
],
[
"Sun",
"Ying",
""
],
[
"Zhu",
"Hengshu",
""
]
] | TITLE: Enhancing Job Salary Prediction with Disentangled Composition Effect
Modeling: A Neural Prototyping Approach
ABSTRACT: In the era of the knowledge economy, understanding how job skills influence
salary is crucial for promoting recruitment with competitive salary systems and
aligned salary expectations. Des... |
2503.15050 | Aolin Chen | Aolin Chen, Haojun Wu, Qi Xin, Steven P. Reiss, Jifeng Xuan | Studying and Understanding the Effectiveness and Failures of
Conversational LLM-Based Repair | null | null | null | null | cs.SE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Automated program repair (APR) is designed to automate the process of
bug-fixing. In recent years, thanks to the rapid development of large language
models (LLMs), automated repair has achieved remarkable progress. Advanced APR
techniques powered by conversational LLMs, most notably ChatGPT, have exhibited
impressive... | [
{
"version": "v1",
"created": "Wed, 19 Mar 2025 09:39:32 GMT"
},
{
"version": "v2",
"created": "Wed, 9 Apr 2025 14:18:47 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Chen",
"Aolin",
""
],
[
"Wu",
"Haojun",
""
],
[
"Xin",
"Qi",
""
],
[
"Reiss",
"Steven P.",
""
],
[
"Xuan",
"Jifeng",
""
]
] | TITLE: Studying and Understanding the Effectiveness and Failures of
Conversational LLM-Based Repair
ABSTRACT: Automated program repair (APR) is designed to automate the process of
bug-fixing. In recent years, thanks to the rapid development of large language
models (LLMs), automated repair has achieved remarkable... |
2503.22026 | SaiKiran Tedla | SaiKiran Tedla, Junyong Lee, Beixuan Yang, Mahmoud Afifi, Michael S.
Brown | Multispectral Demosaicing via Dual Cameras | null | null | null | null | cs.CV eess.IV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Multispectral (MS) images capture detailed scene information across a wide
range of spectral bands, making them invaluable for applications requiring rich
spectral data. Integrating MS imaging into multi camera devices, such as
smartphones, has the potential to enhance both spectral applications and RGB
image quality... | [
{
"version": "v1",
"created": "Thu, 27 Mar 2025 22:40:55 GMT"
},
{
"version": "v2",
"created": "Wed, 9 Apr 2025 00:18:02 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Tedla",
"SaiKiran",
""
],
[
"Lee",
"Junyong",
""
],
[
"Yang",
"Beixuan",
""
],
[
"Afifi",
"Mahmoud",
""
],
[
"Brown",
"Michael S.",
""
]
] | TITLE: Multispectral Demosaicing via Dual Cameras
ABSTRACT: Multispectral (MS) images capture detailed scene information across a wide
range of spectral bands, making them invaluable for applications requiring rich
spectral data. Integrating MS imaging into multi camera devices, such as
smartphones, has the potenti... |
2503.22352 | Bar{\i}\c{s} Batuhan Topal | Bar{\i}\c{s} Batuhan Topal, Umut \"Ozyurt, Zafer Do\u{g}an Budak,
Ramazan Gokberk Cinbis | Meta-LoRA: Meta-Learning LoRA Components for Domain-Aware ID
Personalization | null | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recent advancements in text-to-image generative models, particularly latent
diffusion models (LDMs), have demonstrated remarkable capabilities in
synthesizing high-quality images from textual prompts. However, achieving
identity personalization-ensuring that a model consistently generates
subject-specific outputs fro... | [
{
"version": "v1",
"created": "Fri, 28 Mar 2025 11:47:33 GMT"
},
{
"version": "v2",
"created": "Wed, 9 Apr 2025 07:33:11 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Topal",
"Barış Batuhan",
""
],
[
"Özyurt",
"Umut",
""
],
[
"Budak",
"Zafer Doğan",
""
],
[
"Cinbis",
"Ramazan Gokberk",
""
]
] | TITLE: Meta-LoRA: Meta-Learning LoRA Components for Domain-Aware ID
Personalization
ABSTRACT: Recent advancements in text-to-image generative models, particularly latent
diffusion models (LDMs), have demonstrated remarkable capabilities in
synthesizing high-quality images from textual prompts. However, achieving
... |
2504.00513 | Asma Yamani | Asma Yamani, Malak Baslyman, Moataz Ahmed | Leveraging LLMs for User Stories in AI Systems: UStAI Dataset | null | null | null | null | cs.SE cs.AI | http://creativecommons.org/licenses/by/4.0/ | AI systems are gaining widespread adoption across various sectors and
domains. Creating high-quality AI system requirements is crucial for aligning
the AI system with business goals and consumer values and for social
responsibility. However, with the uncertain nature of AI systems and the heavy
reliance on sensitive ... | [
{
"version": "v1",
"created": "Tue, 1 Apr 2025 08:03:40 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Yamani",
"Asma",
""
],
[
"Baslyman",
"Malak",
""
],
[
"Ahmed",
"Moataz",
""
]
] | TITLE: Leveraging LLMs for User Stories in AI Systems: UStAI Dataset
ABSTRACT: AI systems are gaining widespread adoption across various sectors and
domains. Creating high-quality AI system requirements is crucial for aligning
the AI system with business goals and consumer values and for social
responsibility. Howe... |
2504.00825 | Mohamed Benzaghta | Mohamed Benzaghta, Giovanni Geraci, David L\'opez-P\'erez, and Alvaro
Valcarce | Data-driven Optimization and Transfer Learning for Cellular Network
Antenna Configurations | null | null | null | null | cs.IT cs.NI eess.SP math.IT | http://creativecommons.org/licenses/by/4.0/ | We propose a data-driven approach for large-scale cellular network
optimization, using a production cellular network in London as a case study and
employing Sionna ray tracing for site-specific channel propagation modeling. We
optimize base station antenna tilts and half-power beamwidths, resulting in
more than doubl... | [
{
"version": "v1",
"created": "Tue, 1 Apr 2025 14:13:33 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Benzaghta",
"Mohamed",
""
],
[
"Geraci",
"Giovanni",
""
],
[
"López-Pérez",
"David",
""
],
[
"Valcarce",
"Alvaro",
""
]
] | TITLE: Data-driven Optimization and Transfer Learning for Cellular Network
Antenna Configurations
ABSTRACT: We propose a data-driven approach for large-scale cellular network
optimization, using a production cellular network in London as a case study and
employing Sionna ray tracing for site-specific channel prop... |
2504.00859 | Mahan Rafidashti | Mahan Rafidashti, Ji Lan, Maryam Fatemi, Junsheng Fu, Lars
Hammarstrand, Lennart Svensson | NeuRadar: Neural Radiance Fields for Automotive Radar Point Clouds | null | null | null | null | cs.CV | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Radar is an important sensor for autonomous driving (AD) systems due to its
robustness to adverse weather and different lighting conditions. Novel view
synthesis using neural radiance fields (NeRFs) has recently received
considerable attention in AD due to its potential to enable efficient testing
and validation but ... | [
{
"version": "v1",
"created": "Tue, 1 Apr 2025 14:50:19 GMT"
},
{
"version": "v2",
"created": "Wed, 9 Apr 2025 12:30:13 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Rafidashti",
"Mahan",
""
],
[
"Lan",
"Ji",
""
],
[
"Fatemi",
"Maryam",
""
],
[
"Fu",
"Junsheng",
""
],
[
"Hammarstrand",
"Lars",
""
],
[
"Svensson",
"Lennart",
""
]
] | TITLE: NeuRadar: Neural Radiance Fields for Automotive Radar Point Clouds
ABSTRACT: Radar is an important sensor for autonomous driving (AD) systems due to its
robustness to adverse weather and different lighting conditions. Novel view
synthesis using neural radiance fields (NeRFs) has recently received
considerabl... |
2504.01466 | Kaiwei Zhang | Kaiwei Zhang, Dandan Zhu, Xiongkuo Min, Guangtao Zhai | Mesh Mamba: A Unified State Space Model for Saliency Prediction in
Non-Textured and Textured Meshes | to be published in CVPR 2025 | null | null | null | cs.CV | http://creativecommons.org/licenses/by/4.0/ | Mesh saliency enhances the adaptability of 3D vision by identifying and
emphasizing regions that naturally attract visual attention. To investigate the
interaction between geometric structure and texture in shaping visual
attention, we establish a comprehensive mesh saliency dataset, which is the
first to systematica... | [
{
"version": "v1",
"created": "Wed, 2 Apr 2025 08:22:25 GMT"
},
{
"version": "v2",
"created": "Wed, 9 Apr 2025 08:35:39 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Zhang",
"Kaiwei",
""
],
[
"Zhu",
"Dandan",
""
],
[
"Min",
"Xiongkuo",
""
],
[
"Zhai",
"Guangtao",
""
]
] | TITLE: Mesh Mamba: A Unified State Space Model for Saliency Prediction in
Non-Textured and Textured Meshes
ABSTRACT: Mesh saliency enhances the adaptability of 3D vision by identifying and
emphasizing regions that naturally attract visual attention. To investigate the
interaction between geometric structure and t... |
2504.01732 | Ulas Gunes | Ulas Gunes, Matias Turkulainen, Xuqian Ren, Arno Solin, Juho Kannala,
Esa Rahtu | FIORD: A Fisheye Indoor-Outdoor Dataset with LIDAR Ground Truth for 3D
Scene Reconstruction and Benchmarking | SCIA 2025 | null | null | null | cs.CV | http://creativecommons.org/licenses/by/4.0/ | The development of large-scale 3D scene reconstruction and novel view
synthesis methods mostly rely on datasets comprising perspective images with
narrow fields of view (FoV). While effective for small-scale scenes, these
datasets require large image sets and extensive structure-from-motion (SfM)
processing, limiting... | [
{
"version": "v1",
"created": "Wed, 2 Apr 2025 13:41:23 GMT"
},
{
"version": "v2",
"created": "Wed, 9 Apr 2025 13:59:22 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Gunes",
"Ulas",
""
],
[
"Turkulainen",
"Matias",
""
],
[
"Ren",
"Xuqian",
""
],
[
"Solin",
"Arno",
""
],
[
"Kannala",
"Juho",
""
],
[
"Rahtu",
"Esa",
""
]
] | TITLE: FIORD: A Fisheye Indoor-Outdoor Dataset with LIDAR Ground Truth for 3D
Scene Reconstruction and Benchmarking
ABSTRACT: The development of large-scale 3D scene reconstruction and novel view
synthesis methods mostly rely on datasets comprising perspective images with
narrow fields of view (FoV). While effect... |
2504.02407 | Ruitong Xiao | Xiaohui Sun, Ruitong Xiao, Jianye Mo, Bowen Wu, Qun Yu, Baoxun Wang | F5R-TTS: Improving Flow-Matching based Text-to-Speech with Group
Relative Policy Optimization | null | null | null | null | cs.SD eess.AS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present F5R-TTS, a novel text-to-speech (TTS) system that integrates
Gradient Reward Policy Optimization (GRPO) into a flow-matching based
architecture. By reformulating the deterministic outputs of flow-matching TTS
into probabilistic Gaussian distributions, our approach enables seamless
integration of reinforcem... | [
{
"version": "v1",
"created": "Thu, 3 Apr 2025 08:57:15 GMT"
},
{
"version": "v2",
"created": "Wed, 9 Apr 2025 02:53:57 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Sun",
"Xiaohui",
""
],
[
"Xiao",
"Ruitong",
""
],
[
"Mo",
"Jianye",
""
],
[
"Wu",
"Bowen",
""
],
[
"Yu",
"Qun",
""
],
[
"Wang",
"Baoxun",
""
]
] | TITLE: F5R-TTS: Improving Flow-Matching based Text-to-Speech with Group
Relative Policy Optimization
ABSTRACT: We present F5R-TTS, a novel text-to-speech (TTS) system that integrates
Gradient Reward Policy Optimization (GRPO) into a flow-matching based
architecture. By reformulating the deterministic outputs of f... |
2504.03043 | Joel Sol | Joel Sol, Shadi Alijani, Homayoun Najjaran | Sliced Wasserstein Discrepancy in Disentangling Representation and
Adaptation Networks for Unsupervised Domain Adaptation | 6 pages, 3 figures, submitted to IEEE conference | null | null | null | cs.CV | http://creativecommons.org/licenses/by-nc-nd/4.0/ | This paper introduces DRANet-SWD as a novel complete pipeline for
disentangling content and style representations of images for unsupervised
domain adaptation (UDA). The approach builds upon DRANet by incorporating the
sliced Wasserstein discrepancy (SWD) as a style loss instead of the traditional
Gram matrix loss. T... | [
{
"version": "v1",
"created": "Thu, 3 Apr 2025 21:43:47 GMT"
},
{
"version": "v2",
"created": "Wed, 9 Apr 2025 05:25:42 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Sol",
"Joel",
""
],
[
"Alijani",
"Shadi",
""
],
[
"Najjaran",
"Homayoun",
""
]
] | TITLE: Sliced Wasserstein Discrepancy in Disentangling Representation and
Adaptation Networks for Unsupervised Domain Adaptation
ABSTRACT: This paper introduces DRANet-SWD as a novel complete pipeline for
disentangling content and style representations of images for unsupervised
domain adaptation (UDA). The appro... |
2504.03133 | Zahid Hassan Tushar | Zahid Hassan Tushar, Adeleke Ademakinwa, Jianwu Wang, Zhibo Zhang,
Sanjay Purushotham | Joint Retrieval of Cloud properties using Attention-based Deep Learning
Models | 6 Pages, 4 figures, to be published in 2025 IEEE International
Geoscience and Remote Sensing Symposium (IGARSS 2025) | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Accurate cloud property retrieval is vital for understanding cloud behavior
and its impact on climate, including applications in weather forecasting,
climate modeling, and estimating Earth's radiation balance. The Independent
Pixel Approximation (IPA), a widely used physics-based approach, simplifies
radiative transf... | [
{
"version": "v1",
"created": "Fri, 4 Apr 2025 03:01:19 GMT"
},
{
"version": "v2",
"created": "Wed, 9 Apr 2025 13:19:52 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Tushar",
"Zahid Hassan",
""
],
[
"Ademakinwa",
"Adeleke",
""
],
[
"Wang",
"Jianwu",
""
],
[
"Zhang",
"Zhibo",
""
],
[
"Purushotham",
"Sanjay",
""
]
] | TITLE: Joint Retrieval of Cloud properties using Attention-based Deep Learning
Models
ABSTRACT: Accurate cloud property retrieval is vital for understanding cloud behavior
and its impact on climate, including applications in weather forecasting,
climate modeling, and estimating Earth's radiation balance. The Inde... |
2504.03770 | Shenzhe Zhu | Yi Nian, Shenzhe Zhu, Yuehan Qin, Li Li, Ziyi Wang, Chaowei Xiao, Yue
Zhao | JailDAM: Jailbreak Detection with Adaptive Memory for Vision-Language
Model | null | null | null | null | cs.CR cs.AI | http://creativecommons.org/licenses/by/4.0/ | Multimodal large language models (MLLMs) excel in vision-language tasks but
also pose significant risks of generating harmful content, particularly through
jailbreak attacks. Jailbreak attacks refer to intentional manipulations that
bypass safety mechanisms in models, leading to the generation of inappropriate
or uns... | [
{
"version": "v1",
"created": "Thu, 3 Apr 2025 05:00:28 GMT"
},
{
"version": "v2",
"created": "Tue, 8 Apr 2025 20:25:30 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Nian",
"Yi",
""
],
[
"Zhu",
"Shenzhe",
""
],
[
"Qin",
"Yuehan",
""
],
[
"Li",
"Li",
""
],
[
"Wang",
"Ziyi",
""
],
[
"Xiao",
"Chaowei",
""
],
[
"Zhao",
"Yue",
""
]
] | TITLE: JailDAM: Jailbreak Detection with Adaptive Memory for Vision-Language
Model
ABSTRACT: Multimodal large language models (MLLMs) excel in vision-language tasks but
also pose significant risks of generating harmful content, particularly through
jailbreak attacks. Jailbreak attacks refer to intentional manipul... |
2504.03784 | Kai Ye | Kai Ye, Hongyi Zhou, Jin Zhu, Francesco Quinzan, Chengchung Shi | Robust Reinforcement Learning from Human Feedback for Large Language
Models Fine-Tuning | null | null | null | null | stat.ML cs.AI cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Reinforcement learning from human feedback (RLHF) has emerged as a key
technique for aligning the output of large language models (LLMs) with human
preferences. To learn the reward function, most existing RLHF algorithms use
the Bradley-Terry model, which relies on assumptions about human preferences
that may not ref... | [
{
"version": "v1",
"created": "Thu, 3 Apr 2025 16:16:35 GMT"
},
{
"version": "v2",
"created": "Wed, 9 Apr 2025 03:41:09 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Ye",
"Kai",
""
],
[
"Zhou",
"Hongyi",
""
],
[
"Zhu",
"Jin",
""
],
[
"Quinzan",
"Francesco",
""
],
[
"Shi",
"Chengchung",
""
]
] | TITLE: Robust Reinforcement Learning from Human Feedback for Large Language
Models Fine-Tuning
ABSTRACT: Reinforcement learning from human feedback (RLHF) has emerged as a key
technique for aligning the output of large language models (LLMs) with human
preferences. To learn the reward function, most existing RLHF... |
2504.04079 | Ashwin Vinod | Ashwin Vinod, Chandrajit Bajaj | Scalable Robust Bayesian Co-Clustering with Compositional ELBOs | null | null | null | null | cs.LG stat.ML | http://creativecommons.org/licenses/by/4.0/ | Co-clustering exploits the duality of instances and features to
simultaneously uncover meaningful groups in both dimensions, often
outperforming traditional clustering in high-dimensional or sparse data
settings. Although recent deep learning approaches successfully integrate
feature learning and cluster assignment, ... | [
{
"version": "v1",
"created": "Sat, 5 Apr 2025 06:48:05 GMT"
},
{
"version": "v2",
"created": "Tue, 8 Apr 2025 18:02:36 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Vinod",
"Ashwin",
""
],
[
"Bajaj",
"Chandrajit",
""
]
] | TITLE: Scalable Robust Bayesian Co-Clustering with Compositional ELBOs
ABSTRACT: Co-clustering exploits the duality of instances and features to
simultaneously uncover meaningful groups in both dimensions, often
outperforming traditional clustering in high-dimensional or sparse data
settings. Although recent deep l... |
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