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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2504.04277 | Marios Kokkodis | Marios Kokkodis, Richard Demsyn-Jones, and Vijay Raghavan | Beyond the Hype: Embeddings vs. Prompting for Multiclass Classification
Tasks | null | null | null | null | cs.LG cs.AI cs.CL stat.AP | http://creativecommons.org/licenses/by/4.0/ | Are traditional classification approaches irrelevant in this era of AI hype?
We show that there are multiclass classification problems where predictive
models holistically outperform LLM prompt-based frameworks. Given text and
images from home-service project descriptions provided by Thumbtack customers,
we build emb... | [
{
"version": "v1",
"created": "Sat, 5 Apr 2025 20:35:54 GMT"
},
{
"version": "v2",
"created": "Wed, 9 Apr 2025 17:15:47 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Kokkodis",
"Marios",
""
],
[
"Demsyn-Jones",
"Richard",
""
],
[
"Raghavan",
"Vijay",
""
]
] | TITLE: Beyond the Hype: Embeddings vs. Prompting for Multiclass Classification
Tasks
ABSTRACT: Are traditional classification approaches irrelevant in this era of AI hype?
We show that there are multiclass classification problems where predictive
models holistically outperform LLM prompt-based frameworks. Given t... |
2504.04514 | Yao Tao | Yao Tao, Yehui Tang, Yun Wang, Mingjian Zhu, Hailin Hu, Yunhe Wang | Saliency-driven Dynamic Token Pruning for Large Language Models | null | null | null | null | cs.CL cs.AI | http://creativecommons.org/licenses/by/4.0/ | Despite the recent success of large language models (LLMs), LLMs are
particularly challenging in long-sequence inference scenarios due to the
quadratic computational complexity of the attention mechanism. Inspired by the
interpretability theory of feature attribution in neural network models, we
observe that not all ... | [
{
"version": "v1",
"created": "Sun, 6 Apr 2025 15:15:07 GMT"
},
{
"version": "v2",
"created": "Wed, 9 Apr 2025 14:36:19 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Tao",
"Yao",
""
],
[
"Tang",
"Yehui",
""
],
[
"Wang",
"Yun",
""
],
[
"Zhu",
"Mingjian",
""
],
[
"Hu",
"Hailin",
""
],
[
"Wang",
"Yunhe",
""
]
] | TITLE: Saliency-driven Dynamic Token Pruning for Large Language Models
ABSTRACT: Despite the recent success of large language models (LLMs), LLMs are
particularly challenging in long-sequence inference scenarios due to the
quadratic computational complexity of the attention mechanism. Inspired by the
interpretabili... |
2504.04713 | Yifei Yu | Yifei Yu, Qian-Wen Zhang, Lingfeng Qiao, Di Yin, Fang Li, Jie Wang,
Zengxi Chen, Suncong Zheng, Xiaolong Liang, Xing Sun | Sequential-NIAH: A Needle-In-A-Haystack Benchmark for Extracting
Sequential Needles from Long Contexts | null | null | null | null | cs.CL cs.IR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Evaluating the ability of large language models (LLMs) to handle extended
contexts is critical, particularly for retrieving information relevant to
specific queries embedded within lengthy inputs. We introduce Sequential-NIAH,
a benchmark specifically designed to evaluate the capability of LLMs to extract
sequential ... | [
{
"version": "v1",
"created": "Mon, 7 Apr 2025 03:50:12 GMT"
},
{
"version": "v2",
"created": "Wed, 9 Apr 2025 08:15:21 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Yu",
"Yifei",
""
],
[
"Zhang",
"Qian-Wen",
""
],
[
"Qiao",
"Lingfeng",
""
],
[
"Yin",
"Di",
""
],
[
"Li",
"Fang",
""
],
[
"Wang",
"Jie",
""
],
[
"Chen",
"Zengxi",
""
],
[
"Zheng",
"Suncong",
... | TITLE: Sequential-NIAH: A Needle-In-A-Haystack Benchmark for Extracting
Sequential Needles from Long Contexts
ABSTRACT: Evaluating the ability of large language models (LLMs) to handle extended
contexts is critical, particularly for retrieving information relevant to
specific queries embedded within lengthy input... |
2504.04798 | Jacob Si | Jacob Si, Zijing Ou, Mike Qu, Zhengrui Xiang, Yingzhen Li | TabRep: a Simple and Effective Continuous Representation for Training
Tabular Diffusion Models | null | null | null | null | cs.LG | http://creativecommons.org/licenses/by/4.0/ | Diffusion models have been the predominant generative model for tabular data
generation. However, they face the conundrum of modeling under a separate
versus a unified data representation. The former encounters the challenge of
jointly modeling all multi-modal distributions of tabular data in one model.
While the lat... | [
{
"version": "v1",
"created": "Mon, 7 Apr 2025 07:44:27 GMT"
},
{
"version": "v2",
"created": "Tue, 8 Apr 2025 15:10:24 GMT"
},
{
"version": "v3",
"created": "Wed, 9 Apr 2025 15:38:00 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Si",
"Jacob",
""
],
[
"Ou",
"Zijing",
""
],
[
"Qu",
"Mike",
""
],
[
"Xiang",
"Zhengrui",
""
],
[
"Li",
"Yingzhen",
""
]
] | TITLE: TabRep: a Simple and Effective Continuous Representation for Training
Tabular Diffusion Models
ABSTRACT: Diffusion models have been the predominant generative model for tabular data
generation. However, they face the conundrum of modeling under a separate
versus a unified data representation. The former en... |
2504.05523 | Elisabeth Fittschen | Elisabeth Fittschen, Sabrina Li, Tom Lippincott, Leshem Choshen, Craig
Messner | Pretraining Language Models for Diachronic Linguistic Change Discovery | null | null | null | null | cs.CL | http://creativecommons.org/licenses/by/4.0/ | Large language models (LLMs) have shown potential as tools for scientific
discovery. This has engendered growing interest in their use in humanistic
disciplines, such as historical linguistics and literary studies. These fields
often construct arguments on the basis of delineations like genre, or more
inflexibly, tim... | [
{
"version": "v1",
"created": "Mon, 7 Apr 2025 21:51:32 GMT"
},
{
"version": "v2",
"created": "Wed, 9 Apr 2025 13:09:06 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Fittschen",
"Elisabeth",
""
],
[
"Li",
"Sabrina",
""
],
[
"Lippincott",
"Tom",
""
],
[
"Choshen",
"Leshem",
""
],
[
"Messner",
"Craig",
""
]
] | TITLE: Pretraining Language Models for Diachronic Linguistic Change Discovery
ABSTRACT: Large language models (LLMs) have shown potential as tools for scientific
discovery. This has engendered growing interest in their use in humanistic
disciplines, such as historical linguistics and literary studies. These fields
... |
2504.05643 | Kaiji Sekimoto | Kaiji Sekimoto and Muneki Yasuda | Effective Method for Inverse Ising Problem under Missing Observations in
Restricted Boltzmann Machines | null | null | null | null | stat.ML cond-mat.dis-nn cs.LG physics.data-an | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Restricted Boltzmann machines (RBMs) are energy-based models analogous to the
Ising model and are widely applied in statistical machine learning. The
standard inverse Ising problem with a complete dataset requires computing both
data and model expectations and is computationally challenging because model
expectations... | [
{
"version": "v1",
"created": "Tue, 8 Apr 2025 03:39:56 GMT"
},
{
"version": "v2",
"created": "Wed, 9 Apr 2025 06:05:02 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Sekimoto",
"Kaiji",
""
],
[
"Yasuda",
"Muneki",
""
]
] | TITLE: Effective Method for Inverse Ising Problem under Missing Observations in
Restricted Boltzmann Machines
ABSTRACT: Restricted Boltzmann machines (RBMs) are energy-based models analogous to the
Ising model and are widely applied in statistical machine learning. The
standard inverse Ising problem with a comple... |
2504.05759 | Nathana\"el Beau | Nathana\"el Beau and Beno\^it Crabb\'e | RETROcode: Leveraging a Code Database for Improved Natural Language to
Code Generation | null | null | null | null | cs.CL | http://creativecommons.org/licenses/by-nc-nd/4.0/ | As text and code resources have expanded, large-scale pre-trained models have
shown promising capabilities in code generation tasks, typically employing
supervised fine-tuning with problem statement-program pairs. However,
increasing model size and data volume for performance gains also raises
computational demands a... | [
{
"version": "v1",
"created": "Tue, 8 Apr 2025 07:41:13 GMT"
},
{
"version": "v2",
"created": "Wed, 9 Apr 2025 06:55:15 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Beau",
"Nathanaël",
""
],
[
"Crabbé",
"Benoît",
""
]
] | TITLE: RETROcode: Leveraging a Code Database for Improved Natural Language to
Code Generation
ABSTRACT: As text and code resources have expanded, large-scale pre-trained models have
shown promising capabilities in code generation tasks, typically employing
supervised fine-tuning with problem statement-program pai... |
2504.05795 | Yanping Zha | Hao Zhang, Yanping Zha, Qingwei Zhuang, Zhenfeng Shao, Jiayi Ma | Robust Fusion Controller: Degradation-aware Image Fusion with
Fine-grained Language Instructions | null | null | null | null | cs.CV | http://creativecommons.org/licenses/by/4.0/ | Current image fusion methods struggle to adapt to real-world environments
encompassing diverse degradations with spatially varying characteristics. To
address this challenge, we propose a robust fusion controller (RFC) capable of
achieving degradation-aware image fusion through fine-grained language
instructions, ens... | [
{
"version": "v1",
"created": "Tue, 8 Apr 2025 08:22:55 GMT"
},
{
"version": "v2",
"created": "Wed, 9 Apr 2025 10:05:59 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Zhang",
"Hao",
""
],
[
"Zha",
"Yanping",
""
],
[
"Zhuang",
"Qingwei",
""
],
[
"Shao",
"Zhenfeng",
""
],
[
"Ma",
"Jiayi",
""
]
] | TITLE: Robust Fusion Controller: Degradation-aware Image Fusion with
Fine-grained Language Instructions
ABSTRACT: Current image fusion methods struggle to adapt to real-world environments
encompassing diverse degradations with spatially varying characteristics. To
address this challenge, we propose a robust fusio... |
2504.06122 | Yahui Liu | Jingyuan Zhang, Qi Wang, Xingguang Ji, Yahui Liu, Yang Yue, Fuzheng
Zhang, Di Zhang, Guorui Zhou, Kun Gai | Leanabell-Prover: Posttraining Scaling in Formal Reasoning | 23 pages, 6 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recent advances in automated theorem proving (ATP) through LLMs have
highlighted the potential of formal reasoning with Lean 4 codes. However, ATP
has not yet be revolutionized by the recent posttraining scaling as
demonstrated by Open AI O1/O3 and Deepseek R1. In this work, we investigate the
entire posttraining of ... | [
{
"version": "v1",
"created": "Tue, 8 Apr 2025 15:15:26 GMT"
},
{
"version": "v2",
"created": "Wed, 9 Apr 2025 04:03:00 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Zhang",
"Jingyuan",
""
],
[
"Wang",
"Qi",
""
],
[
"Ji",
"Xingguang",
""
],
[
"Liu",
"Yahui",
""
],
[
"Yue",
"Yang",
""
],
[
"Zhang",
"Fuzheng",
""
],
[
"Zhang",
"Di",
""
],
[
"Zhou",
"Guorui",
... | TITLE: Leanabell-Prover: Posttraining Scaling in Formal Reasoning
ABSTRACT: Recent advances in automated theorem proving (ATP) through LLMs have
highlighted the potential of formal reasoning with Lean 4 codes. However, ATP
has not yet be revolutionized by the recent posttraining scaling as
demonstrated by Open AI O... |
2504.06125 | Luigi Tresca | Luigi Tresca, Carolin Schmidt, James Harrison, Filipe Rodrigues,
Gioele Zardini, Daniele Gammelli, and Marco Pavone | Robo-taxi Fleet Coordination at Scale via Reinforcement Learning | 12 pages, 6 figures, 6 tables | null | null | null | cs.LG cs.SY eess.SY | http://creativecommons.org/licenses/by/4.0/ | Fleets of robo-taxis offering on-demand transportation services, commonly
known as Autonomous Mobility-on-Demand (AMoD) systems, hold significant promise
for societal benefits, such as reducing pollution, energy consumption, and
urban congestion. However, orchestrating these systems at scale remains a
critical challe... | [
{
"version": "v1",
"created": "Tue, 8 Apr 2025 15:19:41 GMT"
},
{
"version": "v2",
"created": "Wed, 9 Apr 2025 07:54:20 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Tresca",
"Luigi",
""
],
[
"Schmidt",
"Carolin",
""
],
[
"Harrison",
"James",
""
],
[
"Rodrigues",
"Filipe",
""
],
[
"Zardini",
"Gioele",
""
],
[
"Gammelli",
"Daniele",
""
],
[
"Pavone",
"Marco",
""
]
] | TITLE: Robo-taxi Fleet Coordination at Scale via Reinforcement Learning
ABSTRACT: Fleets of robo-taxis offering on-demand transportation services, commonly
known as Autonomous Mobility-on-Demand (AMoD) systems, hold significant promise
for societal benefits, such as reducing pollution, energy consumption, and
urban... |
2504.06160 | Rijul Magu | Rijul Magu, Arka Dutta, Sean Kim, Ashiqur R. KhudaBukhsh, Munmun De
Choudhury | Navigating the Rabbit Hole: Emergent Biases in LLM-Generated Attack
Narratives Targeting Mental Health Groups | null | null | null | null | cs.CL cs.AI cs.CY cs.LG cs.SI | http://creativecommons.org/licenses/by/4.0/ | Large Language Models (LLMs) have been shown to demonstrate imbalanced biases
against certain groups. However, the study of unprovoked targeted attacks by
LLMs towards at-risk populations remains underexplored. Our paper presents
three novel contributions: (1) the explicit evaluation of LLM-generated attacks
on highl... | [
{
"version": "v1",
"created": "Tue, 8 Apr 2025 15:56:57 GMT"
},
{
"version": "v2",
"created": "Wed, 9 Apr 2025 04:24:38 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Magu",
"Rijul",
""
],
[
"Dutta",
"Arka",
""
],
[
"Kim",
"Sean",
""
],
[
"KhudaBukhsh",
"Ashiqur R.",
""
],
[
"De Choudhury",
"Munmun",
""
]
] | TITLE: Navigating the Rabbit Hole: Emergent Biases in LLM-Generated Attack
Narratives Targeting Mental Health Groups
ABSTRACT: Large Language Models (LLMs) have been shown to demonstrate imbalanced biases
against certain groups. However, the study of unprovoked targeted attacks by
LLMs towards at-risk populations... |
2504.06270 | Wenqiao Zhu | Wenqiao Zhu, Lulu Wang, Jun Wu | Addressing Cold-start Problem in Click-Through Rate Prediction via
Supervised Diffusion Modeling | null | null | null | null | cs.IR cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Predicting Click-Through Rates is a crucial function within recommendation
and advertising platforms, as the output of CTR prediction determines the order
of items shown to users. The Embedding \& MLP paradigm has become a standard
approach for industrial recommendation systems and has been widely deployed.
However, ... | [
{
"version": "v1",
"created": "Sat, 1 Mar 2025 13:43:06 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Zhu",
"Wenqiao",
""
],
[
"Wang",
"Lulu",
""
],
[
"Wu",
"Jun",
""
]
] | TITLE: Addressing Cold-start Problem in Click-Through Rate Prediction via
Supervised Diffusion Modeling
ABSTRACT: Predicting Click-Through Rates is a crucial function within recommendation
and advertising platforms, as the output of CTR prediction determines the order
of items shown to users. The Embedding \& MLP... |
2504.06272 | Kevin Dela Rosa | Kevin Dela Rosa | RAVEN: An Agentic Framework for Multimodal Entity Discovery from
Large-Scale Video Collections | Presented at AI Agent for Information Retrieval: Generating and
Ranking (Agent4IR) @ AAAI 2025
[https://sites.google.com/view/ai4ir/aaai-2025] | null | null | null | cs.IR cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present RAVEN an adaptive AI agent framework designed for multimodal
entity discovery and retrieval in large-scale video collections. Synthesizing
information across visual, audio, and textual modalities, RAVEN autonomously
processes video data to produce structured, actionable representations for
downstream tasks... | [
{
"version": "v1",
"created": "Mon, 3 Mar 2025 08:28:58 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Rosa",
"Kevin Dela",
""
]
] | TITLE: RAVEN: An Agentic Framework for Multimodal Entity Discovery from
Large-Scale Video Collections
ABSTRACT: We present RAVEN an adaptive AI agent framework designed for multimodal
entity discovery and retrieval in large-scale video collections. Synthesizing
information across visual, audio, and textual modali... |
2504.06274 | Ngoc Luyen Le | Ngoc Luyen Le, Marie-H\'el\`ene Abel | Joint Group Profiling and Recommendation via Deep Neural Network-based
Multi-Task Learning | null | null | null | null | cs.IR cs.AI cs.LG | http://creativecommons.org/licenses/by/4.0/ | Group recommender systems aim to generate recommendations that align with the
collective preferences of a group, introducing challenges that differ
significantly from those in individual recommendation scenarios. This paper
presents Joint Group Profiling and Recommendation via Deep Neural Network-based
Multi-Task Lea... | [
{
"version": "v1",
"created": "Wed, 5 Mar 2025 14:28:48 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Le",
"Ngoc Luyen",
""
],
[
"Abel",
"Marie-Hélène",
""
]
] | TITLE: Joint Group Profiling and Recommendation via Deep Neural Network-based
Multi-Task Learning
ABSTRACT: Group recommender systems aim to generate recommendations that align with the
collective preferences of a group, introducing challenges that differ
significantly from those in individual recommendation scen... |
2504.06282 | Jakub Vasicek | Jakub Va\v{s}\'i\v{c}ek, Dafni Skiadopoulou, Ksenia G. Kuznetsova,
Lukas K\"all, Marc Vaudel, Stefan Bruckner | ProHap Explorer: Visualizing Haplotypes in Proteogenomic Datasets | null | null | null | null | q-bio.GN cs.GR | http://creativecommons.org/licenses/by/4.0/ | In mass spectrometry-based proteomics, experts usually project data onto a
single set of reference sequences, overlooking the influence of common
haplotypes (combinations of genetic variants inherited together from a parent).
We recently introduced ProHap, a tool for generating customized protein
haplotype databases.... | [
{
"version": "v1",
"created": "Tue, 25 Mar 2025 14:48:20 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Vašíček",
"Jakub",
""
],
[
"Skiadopoulou",
"Dafni",
""
],
[
"Kuznetsova",
"Ksenia G.",
""
],
[
"Käll",
"Lukas",
""
],
[
"Vaudel",
"Marc",
""
],
[
"Bruckner",
"Stefan",
""
]
] | TITLE: ProHap Explorer: Visualizing Haplotypes in Proteogenomic Datasets
ABSTRACT: In mass spectrometry-based proteomics, experts usually project data onto a
single set of reference sequences, overlooking the influence of common
haplotypes (combinations of genetic variants inherited together from a parent).
We rece... |
2504.06285 | Bryar Hassan Dr. | Bryar A. Hassan, Shko M. Qader, Alla A. Hassan, Joan Lu, Aram M.
Ahmed, Jafar Majidpour, Tarik A. Rashid | Reducing Formal Context Extraction: A Newly Proposed Framework from Big
Corpora | null | null | null | null | cs.CL | http://creativecommons.org/licenses/by/4.0/ | Automating the extraction of concept hierarchies from free text is
advantageous because manual generation is frequently labor- and
resource-intensive. Free result, the whole procedure for concept hierarchy
learning from free text entails several phases, including sentence-level text
processing, sentence splitting, an... | [
{
"version": "v1",
"created": "Tue, 1 Apr 2025 09:24:07 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Hassan",
"Bryar A.",
""
],
[
"Qader",
"Shko M.",
""
],
[
"Hassan",
"Alla A.",
""
],
[
"Lu",
"Joan",
""
],
[
"Ahmed",
"Aram M.",
""
],
[
"Majidpour",
"Jafar",
""
],
[
"Rashid",
"Tarik A.",
""
]
] | TITLE: Reducing Formal Context Extraction: A Newly Proposed Framework from Big
Corpora
ABSTRACT: Automating the extraction of concept hierarchies from free text is
advantageous because manual generation is frequently labor- and
resource-intensive. Free result, the whole procedure for concept hierarchy
learning fr... |
2504.06292 | Hezhe Qiao | Hongbin Liang, Hezhe Qiao, Wei Huang, Qizhou Wang, Mingsheng Shang,
and Lin Chen | Temporal-contextual Event Learning for Pedestrian Crossing Intent
Prediction | Accepted in ICONIP2024 | null | null | null | cs.CV cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Ensuring the safety of vulnerable road users through accurate prediction of
pedestrian crossing intention (PCI) plays a crucial role in the context of
autonomous and assisted driving. Analyzing the set of observation video frames
in ego-view has been widely used in most PCI prediction methods to forecast the
cross in... | [
{
"version": "v1",
"created": "Fri, 4 Apr 2025 10:44:24 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Liang",
"Hongbin",
""
],
[
"Qiao",
"Hezhe",
""
],
[
"Huang",
"Wei",
""
],
[
"Wang",
"Qizhou",
""
],
[
"Shang",
"Mingsheng",
""
],
[
"Chen",
"Lin",
""
]
] | TITLE: Temporal-contextual Event Learning for Pedestrian Crossing Intent
Prediction
ABSTRACT: Ensuring the safety of vulnerable road users through accurate prediction of
pedestrian crossing intention (PCI) plays a crucial role in the context of
autonomous and assisted driving. Analyzing the set of observation vid... |
2504.06306 | Polycarp Nalela | Polycarp Nalela, Deepthi Rao, Praveen Rao | Predicting Survivability of Cancer Patients with Metastatic Patterns
Using Explainable AI | null | null | null | null | q-bio.QM cs.AI | http://creativecommons.org/licenses/by/4.0/ | Cancer remains a leading global health challenge and a major cause of
mortality. This study leverages machine learning (ML) to predict the
survivability of cancer patients with metastatic patterns using the
comprehensive MSK-MET dataset, which includes genomic and clinical data from
25,775 patients across 27 cancer t... | [
{
"version": "v1",
"created": "Mon, 7 Apr 2025 20:48:15 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Nalela",
"Polycarp",
""
],
[
"Rao",
"Deepthi",
""
],
[
"Rao",
"Praveen",
""
]
] | TITLE: Predicting Survivability of Cancer Patients with Metastatic Patterns
Using Explainable AI
ABSTRACT: Cancer remains a leading global health challenge and a major cause of
mortality. This study leverages machine learning (ML) to predict the
survivability of cancer patients with metastatic patterns using the
... |
2504.06312 | Peizhi Niu | Peizhi Niu, Yu-Hsiang Wang, Vishal Rana, Chetan Rupakheti, Abhishek
Pandey, Olgica Milenkovic | DMol: A Schedule-Driven Diffusion Model for Highly Efficient and
Versatile Molecule Generation | null | null | null | null | cs.LG cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We introduce a new graph diffusion model for small molecule generation,
\emph{DMol}, which outperforms the state-of-the-art DiGress model in terms of
validity by roughly $1.5\%$ across all benchmarking datasets while reducing the
number of diffusion steps by at least $10$-fold, and the running time to
roughly one hal... | [
{
"version": "v1",
"created": "Tue, 8 Apr 2025 03:31:21 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Niu",
"Peizhi",
""
],
[
"Wang",
"Yu-Hsiang",
""
],
[
"Rana",
"Vishal",
""
],
[
"Rupakheti",
"Chetan",
""
],
[
"Pandey",
"Abhishek",
""
],
[
"Milenkovic",
"Olgica",
""
]
] | TITLE: DMol: A Schedule-Driven Diffusion Model for Highly Efficient and
Versatile Molecule Generation
ABSTRACT: We introduce a new graph diffusion model for small molecule generation,
\emph{DMol}, which outperforms the state-of-the-art DiGress model in terms of
validity by roughly $1.5\%$ across all benchmarking ... |
2504.06314 | Abdelghani MADDI | Abdelghani Maddi (GEMASS), Jaime Teixeira Da Silva (MIDAP) | Beyond authorship: Analyzing contributions in PLOS ONE and the
challenges of appropriate attribution | null | Journal of Data and Information Science, 2024, 9 (3), pp.88-115 | 10.2478/jdis-2024-0015 | null | cs.DL cs.CY | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Purpose This study aims to evaluate the accuracy of authorship attributions
in scientific publications, focusing on the fairness and precision of
individual contributions within academic works. Design/methodology/approach The
study analyzes 81,823 publications from the journal PLOS ONE , covering the
period from Janu... | [
{
"version": "v1",
"created": "Tue, 8 Apr 2025 06:47:52 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Maddi",
"Abdelghani",
"",
"GEMASS"
],
[
"Da Silva",
"Jaime Teixeira",
"",
"MIDAP"
]
] | TITLE: Beyond authorship: Analyzing contributions in PLOS ONE and the
challenges of appropriate attribution
ABSTRACT: Purpose This study aims to evaluate the accuracy of authorship attributions
in scientific publications, focusing on the fairness and precision of
individual contributions within academic works. De... |
2504.06318 | Mathias Angermaier | Mathias Angermaier and Joao Pinheiro-Neto and Elisabeth Hoeldrich and
Jana Lasser | The Schwurbelarchiv: a German Language Telegram dataset for the Study of
Conspiracy Theories | null | null | null | null | cs.SI | http://creativecommons.org/licenses/by/4.0/ | Sociality borne by language, as is the predominant digital trace on
text-based social media platforms, harbours the raw material for exploring
multiple social phenomena. Distinctively, the messaging service Telegram
provides functionalities that allow for socially interactive as well as
one-to-many communication. Our... | [
{
"version": "v1",
"created": "Tue, 8 Apr 2025 09:11:46 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Angermaier",
"Mathias",
""
],
[
"Pinheiro-Neto",
"Joao",
""
],
[
"Hoeldrich",
"Elisabeth",
""
],
[
"Lasser",
"Jana",
""
]
] | TITLE: The Schwurbelarchiv: a German Language Telegram dataset for the Study of
Conspiracy Theories
ABSTRACT: Sociality borne by language, as is the predominant digital trace on
text-based social media platforms, harbours the raw material for exploring
multiple social phenomena. Distinctively, the messaging servi... |
2504.06323 | Bailey Eccles | Bailey J. Eccles, Leon Wong, Blesson Varghese | Mosaic: Composite Projection Pruning for Resource-efficient LLMs | null | null | null | null | cs.LG cs.AI | http://creativecommons.org/licenses/by/4.0/ | Extensive compute and memory requirements limit the deployment of large
language models (LLMs) on any hardware. Compression methods, such as pruning,
can reduce model size, which in turn reduces resource requirements.
State-of-the-art pruning is based on coarse-grained methods. They are
time-consuming and inherently ... | [
{
"version": "v1",
"created": "Tue, 8 Apr 2025 11:51:35 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Eccles",
"Bailey J.",
""
],
[
"Wong",
"Leon",
""
],
[
"Varghese",
"Blesson",
""
]
] | TITLE: Mosaic: Composite Projection Pruning for Resource-efficient LLMs
ABSTRACT: Extensive compute and memory requirements limit the deployment of large
language models (LLMs) on any hardware. Compression methods, such as pruning,
can reduce model size, which in turn reduces resource requirements.
State-of-the-art... |
2504.06324 | Monika Jotautait\.e | Monika Jotautaite, Mary Phuong, Chatrik Singh Mangat, Maria Angelica
Martinez | From Stability to Inconsistency: A Study of Moral Preferences in LLMs | null | null | null | null | cs.CY cs.AI | http://creativecommons.org/licenses/by/4.0/ | As large language models (LLMs) increasingly integrate into our daily lives,
it becomes crucial to understand their implicit biases and moral tendencies. To
address this, we introduce a Moral Foundations LLM dataset (MFD-LLM) grounded
in Moral Foundations Theory, which conceptualizes human morality through six
core f... | [
{
"version": "v1",
"created": "Tue, 8 Apr 2025 11:52:50 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Jotautaite",
"Monika",
""
],
[
"Phuong",
"Mary",
""
],
[
"Mangat",
"Chatrik Singh",
""
],
[
"Martinez",
"Maria Angelica",
""
]
] | TITLE: From Stability to Inconsistency: A Study of Moral Preferences in LLMs
ABSTRACT: As large language models (LLMs) increasingly integrate into our daily lives,
it becomes crucial to understand their implicit biases and moral tendencies. To
address this, we introduce a Moral Foundations LLM dataset (MFD-LLM) gro... |
2504.06325 | Wenbin Xing | Ronghui Zhang, Wenbin Xing, Mengran Li, Zihan Wang, Junzhou Chen,
Xiaolei Ma, Zhiyuan Liu, Zhengbing He | MM-STFlowNet: A Transportation Hub-Oriented Multi-Mode Passenger Flow
Prediction Method via Spatial-Temporal Dynamic Graph Modeling | null | null | null | null | cs.LG cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Accurate and refined passenger flow prediction is essential for optimizing
the collaborative management of multiple collection and distribution modes in
large-scale transportation hubs. Traditional methods often focus only on the
overall passenger volume, neglecting the interdependence between different
modes within ... | [
{
"version": "v1",
"created": "Tue, 8 Apr 2025 12:00:06 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Zhang",
"Ronghui",
""
],
[
"Xing",
"Wenbin",
""
],
[
"Li",
"Mengran",
""
],
[
"Wang",
"Zihan",
""
],
[
"Chen",
"Junzhou",
""
],
[
"Ma",
"Xiaolei",
""
],
[
"Liu",
"Zhiyuan",
""
],
[
"He",
"Zheng... | TITLE: MM-STFlowNet: A Transportation Hub-Oriented Multi-Mode Passenger Flow
Prediction Method via Spatial-Temporal Dynamic Graph Modeling
ABSTRACT: Accurate and refined passenger flow prediction is essential for optimizing
the collaborative management of multiple collection and distribution modes in
large-scale ... |
2504.06327 | Ali Kashefi | Ali Kashefi, Tapan Mukerji | Physics-informed KAN PointNet: Deep learning for simultaneous solutions
to inverse problems in incompressible flow on numerous irregular geometries | null | null | null | null | cs.LG physics.flu-dyn | http://creativecommons.org/licenses/by/4.0/ | Kolmogorov-Arnold Networks (KANs) have gained attention as a promising
alternative to traditional Multilayer Perceptrons (MLPs) for deep learning
applications in computational physics, especially within the framework of
physics-informed neural networks (PINNs). Physics-informed Kolmogorov-Arnold
Networks (PIKANs) and... | [
{
"version": "v1",
"created": "Tue, 8 Apr 2025 12:31:57 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Kashefi",
"Ali",
""
],
[
"Mukerji",
"Tapan",
""
]
] | TITLE: Physics-informed KAN PointNet: Deep learning for simultaneous solutions
to inverse problems in incompressible flow on numerous irregular geometries
ABSTRACT: Kolmogorov-Arnold Networks (KANs) have gained attention as a promising
alternative to traditional Multilayer Perceptrons (MLPs) for deep learning
app... |
2504.06330 | Hicham Talaoubrid | Hicham Talaoubrid, Anissa Mokraoui, Ismail Ben Ayed, Axel Prouvost,
Sonimith Hang, Monit Korn, R\'emi Harvey | Analyzing the Impact of Low-Rank Adaptation for Cross-Domain Few-Shot
Object Detection in Aerial Images | null | null | null | null | cs.CV cs.AI | http://creativecommons.org/licenses/by/4.0/ | This paper investigates the application of Low-Rank Adaptation (LoRA) to
small models for cross-domain few-shot object detection in aerial images.
Originally designed for large-scale models, LoRA helps mitigate overfitting,
making it a promising approach for resource-constrained settings. We integrate
LoRA into Diffu... | [
{
"version": "v1",
"created": "Tue, 8 Apr 2025 14:10:39 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Talaoubrid",
"Hicham",
""
],
[
"Mokraoui",
"Anissa",
""
],
[
"Ayed",
"Ismail Ben",
""
],
[
"Prouvost",
"Axel",
""
],
[
"Hang",
"Sonimith",
""
],
[
"Korn",
"Monit",
""
],
[
"Harvey",
"Rémi",
""
]
] | TITLE: Analyzing the Impact of Low-Rank Adaptation for Cross-Domain Few-Shot
Object Detection in Aerial Images
ABSTRACT: This paper investigates the application of Low-Rank Adaptation (LoRA) to
small models for cross-domain few-shot object detection in aerial images.
Originally designed for large-scale models, Lo... |
2504.06358 | Yupeng Cheng | Yupeng Cheng, Zi Pong Lim, Sarthak Ketanbhai Modi, Yon Shin Teo, Yushi
Cao, Shang-Wei Lin | Towards Calibration Enhanced Network by Inverse Adversarial Attack | 11 pages | null | null | null | cs.CV | http://creativecommons.org/licenses/by/4.0/ | Test automation has become increasingly important as the complexity of both
design and content in Human Machine Interface (HMI) software continues to grow.
Current standard practice uses Optical Character Recognition (OCR) techniques
to automatically extract textual information from HMI screens for validation.
At pre... | [
{
"version": "v1",
"created": "Tue, 8 Apr 2025 18:13:23 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Cheng",
"Yupeng",
""
],
[
"Lim",
"Zi Pong",
""
],
[
"Modi",
"Sarthak Ketanbhai",
""
],
[
"Teo",
"Yon Shin",
""
],
[
"Cao",
"Yushi",
""
],
[
"Lin",
"Shang-Wei",
""
]
] | TITLE: Towards Calibration Enhanced Network by Inverse Adversarial Attack
ABSTRACT: Test automation has become increasingly important as the complexity of both
design and content in Human Machine Interface (HMI) software continues to grow.
Current standard practice uses Optical Character Recognition (OCR) technique... |
2504.06393 | Rebecca M. M. Hicke | Rebecca M. M. Hicke, Sil Hamilton, and David Mimno | The Zero Body Problem: Probing LLM Use of Sensory Language | null | null | null | null | cs.CL cs.LG | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Sensory language expresses embodied experiences ranging from taste and sound
to excitement and stomachache. This language is of interest to scholars from a
wide range of domains including robotics, narratology, linguistics, and
cognitive science. In this work, we explore whether language models, which are
not embodie... | [
{
"version": "v1",
"created": "Tue, 8 Apr 2025 19:31:37 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Hicke",
"Rebecca M. M.",
""
],
[
"Hamilton",
"Sil",
""
],
[
"Mimno",
"David",
""
]
] | TITLE: The Zero Body Problem: Probing LLM Use of Sensory Language
ABSTRACT: Sensory language expresses embodied experiences ranging from taste and sound
to excitement and stomachache. This language is of interest to scholars from a
wide range of domains including robotics, narratology, linguistics, and
cognitive sc... |
2504.06410 | Huzaifa Arif | Huzaifa Arif, Keerthiram Murugesan, Payel Das, Alex Gittens, Pin-Yu
Chen | PEEL the Layers and Find Yourself: Revisiting Inference-time Data
Leakage for Residual Neural Networks | null | null | null | null | cs.LG cs.CR cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper explores inference-time data leakage risks of deep neural networks
(NNs), where a curious and honest model service provider is interested in
retrieving users' private data inputs solely based on the model inference
results. Particularly, we revisit residual NNs due to their popularity in
computer vision an... | [
{
"version": "v1",
"created": "Tue, 8 Apr 2025 20:11:05 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Arif",
"Huzaifa",
""
],
[
"Murugesan",
"Keerthiram",
""
],
[
"Das",
"Payel",
""
],
[
"Gittens",
"Alex",
""
],
[
"Chen",
"Pin-Yu",
""
]
] | TITLE: PEEL the Layers and Find Yourself: Revisiting Inference-time Data
Leakage for Residual Neural Networks
ABSTRACT: This paper explores inference-time data leakage risks of deep neural networks
(NNs), where a curious and honest model service provider is interested in
retrieving users' private data inputs sole... |
2504.06417 | Ildi Alla | Ildi Alla, Selma Yahia, Valeria Loscri | TRIDENT: Tri-modal Real-time Intrusion Detection Engine for New Targets | null | null | null | null | cs.CR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The increasing availability of drones and their potential for malicious
activities pose significant privacy and security risks, necessitating fast and
reliable detection in real-world environments. However, existing drone
detection systems often struggle in real-world settings due to environmental
noise and sensor li... | [
{
"version": "v1",
"created": "Tue, 8 Apr 2025 20:33:43 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Alla",
"Ildi",
""
],
[
"Yahia",
"Selma",
""
],
[
"Loscri",
"Valeria",
""
]
] | TITLE: TRIDENT: Tri-modal Real-time Intrusion Detection Engine for New Targets
ABSTRACT: The increasing availability of drones and their potential for malicious
activities pose significant privacy and security risks, necessitating fast and
reliable detection in real-world environments. However, existing drone
detec... |
2504.06422 | Adam McArthur | Adam McArthur, Stephanie Wichuk, Stephen Burnside, Andrew Kirby,
Alexander Scammon, Damian Sol, Abhilash Hareendranathan, Jacob L. Jaremko | Retuve: Automated Multi-Modality Analysis of Hip Dysplasia with Open
Source AI | 12 pages, 8 figures, submitted to Software Impacts | null | null | null | eess.IV cs.CV | http://creativecommons.org/licenses/by/4.0/ | Developmental dysplasia of the hip (DDH) poses significant diagnostic
challenges, hindering timely intervention. Current screening methodologies lack
standardization, and AI-driven studies suffer from reproducibility issues due
to limited data and code availability. To address these limitations, we
introduce Retuve, ... | [
{
"version": "v1",
"created": "Tue, 8 Apr 2025 20:41:21 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"McArthur",
"Adam",
""
],
[
"Wichuk",
"Stephanie",
""
],
[
"Burnside",
"Stephen",
""
],
[
"Kirby",
"Andrew",
""
],
[
"Scammon",
"Alexander",
""
],
[
"Sol",
"Damian",
""
],
[
"Hareendranathan",
"Abhilash",
"... | TITLE: Retuve: Automated Multi-Modality Analysis of Hip Dysplasia with Open
Source AI
ABSTRACT: Developmental dysplasia of the hip (DDH) poses significant diagnostic
challenges, hindering timely intervention. Current screening methodologies lack
standardization, and AI-driven studies suffer from reproducibility i... |
2504.06432 | Sibo Dong | Rupayan Mallick, Sibo Dong, Nataniel Ruiz, Sarah Adel Bargal | D-Feat Occlusions: Diffusion Features for Robustness to Partial Visual
Occlusions in Object Recognition | null | null | null | null | cs.CV | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Applications of diffusion models for visual tasks have been quite noteworthy.
This paper targets making classification models more robust to occlusions for
the task of object recognition by proposing a pipeline that utilizes a frozen
diffusion model. Diffusion features have demonstrated success in image
generation an... | [
{
"version": "v1",
"created": "Tue, 8 Apr 2025 21:05:29 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Mallick",
"Rupayan",
""
],
[
"Dong",
"Sibo",
""
],
[
"Ruiz",
"Nataniel",
""
],
[
"Bargal",
"Sarah Adel",
""
]
] | TITLE: D-Feat Occlusions: Diffusion Features for Robustness to Partial Visual
Occlusions in Object Recognition
ABSTRACT: Applications of diffusion models for visual tasks have been quite noteworthy.
This paper targets making classification models more robust to occlusions for
the task of object recognition by pro... |
2504.06460 | Hao Yan | Sai Adith Senthil Kumar, Hao Yan, Saipavan Perepa, Murong Yue, Ziyu
Yao | Can LLMs Simulate Personas with Reversed Performance? A Benchmark for
Counterfactual Instruction Following | null | null | null | null | cs.CL | http://creativecommons.org/licenses/by/4.0/ | Large Language Models (LLMs) are now increasingly widely used to simulate
personas in virtual environments, leveraging their instruction-following
capability. However, we discovered that even state-of-the-art LLMs cannot
simulate personas with reversed performance (e.g., student personas with low
proficiency in educa... | [
{
"version": "v1",
"created": "Tue, 8 Apr 2025 22:00:32 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Kumar",
"Sai Adith Senthil",
""
],
[
"Yan",
"Hao",
""
],
[
"Perepa",
"Saipavan",
""
],
[
"Yue",
"Murong",
""
],
[
"Yao",
"Ziyu",
""
]
] | TITLE: Can LLMs Simulate Personas with Reversed Performance? A Benchmark for
Counterfactual Instruction Following
ABSTRACT: Large Language Models (LLMs) are now increasingly widely used to simulate
personas in virtual environments, leveraging their instruction-following
capability. However, we discovered that eve... |
2504.06492 | Mingchen Li | Mingchen Li, Di Zhuang, Keyu Chen, Dumindu Samaraweera, and Morris
Chang | Exploiting Meta-Learning-based Poisoning Attacks for Graph Link
Prediction | null | null | null | null | cs.LG cs.AI | http://creativecommons.org/licenses/by/4.0/ | Link prediction in graph data utilizes various algorithms and machine
learning/deep learning models to predict potential relationships between graph
nodes. This technique has found widespread use in numerous real-world
applications, including recommendation systems, community networks, and
biological structures. Howe... | [
{
"version": "v1",
"created": "Tue, 8 Apr 2025 23:36:29 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Li",
"Mingchen",
""
],
[
"Zhuang",
"Di",
""
],
[
"Chen",
"Keyu",
""
],
[
"Samaraweera",
"Dumindu",
""
],
[
"Chang",
"Morris",
""
]
] | TITLE: Exploiting Meta-Learning-based Poisoning Attacks for Graph Link
Prediction
ABSTRACT: Link prediction in graph data utilizes various algorithms and machine
learning/deep learning models to predict potential relationships between graph
nodes. This technique has found widespread use in numerous real-world
app... |
2504.06497 | Minati Rath | Minati Rath, Hema Date | Continuous-Variable Quantum Encoding Techniques: A Comparative Study of
Embedding Techniques and Their Impact on Machine Learning Performance | null | null | null | null | quant-ph cs.AI | http://creativecommons.org/licenses/by/4.0/ | This study explores the intersection of continuous-variable quantum computing
(CVQC) and classical machine learning, focusing on CVQC data encoding
techniques, including Displacement encoding and squeezing encoding, alongside
Instantaneous Quantum Polynomial (IQP) encoding from discrete quantum
computing. We perform ... | [
{
"version": "v1",
"created": "Wed, 9 Apr 2025 00:00:45 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Rath",
"Minati",
""
],
[
"Date",
"Hema",
""
]
] | TITLE: Continuous-Variable Quantum Encoding Techniques: A Comparative Study of
Embedding Techniques and Their Impact on Machine Learning Performance
ABSTRACT: This study explores the intersection of continuous-variable quantum computing
(CVQC) and classical machine learning, focusing on CVQC data encoding
techniq... |
2504.06504 | Xiaohang Yang | Xiaohang Yang, Qing Wang, Jiahao Yang, Gregory Slabaugh, Shanxin Yuan | STaR: Seamless Spatial-Temporal Aware Motion Retargeting with
Penetration and Consistency Constraints | 12 pages, 9 figures; | null | null | null | cs.CV | http://creativecommons.org/licenses/by-sa/4.0/ | Motion retargeting seeks to faithfully replicate the spatio-temporal motion
characteristics of a source character onto a target character with a different
body shape. Apart from motion semantics preservation, ensuring geometric
plausibility and maintaining temporal consistency are also crucial for
effective motion re... | [
{
"version": "v1",
"created": "Wed, 9 Apr 2025 00:37:08 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Yang",
"Xiaohang",
""
],
[
"Wang",
"Qing",
""
],
[
"Yang",
"Jiahao",
""
],
[
"Slabaugh",
"Gregory",
""
],
[
"Yuan",
"Shanxin",
""
]
] | TITLE: STaR: Seamless Spatial-Temporal Aware Motion Retargeting with
Penetration and Consistency Constraints
ABSTRACT: Motion retargeting seeks to faithfully replicate the spatio-temporal motion
characteristics of a source character onto a target character with a different
body shape. Apart from motion semantics ... |
2504.06511 | Tianwu Zhou | Liu Shi, Tianwu Zhou, Wei Xu, Li Liu, Zhexin Cui, Shaoyi Liang,
Haoxing Niu, Yichong Tian, Jianwei Guo | GTS-LUM: Reshaping User Behavior Modeling with LLMs in
Telecommunications Industry | null | null | null | null | cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | As telecommunication service providers shifting their focus to analyzing user
behavior for package design and marketing interventions, a critical challenge
lies in developing a unified, end-to-end framework capable of modeling
long-term and periodic user behavior sequences with diverse time granularities,
multi-modal... | [
{
"version": "v1",
"created": "Wed, 9 Apr 2025 01:12:07 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Shi",
"Liu",
""
],
[
"Zhou",
"Tianwu",
""
],
[
"Xu",
"Wei",
""
],
[
"Liu",
"Li",
""
],
[
"Cui",
"Zhexin",
""
],
[
"Liang",
"Shaoyi",
""
],
[
"Niu",
"Haoxing",
""
],
[
"Tian",
"Yichong",
""
... | TITLE: GTS-LUM: Reshaping User Behavior Modeling with LLMs in
Telecommunications Industry
ABSTRACT: As telecommunication service providers shifting their focus to analyzing user
behavior for package design and marketing interventions, a critical challenge
lies in developing a unified, end-to-end framework capable... |
2504.06514 | Ming Li | Chenrui Fan, Ming Li, Lichao Sun, Tianyi Zhou | Missing Premise exacerbates Overthinking: Are Reasoning Models losing
Critical Thinking Skill? | null | null | null | null | cs.AI cs.CL cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We find that the response length of reasoning LLMs, whether trained by
reinforcement learning or supervised learning, drastically increases for
ill-posed questions with missing premises (MiP), ending up with redundant and
ineffective thinking. This newly introduced scenario exacerbates the general
overthinking issue ... | [
{
"version": "v1",
"created": "Wed, 9 Apr 2025 01:25:27 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Fan",
"Chenrui",
""
],
[
"Li",
"Ming",
""
],
[
"Sun",
"Lichao",
""
],
[
"Zhou",
"Tianyi",
""
]
] | TITLE: Missing Premise exacerbates Overthinking: Are Reasoning Models losing
Critical Thinking Skill?
ABSTRACT: We find that the response length of reasoning LLMs, whether trained by
reinforcement learning or supervised learning, drastically increases for
ill-posed questions with missing premises (MiP), ending up... |
2504.06521 | Songze Li | Songze Li, Tonghua Su, Xu-Yao Zhang, Qixing Xu, Zhongjie Wang | DUKAE: DUal-level Knowledge Accumulation and Ensemble for Pre-Trained
Model-Based Continual Learning | null | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Pre-trained model-based continual learning (PTMCL) has garnered growing
attention, as it enables more rapid acquisition of new knowledge by leveraging
the extensive foundational understanding inherent in pre-trained model (PTM).
Most existing PTMCL methods use Parameter-Efficient Fine-Tuning (PEFT) to learn
new knowl... | [
{
"version": "v1",
"created": "Wed, 9 Apr 2025 01:40:38 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Li",
"Songze",
""
],
[
"Su",
"Tonghua",
""
],
[
"Zhang",
"Xu-Yao",
""
],
[
"Xu",
"Qixing",
""
],
[
"Wang",
"Zhongjie",
""
]
] | TITLE: DUKAE: DUal-level Knowledge Accumulation and Ensemble for Pre-Trained
Model-Based Continual Learning
ABSTRACT: Pre-trained model-based continual learning (PTMCL) has garnered growing
attention, as it enables more rapid acquisition of new knowledge by leveraging
the extensive foundational understanding inhe... |
2504.06527 | Xyu Liu | Xinyu Liu, Xiaoguang Lin, Xiang Liu, Yong Yang, Hongqian Wang, Qilong
Sun | TSP-OCS: A Time-Series Prediction for Optimal Camera Selection in
Multi-Viewpoint Surgical Video Analysis | null | null | null | null | cs.CV cs.AI | http://creativecommons.org/licenses/by/4.0/ | Recording the open surgery process is essential for educational and medical
evaluation purposes; however, traditional single-camera methods often face
challenges such as occlusions caused by the surgeon's head and body, as well as
limitations due to fixed camera angles, which reduce comprehensibility of the
video con... | [
{
"version": "v1",
"created": "Wed, 9 Apr 2025 02:07:49 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Liu",
"Xinyu",
""
],
[
"Lin",
"Xiaoguang",
""
],
[
"Liu",
"Xiang",
""
],
[
"Yang",
"Yong",
""
],
[
"Wang",
"Hongqian",
""
],
[
"Sun",
"Qilong",
""
]
] | TITLE: TSP-OCS: A Time-Series Prediction for Optimal Camera Selection in
Multi-Viewpoint Surgical Video Analysis
ABSTRACT: Recording the open surgery process is essential for educational and medical
evaluation purposes; however, traditional single-camera methods often face
challenges such as occlusions caused by ... |
2504.06529 | Khai Phan Tran | Khai Phan Tran, Xue Li | CDER: Collaborative Evidence Retrieval for Document-level Relation
Extraction | Published at ACIIDS 2024 | null | 10.1007/978-981-97-4982-9_3 | null | cs.CL | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Document-level Relation Extraction (DocRE) involves identifying relations
between entities across multiple sentences in a document. Evidence sentences,
crucial for precise entity pair relationships identification, enhance focus on
essential text segments, improving DocRE performance. However, existing
evidence retrie... | [
{
"version": "v1",
"created": "Wed, 9 Apr 2025 02:10:21 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Tran",
"Khai Phan",
""
],
[
"Li",
"Xue",
""
]
] | TITLE: CDER: Collaborative Evidence Retrieval for Document-level Relation
Extraction
ABSTRACT: Document-level Relation Extraction (DocRE) involves identifying relations
between entities across multiple sentences in a document. Evidence sentences,
crucial for precise entity pair relationships identification, enhan... |
2504.06533 | Zhouyang Liu | Zhouyang Liu, Ning Liu, Yixin Chen, Jiezhong He, Dongsheng Li | Flexible Graph Similarity Computation With A Proactive Optimization
Strategy | null | null | null | null | cs.LG cs.AI cs.DS | http://creativecommons.org/licenses/by/4.0/ | Graph Edit Distance (GED) is an important similarity measure in graph
retrieval, which quantifies the minimum cost of transforming one graph into
another through edit operations, and offers flexibility by allowing
customizable operation costs. Recent learning-based approaches approximate GEDs
with the distances betwe... | [
{
"version": "v1",
"created": "Wed, 9 Apr 2025 02:16:46 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Liu",
"Zhouyang",
""
],
[
"Liu",
"Ning",
""
],
[
"Chen",
"Yixin",
""
],
[
"He",
"Jiezhong",
""
],
[
"Li",
"Dongsheng",
""
]
] | TITLE: Flexible Graph Similarity Computation With A Proactive Optimization
Strategy
ABSTRACT: Graph Edit Distance (GED) is an important similarity measure in graph
retrieval, which quantifies the minimum cost of transforming one graph into
another through edit operations, and offers flexibility by allowing
custom... |
2504.06536 | Happy Buzaaba | Happy Buzaaba, Alexander Wettig, David Ifeoluwa Adelani, Christiane
Fellbaum | Lugha-Llama: Adapting Large Language Models for African Languages | null | null | null | null | cs.CL cs.AI cs.LG | http://creativecommons.org/licenses/by/4.0/ | Large language models (LLMs) have achieved impressive results in a wide range
of natural language applications. However, they often struggle to recognize
low-resource languages, in particular African languages, which are not well
represented in large training corpora. In this paper, we consider how to adapt
LLMs to l... | [
{
"version": "v1",
"created": "Wed, 9 Apr 2025 02:25:53 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Buzaaba",
"Happy",
""
],
[
"Wettig",
"Alexander",
""
],
[
"Adelani",
"David Ifeoluwa",
""
],
[
"Fellbaum",
"Christiane",
""
]
] | TITLE: Lugha-Llama: Adapting Large Language Models for African Languages
ABSTRACT: Large language models (LLMs) have achieved impressive results in a wide range
of natural language applications. However, they often struggle to recognize
low-resource languages, in particular African languages, which are not well
rep... |
2504.06543 | Wei Huang | Wei Huang, Meiyu Liang, Peining Li, Xu Hou, Yawen Li, Junping Du, Zhe
Xue, Zeli Guan | DiffusionCom: Structure-Aware Multimodal Diffusion Model for Multimodal
Knowledge Graph Completion | 11 pages, 6 figures | null | null | null | cs.IR | http://creativecommons.org/licenses/by/4.0/ | Most current MKGC approaches are predominantly based on discriminative models
that maximize conditional likelihood. These approaches struggle to efficiently
capture the complex connections in real-world knowledge graphs, thereby
limiting their overall performance. To address this issue, we propose a
structure-aware m... | [
{
"version": "v1",
"created": "Wed, 9 Apr 2025 02:50:37 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Huang",
"Wei",
""
],
[
"Liang",
"Meiyu",
""
],
[
"Li",
"Peining",
""
],
[
"Hou",
"Xu",
""
],
[
"Li",
"Yawen",
""
],
[
"Du",
"Junping",
""
],
[
"Xue",
"Zhe",
""
],
[
"Guan",
"Zeli",
""
]
] | TITLE: DiffusionCom: Structure-Aware Multimodal Diffusion Model for Multimodal
Knowledge Graph Completion
ABSTRACT: Most current MKGC approaches are predominantly based on discriminative models
that maximize conditional likelihood. These approaches struggle to efficiently
capture the complex connections in real-w... |
2504.06544 | Yue Cheng | Weiwei Xing and Yue Cheng and Hongzhu Yi and Xiaohui Gao and Xiang Wei
and Xiaoyu Guo and Yuming Zhang and Xinyu Pang | LCGC: Learning from Consistency Gradient Conflicting for
Class-Imbalanced Semi-Supervised Debiasing | This paper has been accepted by AAAI 2025 | null | null | null | cs.CV | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Classifiers often learn to be biased corresponding to the class-imbalanced
dataset, especially under the semi-supervised learning (SSL) set. While
previous work tries to appropriately re-balance the classifiers by subtracting
a class-irrelevant image's logit, but lacks a firm theoretical basis. We
theoretically analy... | [
{
"version": "v1",
"created": "Wed, 9 Apr 2025 02:57:53 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Xing",
"Weiwei",
""
],
[
"Cheng",
"Yue",
""
],
[
"Yi",
"Hongzhu",
""
],
[
"Gao",
"Xiaohui",
""
],
[
"Wei",
"Xiang",
""
],
[
"Guo",
"Xiaoyu",
""
],
[
"Zhang",
"Yuming",
""
],
[
"Pang",
"Xinyu",
... | TITLE: LCGC: Learning from Consistency Gradient Conflicting for
Class-Imbalanced Semi-Supervised Debiasing
ABSTRACT: Classifiers often learn to be biased corresponding to the class-imbalanced
dataset, especially under the semi-supervised learning (SSL) set. While
previous work tries to appropriately re-balance th... |
2504.06559 | Ali Eslamian | Ali Eslamian, Alireza Afzal Aghaei and Qiang Cheng | TabKAN: Advancing Tabular Data Analysis using Kolmograv-Arnold Network | 27 pages, 12 figures, 13 tables | null | null | null | cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Tabular data analysis presents unique challenges due to its heterogeneous
feature types, missing values, and complex interactions. While traditional
machine learning methods, such as gradient boosting, often outperform deep
learning approaches, recent advancements in neural architectures offer
promising alternatives.... | [
{
"version": "v1",
"created": "Wed, 9 Apr 2025 03:46:10 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Eslamian",
"Ali",
""
],
[
"Aghaei",
"Alireza Afzal",
""
],
[
"Cheng",
"Qiang",
""
]
] | TITLE: TabKAN: Advancing Tabular Data Analysis using Kolmograv-Arnold Network
ABSTRACT: Tabular data analysis presents unique challenges due to its heterogeneous
feature types, missing values, and complex interactions. While traditional
machine learning methods, such as gradient boosting, often outperform deep
lear... |
2504.06561 | Xiaohang Jiang | Xiao-Hang Jiang, Yang Ai, Rui-Chen Zheng, Zhen-Hua Ling | A Streamable Neural Audio Codec with Residual Scalar-Vector Quantization
for Real-Time Communication | Accepted by IEEE Signal Processing Letters | null | null | null | cs.SD | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper proposes StreamCodec, a streamable neural audio codec designed for
real-time communication. StreamCodec adopts a fully causal, symmetric
encoder-decoder structure and operates in the modified discrete cosine
transform (MDCT) domain, aiming for low-latency inference and real-time
efficient generation. To im... | [
{
"version": "v1",
"created": "Wed, 9 Apr 2025 03:49:00 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Jiang",
"Xiao-Hang",
""
],
[
"Ai",
"Yang",
""
],
[
"Zheng",
"Rui-Chen",
""
],
[
"Ling",
"Zhen-Hua",
""
]
] | TITLE: A Streamable Neural Audio Codec with Residual Scalar-Vector Quantization
for Real-Time Communication
ABSTRACT: This paper proposes StreamCodec, a streamable neural audio codec designed for
real-time communication. StreamCodec adopts a fully causal, symmetric
encoder-decoder structure and operates in the mo... |
2504.06578 | Rahul Singh Maharjan | Rahul Singh Maharjan, Marta Romeo, Angelo Cangelosi | Attributes-aware Visual Emotion Representation Learning | 9 pages, 3 figures | null | null | null | cs.CV cs.AI cs.MM | http://creativecommons.org/licenses/by/4.0/ | Visual emotion analysis or recognition has gained considerable attention due
to the growing interest in understanding how images can convey rich semantics
and evoke emotions in human perception. However, visual emotion analysis poses
distinctive challenges compared to traditional vision tasks, especially due to
the i... | [
{
"version": "v1",
"created": "Wed, 9 Apr 2025 05:00:43 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Maharjan",
"Rahul Singh",
""
],
[
"Romeo",
"Marta",
""
],
[
"Cangelosi",
"Angelo",
""
]
] | TITLE: Attributes-aware Visual Emotion Representation Learning
ABSTRACT: Visual emotion analysis or recognition has gained considerable attention due
to the growing interest in understanding how images can convey rich semantics
and evoke emotions in human perception. However, visual emotion analysis poses
distincti... |
2504.06580 | Joochan Kim | Joochan Kim, Minjoon Jung, Byoung-Tak Zhang | Exploring Ordinal Bias in Action Recognition for Instructional Videos | Accepted to SCSL @ ICLR 2025 | null | null | null | cs.CV cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Action recognition models have achieved promising results in understanding
instructional videos. However, they often rely on dominant, dataset-specific
action sequences rather than true video comprehension, a problem that we define
as ordinal bias. To address this issue, we propose two effective video
manipulation me... | [
{
"version": "v1",
"created": "Wed, 9 Apr 2025 05:03:51 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Kim",
"Joochan",
""
],
[
"Jung",
"Minjoon",
""
],
[
"Zhang",
"Byoung-Tak",
""
]
] | TITLE: Exploring Ordinal Bias in Action Recognition for Instructional Videos
ABSTRACT: Action recognition models have achieved promising results in understanding
instructional videos. However, they often rely on dominant, dataset-specific
action sequences rather than true video comprehension, a problem that we defi... |
2504.06584 | Junrui Zhang | Junrui Zhang, Chenjie Wang, Jie Peng, Haoyu Li, Jianmin Ji, Yu Zhang,
and Yanyong Zhang | CAFE-AD: Cross-Scenario Adaptive Feature Enhancement for Trajectory
Planning in Autonomous Driving | ICRA 2025; first two authors contributed equally | null | null | null | cs.RO cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Imitation learning based planning tasks on the nuPlan dataset have gained
great interest due to their potential to generate human-like driving behaviors.
However, open-loop training on the nuPlan dataset tends to cause causal
confusion during closed-loop testing, and the dataset also presents a long-tail
distribution... | [
{
"version": "v1",
"created": "Wed, 9 Apr 2025 05:16:29 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Zhang",
"Junrui",
""
],
[
"Wang",
"Chenjie",
""
],
[
"Peng",
"Jie",
""
],
[
"Li",
"Haoyu",
""
],
[
"Ji",
"Jianmin",
""
],
[
"Zhang",
"Yu",
""
],
[
"Zhang",
"Yanyong",
""
]
] | TITLE: CAFE-AD: Cross-Scenario Adaptive Feature Enhancement for Trajectory
Planning in Autonomous Driving
ABSTRACT: Imitation learning based planning tasks on the nuPlan dataset have gained
great interest due to their potential to generate human-like driving behaviors.
However, open-loop training on the nuPlan da... |
2504.06588 | Yiheng Xie | Yiheng Xie, Lucien Werner, Kaibo Chen, Thuy-Linh Le, Christine Ortega,
Steven Low | A Digital Twin of an Electrical Distribution Grid: SoCal 28-Bus Dataset | null | null | null | null | eess.SY cs.SY | http://creativecommons.org/licenses/by-nc-nd/4.0/ | We provide an open-access dataset of phasor & waveform measurement units
(PMUs/WMUs) of a real-world electrical distribution network. The network
consists of diverse sets of generation resources (including solar panels, fuel
cells, natural gas generators, and utility interconnections), loads (including
large-scale el... | [
{
"version": "v1",
"created": "Wed, 9 Apr 2025 05:35:07 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Xie",
"Yiheng",
""
],
[
"Werner",
"Lucien",
""
],
[
"Chen",
"Kaibo",
""
],
[
"Le",
"Thuy-Linh",
""
],
[
"Ortega",
"Christine",
""
],
[
"Low",
"Steven",
""
]
] | TITLE: A Digital Twin of an Electrical Distribution Grid: SoCal 28-Bus Dataset
ABSTRACT: We provide an open-access dataset of phasor & waveform measurement units
(PMUs/WMUs) of a real-world electrical distribution network. The network
consists of diverse sets of generation resources (including solar panels, fuel
ce... |
2504.06607 | Onkar Krishna | Onkar Krishna and Hiroki Ohashi | Visually Similar Pair Alignment for Robust Cross-Domain Object Detection | 15 pages, Journal paper submission | null | null | null | cs.CV | http://creativecommons.org/licenses/by/4.0/ | Domain gaps between training data (source) and real-world environments
(target) often degrade the performance of object detection models. Most
existing methods aim to bridge this gap by aligning features across source and
target domains but often fail to account for visual differences, such as color
or orientation, i... | [
{
"version": "v1",
"created": "Wed, 9 Apr 2025 06:11:11 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Krishna",
"Onkar",
""
],
[
"Ohashi",
"Hiroki",
""
]
] | TITLE: Visually Similar Pair Alignment for Robust Cross-Domain Object Detection
ABSTRACT: Domain gaps between training data (source) and real-world environments
(target) often degrade the performance of object detection models. Most
existing methods aim to bridge this gap by aligning features across source and
targ... |
2504.06608 | Jiajun Chen | Jiajun Chen, Hongpeng Yin, Yifu Yang | A Cross-Domain Few-Shot Learning Method Based on Domain Knowledge
Mapping | null | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In task-based few-shot learning paradigms, it is commonly assumed that
different tasks are independently and identically distributed (i.i.d.).
However, in real-world scenarios, the distribution encountered in few-shot
learning can significantly differ from the distribution of existing data. Thus,
how to effectively l... | [
{
"version": "v1",
"created": "Wed, 9 Apr 2025 06:11:55 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Chen",
"Jiajun",
""
],
[
"Yin",
"Hongpeng",
""
],
[
"Yang",
"Yifu",
""
]
] | TITLE: A Cross-Domain Few-Shot Learning Method Based on Domain Knowledge
Mapping
ABSTRACT: In task-based few-shot learning paradigms, it is commonly assumed that
different tasks are independently and identically distributed (i.i.d.).
However, in real-world scenarios, the distribution encountered in few-shot
learn... |
2504.06610 | Hacer Yalim Keles | Sumeyye Meryem Tasyurek and Tugce Kiziltepe and Hacer Yalim Keles | Disentangle and Regularize: Sign Language Production with
Articulator-Based Disentanglement and Channel-Aware Regularization | 11 pages, 4 figures, 1 table | null | null | null | cs.LG cs.CV | http://creativecommons.org/licenses/by-nc-nd/4.0/ | In this work, we propose a simple gloss-free, transformer-based sign language
production (SLP) framework that directly maps spoken-language text to sign pose
sequences. We first train a pose autoencoder that encodes sign poses into a
compact latent space using an articulator-based disentanglement strategy, where
feat... | [
{
"version": "v1",
"created": "Wed, 9 Apr 2025 06:14:19 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Tasyurek",
"Sumeyye Meryem",
""
],
[
"Kiziltepe",
"Tugce",
""
],
[
"Keles",
"Hacer Yalim",
""
]
] | TITLE: Disentangle and Regularize: Sign Language Production with
Articulator-Based Disentanglement and Channel-Aware Regularization
ABSTRACT: In this work, we propose a simple gloss-free, transformer-based sign language
production (SLP) framework that directly maps spoken-language text to sign pose
sequences. We ... |
2504.06622 | Diksha Sharma | Diksha Sharma, Vivek Balasaheb Sabale, Thirumalai M., Atul Kumar | Quantum neural networks facilitating quantum state classification | null | null | null | null | quant-ph cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The classification of quantum states into distinct classes poses a
significant challenge. In this study, we address this problem using quantum
neural networks in combination with a problem-inspired circuit and customised
as well as predefined ans\"{a}tz. To facilitate the resource-efficient quantum
state classificati... | [
{
"version": "v1",
"created": "Wed, 9 Apr 2025 06:42:32 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Sharma",
"Diksha",
""
],
[
"Sabale",
"Vivek Balasaheb",
""
],
[
"M.",
"Thirumalai",
""
],
[
"Kumar",
"Atul",
""
]
] | TITLE: Quantum neural networks facilitating quantum state classification
ABSTRACT: The classification of quantum states into distinct classes poses a
significant challenge. In this study, we address this problem using quantum
neural networks in combination with a problem-inspired circuit and customised
as well as p... |
2504.06633 | Zhelin Xu | Zhelin Xu, Atsushi Matsumura | A Serendipitous Recommendation System Considering User Curiosity | 15 pages, 3 figures, accepted as a full paper at iiWAS 2024 | null | 10.1007/978-3-031-78093-6_3 | null | cs.IR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | To address the problem of narrow recommendation ranges caused by an emphasis
on prediction accuracy, serendipitous recommendations, which consider both
usefulness and unexpectedness, have attracted attention. However, realizing
serendipitous recommendations is challenging due to the varying proportions of
usefulness ... | [
{
"version": "v1",
"created": "Wed, 9 Apr 2025 07:15:06 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Xu",
"Zhelin",
""
],
[
"Matsumura",
"Atsushi",
""
]
] | TITLE: A Serendipitous Recommendation System Considering User Curiosity
ABSTRACT: To address the problem of narrow recommendation ranges caused by an emphasis
on prediction accuracy, serendipitous recommendations, which consider both
usefulness and unexpectedness, have attracted attention. However, realizing
serend... |
2504.06634 | Junyoung Kim | Junyoung Kim, Youngrok Kim, Siyeol Jung, Donghyun Min | Crafting Query-Aware Selective Attention for Single Image
Super-Resolution | 10 pages, 5 figures, 4 tables | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Single Image Super-Resolution (SISR) reconstructs high-resolution images from
low-resolution inputs, enhancing image details. While Vision Transformer
(ViT)-based models improve SISR by capturing long-range dependencies, they
suffer from quadratic computational costs or employ selective attention
mechanisms that do n... | [
{
"version": "v1",
"created": "Wed, 9 Apr 2025 07:17:29 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Kim",
"Junyoung",
""
],
[
"Kim",
"Youngrok",
""
],
[
"Jung",
"Siyeol",
""
],
[
"Min",
"Donghyun",
""
]
] | TITLE: Crafting Query-Aware Selective Attention for Single Image
Super-Resolution
ABSTRACT: Single Image Super-Resolution (SISR) reconstructs high-resolution images from
low-resolution inputs, enhancing image details. While Vision Transformer
(ViT)-based models improve SISR by capturing long-range dependencies, t... |
2504.06637 | Chenghao Ma | Chenghao Ma, Haihong E., Junpeng Ding, Jun Zhang, Ziyan Ma, Huang
Qing, Bofei Gao, Liang Chen, Meina Song | SCI-Reason: A Dataset with Chain-of-Thought Rationales for Complex
Multimodal Reasoning in Academic Areas | Submitted to ICCV 2025. 11 pages (including references) | null | null | null | cs.MM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Large Language Models (LLMs) and Large Multimodal Models (LMMs) demonstrate
impressive problem-solving skills in many tasks and domains. However, their
ability to reason with complex images in academic domains has not been
systematically investigated. To bridge this gap, we present SCI-Reason, a
dataset for complex m... | [
{
"version": "v1",
"created": "Wed, 9 Apr 2025 07:26:24 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Ma",
"Chenghao",
""
],
[
"E.",
"Haihong",
""
],
[
"Ding",
"Junpeng",
""
],
[
"Zhang",
"Jun",
""
],
[
"Ma",
"Ziyan",
""
],
[
"Qing",
"Huang",
""
],
[
"Gao",
"Bofei",
""
],
[
"Chen",
"Liang",
... | TITLE: SCI-Reason: A Dataset with Chain-of-Thought Rationales for Complex
Multimodal Reasoning in Academic Areas
ABSTRACT: Large Language Models (LLMs) and Large Multimodal Models (LMMs) demonstrate
impressive problem-solving skills in many tasks and domains. However, their
ability to reason with complex images i... |
2504.06638 | Hu Cui | Hu Cui, Tessai Hayama | HGMamba: Enhancing 3D Human Pose Estimation with a HyperGCN-Mamba
Network | accepted by IJCNN2025 | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | 3D human pose lifting is a promising research area that leverages estimated
and ground-truth 2D human pose data for training. While existing approaches
primarily aim to enhance the performance of estimated 2D poses, they often
struggle when applied to ground-truth 2D pose data. We observe that achieving
accurate 3D p... | [
{
"version": "v1",
"created": "Wed, 9 Apr 2025 07:28:19 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Cui",
"Hu",
""
],
[
"Hayama",
"Tessai",
""
]
] | TITLE: HGMamba: Enhancing 3D Human Pose Estimation with a HyperGCN-Mamba
Network
ABSTRACT: 3D human pose lifting is a promising research area that leverages estimated
and ground-truth 2D human pose data for training. While existing approaches
primarily aim to enhance the performance of estimated 2D poses, they of... |
2504.06639 | Suvam Singh | Suvam Singh, Zolt\'an Harman, and Christoph H. Keitel | Dielectronic recombination studies of ions relevant to kilonovae and
non-LTE plasma | null | null | null | null | astro-ph.HE physics.atom-ph | http://creativecommons.org/licenses/by/4.0/ | This study presents calculations of rate coefficients, resonance strengths,
and cross sections for the dielectronic recombination (DR) of Y^+, Sr^+, Te^2+,
and Ce^2+--low-charge ions relevant to kilonovae and non-local thermodynamic
equilibrium (non-LTE) plasmas. Using relativistic atomic structure methods, we
comput... | [
{
"version": "v1",
"created": "Wed, 9 Apr 2025 07:30:19 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Singh",
"Suvam",
""
],
[
"Harman",
"Zoltán",
""
],
[
"Keitel",
"Christoph H.",
""
]
] | TITLE: Dielectronic recombination studies of ions relevant to kilonovae and
non-LTE plasma
ABSTRACT: This study presents calculations of rate coefficients, resonance strengths,
and cross sections for the dielectronic recombination (DR) of Y^+, Sr^+, Te^2+,
and Ce^2+--low-charge ions relevant to kilonovae and non-... |
2504.06649 | Songwei Zhao | Songwei Zhao, Yuan Jiang, Zijing Zhang, Yang Yu, Hechang Chen | GRAIN: Multi-Granular and Implicit Information Aggregation Graph Neural
Network for Heterophilous Graphs | Accepted by AAAI 2025 | null | null | null | cs.LG cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Graph neural networks (GNNs) have shown significant success in learning graph
representations. However, recent studies reveal that GNNs often fail to
outperform simple MLPs on heterophilous graph tasks, where connected nodes may
differ in features or labels, challenging the homophily assumption. Existing
methods addr... | [
{
"version": "v1",
"created": "Wed, 9 Apr 2025 07:36:44 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Zhao",
"Songwei",
""
],
[
"Jiang",
"Yuan",
""
],
[
"Zhang",
"Zijing",
""
],
[
"Yu",
"Yang",
""
],
[
"Chen",
"Hechang",
""
]
] | TITLE: GRAIN: Multi-Granular and Implicit Information Aggregation Graph Neural
Network for Heterophilous Graphs
ABSTRACT: Graph neural networks (GNNs) have shown significant success in learning graph
representations. However, recent studies reveal that GNNs often fail to
outperform simple MLPs on heterophilous gr... |
2504.06658 | Xiaohua Feng | Xiaohua Feng, Yuyuan Li, Chengye Wang, Junlin Liu, Li Zhang, Chaochao
Chen | A Neuro-inspired Interpretation of Unlearning in Large Language Models
through Sample-level Unlearning Difficulty | 16 pages | null | null | null | cs.LG cs.AI cs.CL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Driven by privacy protection laws and regulations, unlearning in Large
Language Models (LLMs) is gaining increasing attention. However, current
research often neglects the interpretability of the unlearning process,
particularly concerning sample-level unlearning difficulty. Existing studies
typically assume a unifor... | [
{
"version": "v1",
"created": "Wed, 9 Apr 2025 07:48:10 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Feng",
"Xiaohua",
""
],
[
"Li",
"Yuyuan",
""
],
[
"Wang",
"Chengye",
""
],
[
"Liu",
"Junlin",
""
],
[
"Zhang",
"Li",
""
],
[
"Chen",
"Chaochao",
""
]
] | TITLE: A Neuro-inspired Interpretation of Unlearning in Large Language Models
through Sample-level Unlearning Difficulty
ABSTRACT: Driven by privacy protection laws and regulations, unlearning in Large
Language Models (LLMs) is gaining increasing attention. However, current
research often neglects the interpretab... |
2504.06659 | Xiaohua Feng | Xiaohua Feng, Yuyuan Li, Huwei Ji, Jiaming Zhang, Li Zhang, Tianyu Du,
Chaochao Chen | Bridging the Gap Between Preference Alignment and Machine Unlearning | 17 pages | null | null | null | cs.LG cs.AI cs.CL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Despite advances in Preference Alignment (PA) for Large Language Models
(LLMs), mainstream methods like Reinforcement Learning with Human Feedback
(RLHF) face notable challenges. These approaches require high-quality datasets
of positive preference examples, which are costly to obtain and computationally
intensive du... | [
{
"version": "v1",
"created": "Wed, 9 Apr 2025 07:49:08 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Feng",
"Xiaohua",
""
],
[
"Li",
"Yuyuan",
""
],
[
"Ji",
"Huwei",
""
],
[
"Zhang",
"Jiaming",
""
],
[
"Zhang",
"Li",
""
],
[
"Du",
"Tianyu",
""
],
[
"Chen",
"Chaochao",
""
]
] | TITLE: Bridging the Gap Between Preference Alignment and Machine Unlearning
ABSTRACT: Despite advances in Preference Alignment (PA) for Large Language Models
(LLMs), mainstream methods like Reinforcement Learning with Human Feedback
(RLHF) face notable challenges. These approaches require high-quality datasets
of p... |
2504.06660 | Osama Ahmad | Osama Ahmad, Zubair Khalid | Robust and Noise-resilient Long-Term Prediction of Spatiotemporal Data
Using Variational Mode Graph Neural Networks with 3D Attention | Accepted in IJCNN, 2025 | null | null | null | cs.LG | http://creativecommons.org/licenses/by/4.0/ | This paper focuses on improving the robustness of spatiotemporal long-term
prediction using a variational mode graph convolutional network (VMGCN) by
introducing 3D channel attention. The deep learning network for this task
relies on historical data inputs, yet real-time data can be corrupted by sensor
noise, alterin... | [
{
"version": "v1",
"created": "Wed, 9 Apr 2025 07:49:45 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Ahmad",
"Osama",
""
],
[
"Khalid",
"Zubair",
""
]
] | TITLE: Robust and Noise-resilient Long-Term Prediction of Spatiotemporal Data
Using Variational Mode Graph Neural Networks with 3D Attention
ABSTRACT: This paper focuses on improving the robustness of spatiotemporal long-term
prediction using a variational mode graph convolutional network (VMGCN) by
introducing 3... |
2504.06672 | Elia Peruzzo | Elia Peruzzo, Dejia Xu, Xingqian Xu, Humphrey Shi, Nicu Sebe | RAGME: Retrieval Augmented Video Generation for Enhanced Motion Realism | Code available at: https://github.com/helia95/ragme | null | null | null | cs.CV | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Video generation is experiencing rapid growth, driven by advances in
diffusion models and the development of better and larger datasets. However,
producing high-quality videos remains challenging due to the high-dimensional
data and the complexity of the task. Recent efforts have primarily focused on
enhancing visual... | [
{
"version": "v1",
"created": "Wed, 9 Apr 2025 08:14:05 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Peruzzo",
"Elia",
""
],
[
"Xu",
"Dejia",
""
],
[
"Xu",
"Xingqian",
""
],
[
"Shi",
"Humphrey",
""
],
[
"Sebe",
"Nicu",
""
]
] | TITLE: RAGME: Retrieval Augmented Video Generation for Enhanced Motion Realism
ABSTRACT: Video generation is experiencing rapid growth, driven by advances in
diffusion models and the development of better and larger datasets. However,
producing high-quality videos remains challenging due to the high-dimensional
dat... |
2504.06680 | Christoph Balada | Christoph Balada, Aida Romano-Martinez, Vincent ten Cate, Katharina
Geschke, Jonas Tesarz, Paul Cla{\ss}en, Alexander K. Schuster, Dativa
Tibyampansha, Karl-Patrik Kresoja, Philipp S. Wild, Sheraz Ahmed, Andreas
Dengel | Deep Learning for Cardiovascular Risk Assessment: Proxy Features from
Carotid Sonography as Predictors of Arterial Damage | null | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this study, hypertension is utilized as an indicator of individual
vascular damage. This damage can be identified through machine learning
techniques, providing an early risk marker for potential major cardiovascular
events and offering valuable insights into the overall arterial condition of
individual patients. ... | [
{
"version": "v1",
"created": "Wed, 9 Apr 2025 08:38:17 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Balada",
"Christoph",
""
],
[
"Romano-Martinez",
"Aida",
""
],
[
"Cate",
"Vincent ten",
""
],
[
"Geschke",
"Katharina",
""
],
[
"Tesarz",
"Jonas",
""
],
[
"Claßen",
"Paul",
""
],
[
"Schuster",
"Alexander K.",
... | TITLE: Deep Learning for Cardiovascular Risk Assessment: Proxy Features from
Carotid Sonography as Predictors of Arterial Damage
ABSTRACT: In this study, hypertension is utilized as an indicator of individual
vascular damage. This damage can be identified through machine learning
techniques, providing an early ri... |
2504.06699 | Sam Jacob Jacob | Sam Jacob Jacob, Markus Mrosek, Carsten Othmer, Harald K\"ostler | Benchmarking Convolutional Neural Network and Graph Neural Network based
Surrogate Models on a Real-World Car External Aerodynamics Dataset | null | null | null | null | cs.LG | http://creativecommons.org/licenses/by/4.0/ | Aerodynamic optimization is crucial for developing eco-friendly, aerodynamic,
and stylish cars, which requires close collaboration between aerodynamicists
and stylists, a collaboration impaired by the time-consuming nature of
aerodynamic simulations. Surrogate models offer a viable solution to reduce
this overhead, b... | [
{
"version": "v1",
"created": "Wed, 9 Apr 2025 09:04:59 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Jacob",
"Sam Jacob",
""
],
[
"Mrosek",
"Markus",
""
],
[
"Othmer",
"Carsten",
""
],
[
"Köstler",
"Harald",
""
]
] | TITLE: Benchmarking Convolutional Neural Network and Graph Neural Network based
Surrogate Models on a Real-World Car External Aerodynamics Dataset
ABSTRACT: Aerodynamic optimization is crucial for developing eco-friendly, aerodynamic,
and stylish cars, which requires close collaboration between aerodynamicists
an... |
2504.06714 | Jujia Zhao | Jujia Zhao, Wenjie Wang, Chen Xu, Xiuying Wang, Zhaochun Ren, Suzan
Verberne | Unifying Search and Recommendation: A Generative Paradigm Inspired by
Information Theory | null | null | null | null | cs.IR | http://creativecommons.org/licenses/by/4.0/ | Recommender systems and search engines serve as foundational elements of
online platforms, with the former delivering information proactively and the
latter enabling users to seek information actively. Unifying both tasks in a
shared model is promising since it can enhance user modeling and item
understanding. Previo... | [
{
"version": "v1",
"created": "Wed, 9 Apr 2025 09:15:37 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Zhao",
"Jujia",
""
],
[
"Wang",
"Wenjie",
""
],
[
"Xu",
"Chen",
""
],
[
"Wang",
"Xiuying",
""
],
[
"Ren",
"Zhaochun",
""
],
[
"Verberne",
"Suzan",
""
]
] | TITLE: Unifying Search and Recommendation: A Generative Paradigm Inspired by
Information Theory
ABSTRACT: Recommender systems and search engines serve as foundational elements of
online platforms, with the former delivering information proactively and the
latter enabling users to seek information actively. Unifyi... |
2504.06719 | Pedro Hermosilla Casajus | Pedro Hermosilla and Christian Stippel and Leon Sick | Masked Scene Modeling: Narrowing the Gap Between Supervised and
Self-Supervised Learning in 3D Scene Understanding | Accepted at CVPR 2025 | null | null | null | cs.CV cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Self-supervised learning has transformed 2D computer vision by enabling
models trained on large, unannotated datasets to provide versatile
off-the-shelf features that perform similarly to models trained with labels.
However, in 3D scene understanding, self-supervised methods are typically only
used as a weight initia... | [
{
"version": "v1",
"created": "Wed, 9 Apr 2025 09:19:49 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Hermosilla",
"Pedro",
""
],
[
"Stippel",
"Christian",
""
],
[
"Sick",
"Leon",
""
]
] | TITLE: Masked Scene Modeling: Narrowing the Gap Between Supervised and
Self-Supervised Learning in 3D Scene Understanding
ABSTRACT: Self-supervised learning has transformed 2D computer vision by enabling
models trained on large, unannotated datasets to provide versatile
off-the-shelf features that perform similar... |
2504.06722 | Katsuya Akamatsu | Katsuya O. Akamatsu, Kenji Harada, Tsuyoshi Okubo, Naoki Kawashima | Plastic tensor networks for interpretable generative modeling | 37 pages, 16 figures | null | null | null | cs.LG cond-mat.stat-mech | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A structural optimization scheme for a single-layer nonnegative adaptive
tensor tree (NATT) that models a target probability distribution is proposed.
The NATT scheme, by construction, has the advantage that it is interpretable as
a probabilistic graphical model. We consider the NATT scheme and a recently
proposed Bo... | [
{
"version": "v1",
"created": "Wed, 9 Apr 2025 09:23:11 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Akamatsu",
"Katsuya O.",
""
],
[
"Harada",
"Kenji",
""
],
[
"Okubo",
"Tsuyoshi",
""
],
[
"Kawashima",
"Naoki",
""
]
] | TITLE: Plastic tensor networks for interpretable generative modeling
ABSTRACT: A structural optimization scheme for a single-layer nonnegative adaptive
tensor tree (NATT) that models a target probability distribution is proposed.
The NATT scheme, by construction, has the advantage that it is interpretable as
a prob... |
2504.06740 | Hongkuan Zhou | Ylli Sadikaj, Hongkuan Zhou, Lavdim Halilaj, Stefan Schmid, Steffen
Staab, Claudia Plant | MultiADS: Defect-aware Supervision for Multi-type Anomaly Detection and
Segmentation in Zero-Shot Learning | null | null | null | null | cs.CV | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Precise optical inspection in industrial applications is crucial for
minimizing scrap rates and reducing the associated costs. Besides merely
detecting if a product is anomalous or not, it is crucial to know the distinct
type of defect, such as a bent, cut, or scratch. The ability to recognize the
"exact" defect type... | [
{
"version": "v1",
"created": "Wed, 9 Apr 2025 09:52:04 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Sadikaj",
"Ylli",
""
],
[
"Zhou",
"Hongkuan",
""
],
[
"Halilaj",
"Lavdim",
""
],
[
"Schmid",
"Stefan",
""
],
[
"Staab",
"Steffen",
""
],
[
"Plant",
"Claudia",
""
]
] | TITLE: MultiADS: Defect-aware Supervision for Multi-type Anomaly Detection and
Segmentation in Zero-Shot Learning
ABSTRACT: Precise optical inspection in industrial applications is crucial for
minimizing scrap rates and reducing the associated costs. Besides merely
detecting if a product is anomalous or not, it i... |
2504.06741 | Constantin Ulrich | Constantin Ulrich, Tassilo Wald, Fabian Isensee, Klaus H. Maier-Hein | Large Scale Supervised Pretraining For Traumatic Brain Injury
Segmentation | null | null | null | null | cs.CV | http://creativecommons.org/licenses/by/4.0/ | The segmentation of lesions in Moderate to Severe Traumatic Brain Injury
(msTBI) presents a significant challenge in neuroimaging due to the diverse
characteristics of these lesions, which vary in size, shape, and distribution
across brain regions and tissue types. This heterogeneity complicates
traditional image pro... | [
{
"version": "v1",
"created": "Wed, 9 Apr 2025 09:52:45 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Ulrich",
"Constantin",
""
],
[
"Wald",
"Tassilo",
""
],
[
"Isensee",
"Fabian",
""
],
[
"Maier-Hein",
"Klaus H.",
""
]
] | TITLE: Large Scale Supervised Pretraining For Traumatic Brain Injury
Segmentation
ABSTRACT: The segmentation of lesions in Moderate to Severe Traumatic Brain Injury
(msTBI) presents a significant challenge in neuroimaging due to the diverse
characteristics of these lesions, which vary in size, shape, and distribu... |
2504.06766 | Yuxin Wang | Yuxin Wang, Yiran Guo, Yining Zheng, Zhangyue Yin, Shuo Chen, Jie
Yang, Jiajun Chen, Xuanjing Huang, Xipeng Qiu | FamilyTool: A Multi-hop Personalized Tool Use Benchmark | null | null | null | null | cs.AI cs.CL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The integration of tool learning with Large Language Models (LLMs) has
expanded their capabilities in handling complex tasks by leveraging external
tools. However, existing benchmarks for tool learning inadequately address
critical real-world personalized scenarios, particularly those requiring
multi-hop reasoning an... | [
{
"version": "v1",
"created": "Wed, 9 Apr 2025 10:42:36 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Wang",
"Yuxin",
""
],
[
"Guo",
"Yiran",
""
],
[
"Zheng",
"Yining",
""
],
[
"Yin",
"Zhangyue",
""
],
[
"Chen",
"Shuo",
""
],
[
"Yang",
"Jie",
""
],
[
"Chen",
"Jiajun",
""
],
[
"Huang",
"Xuanjing... | TITLE: FamilyTool: A Multi-hop Personalized Tool Use Benchmark
ABSTRACT: The integration of tool learning with Large Language Models (LLMs) has
expanded their capabilities in handling complex tasks by leveraging external
tools. However, existing benchmarks for tool learning inadequately address
critical real-world ... |
2504.06767 | Matteo Santacesaria | Paolo Angella, Luca Balbi, Fabrizio Ferrando, Paolo Traverso, Rosario
Varriale, Vito Paolo Pastore, Matteo Santacesaria | DIMA: DIffusing Motion Artifacts for unsupervised correction in brain
MRI images | 7 pages, 5 figures, 7 tables | null | null | null | eess.IV cs.CV | http://creativecommons.org/licenses/by/4.0/ | Motion artifacts remain a significant challenge in Magnetic Resonance Imaging
(MRI), compromising diagnostic quality and potentially leading to misdiagnosis
or repeated scans. Existing deep learning approaches for motion artifact
correction typically require paired motion-free and motion-affected images for
training,... | [
{
"version": "v1",
"created": "Wed, 9 Apr 2025 10:43:38 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Angella",
"Paolo",
""
],
[
"Balbi",
"Luca",
""
],
[
"Ferrando",
"Fabrizio",
""
],
[
"Traverso",
"Paolo",
""
],
[
"Varriale",
"Rosario",
""
],
[
"Pastore",
"Vito Paolo",
""
],
[
"Santacesaria",
"Matteo",
""... | TITLE: DIMA: DIffusing Motion Artifacts for unsupervised correction in brain
MRI images
ABSTRACT: Motion artifacts remain a significant challenge in Magnetic Resonance Imaging
(MRI), compromising diagnostic quality and potentially leading to misdiagnosis
or repeated scans. Existing deep learning approaches for mo... |
2504.06780 | Yong Bai | Yong Bai, Rui Xiang, Kaiyuan Li, Yongxiang Tang, Yanhua Cheng, Xialong
Liu, Peng Jiang, Kun Gai | CHIME: A Compressive Framework for Holistic Interest Modeling | null | null | null | null | cs.IR | http://creativecommons.org/licenses/by/4.0/ | Modeling holistic user interests is important for improving recommendation
systems but is challenged by high computational cost and difficulty in handling
diverse information with full behavior context. Existing search-based methods
might lose critical signals during behavior selection. To overcome these
limitations,... | [
{
"version": "v1",
"created": "Wed, 9 Apr 2025 11:08:49 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Bai",
"Yong",
""
],
[
"Xiang",
"Rui",
""
],
[
"Li",
"Kaiyuan",
""
],
[
"Tang",
"Yongxiang",
""
],
[
"Cheng",
"Yanhua",
""
],
[
"Liu",
"Xialong",
""
],
[
"Jiang",
"Peng",
""
],
[
"Gai",
"Kun",
... | TITLE: CHIME: A Compressive Framework for Holistic Interest Modeling
ABSTRACT: Modeling holistic user interests is important for improving recommendation
systems but is challenged by high computational cost and difficulty in handling
diverse information with full behavior context. Existing search-based methods
migh... |
2504.06781 | Reiji Saito | Reiji Saito, Kazuhiro Hotta | Domain Generalization through Attenuation of Domain-Specific Information | Accepted by CVPR 2025 Workshops | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we propose a new evaluation metric called Domain Independence
(DI) and Attenuation of Domain-Specific Information (ADSI) which is
specifically designed for domain-generalized semantic segmentation in
automotive images. DI measures the presence of domain-specific information: a
lower DI value indicates ... | [
{
"version": "v1",
"created": "Wed, 9 Apr 2025 11:10:29 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Saito",
"Reiji",
""
],
[
"Hotta",
"Kazuhiro",
""
]
] | TITLE: Domain Generalization through Attenuation of Domain-Specific Information
ABSTRACT: In this paper, we propose a new evaluation metric called Domain Independence
(DI) and Attenuation of Domain-Specific Information (ADSI) which is
specifically designed for domain-generalized semantic segmentation in
automotive ... |
2504.06785 | Andrea Visentin Dr | Shuoshuo Xu, Kai Zhao, James Loney, Zili Li, Andrea Visentin | Zero-Shot Image-Based Large Language Model Approach to Road Pavement
Monitoring | null | null | null | null | cs.CV cs.AI | http://creativecommons.org/licenses/by/4.0/ | Effective and rapid evaluation of pavement surface condition is critical for
prioritizing maintenance, ensuring transportation safety, and minimizing
vehicle wear and tear. While conventional manual inspections suffer from
subjectivity, existing machine learning-based methods are constrained by their
reliance on larg... | [
{
"version": "v1",
"created": "Wed, 9 Apr 2025 11:19:17 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Xu",
"Shuoshuo",
""
],
[
"Zhao",
"Kai",
""
],
[
"Loney",
"James",
""
],
[
"Li",
"Zili",
""
],
[
"Visentin",
"Andrea",
""
]
] | TITLE: Zero-Shot Image-Based Large Language Model Approach to Road Pavement
Monitoring
ABSTRACT: Effective and rapid evaluation of pavement surface condition is critical for
prioritizing maintenance, ensuring transportation safety, and minimizing
vehicle wear and tear. While conventional manual inspections suffer... |
2504.06805 | Nicola Novello | Nicola Novello and Andrea M. Tonello | Robust Classification with Noisy Labels Based on Posterior Maximization | null | null | null | null | cs.LG | http://creativecommons.org/licenses/by/4.0/ | Designing objective functions robust to label noise is crucial for real-world
classification algorithms. In this paper, we investigate the robustness to
label noise of an $f$-divergence-based class of objective functions recently
proposed for supervised classification, herein referred to as $f$-PML. We show
that, in ... | [
{
"version": "v1",
"created": "Wed, 9 Apr 2025 11:52:51 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Novello",
"Nicola",
""
],
[
"Tonello",
"Andrea M.",
""
]
] | TITLE: Robust Classification with Noisy Labels Based on Posterior Maximization
ABSTRACT: Designing objective functions robust to label noise is crucial for real-world
classification algorithms. In this paper, we investigate the robustness to
label noise of an $f$-divergence-based class of objective functions recent... |
2504.06811 | Bhavesh Gyanchandani | Abhinav Roy, Bhavesh Gyanchandani, and Aditya Oza | Hybrid CNN with Chebyshev Polynomial Expansion for Medical Image
Analysis | null | null | null | null | cs.CV cs.LG | http://creativecommons.org/licenses/by/4.0/ | Lung cancer remains one of the leading causes of cancer-related mortality
worldwide, with early and accurate diagnosis playing a pivotal role in
improving patient outcomes. Automated detection of pulmonary nodules in
computed tomography (CT) scans is a challenging task due to variability in
nodule size, shape, textur... | [
{
"version": "v1",
"created": "Wed, 9 Apr 2025 12:02:56 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Roy",
"Abhinav",
""
],
[
"Gyanchandani",
"Bhavesh",
""
],
[
"Oza",
"Aditya",
""
]
] | TITLE: Hybrid CNN with Chebyshev Polynomial Expansion for Medical Image
Analysis
ABSTRACT: Lung cancer remains one of the leading causes of cancer-related mortality
worldwide, with early and accurate diagnosis playing a pivotal role in
improving patient outcomes. Automated detection of pulmonary nodules in
comput... |
2504.06829 | Mahdieh Alizadeh | Ali Goli, Mahdieh Alizadeh, Hadi Sadoghi Yazdi | Adaptive Locally Linear Embedding | 16 pages | null | null | null | cs.LG cs.AI | http://creativecommons.org/licenses/by-sa/4.0/ | Manifold learning techniques, such as Locally linear embedding (LLE), are
designed to preserve the local neighborhood structures of high-dimensional data
during dimensionality reduction. Traditional LLE employs Euclidean distance to
define neighborhoods, which can struggle to capture the intrinsic geometric
relations... | [
{
"version": "v1",
"created": "Wed, 9 Apr 2025 12:40:13 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Goli",
"Ali",
""
],
[
"Alizadeh",
"Mahdieh",
""
],
[
"Yazdi",
"Hadi Sadoghi",
""
]
] | TITLE: Adaptive Locally Linear Embedding
ABSTRACT: Manifold learning techniques, such as Locally linear embedding (LLE), are
designed to preserve the local neighborhood structures of high-dimensional data
during dimensionality reduction. Traditional LLE employs Euclidean distance to
define neighborhoods, which can ... |
2504.06835 | Ziyi Wang | Ziyi Wang, Haoran Wu, Yiming Rong, Deyang Jiang, Yixin Zhang, Yunlong
Zhao, Shuang Xu, Bo XU | LVC: A Lightweight Compression Framework for Enhancing VLMs in Long
Video Understanding | null | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Long video understanding is a complex task that requires both spatial detail
and temporal awareness. While Vision-Language Models (VLMs) obtain frame-level
understanding capabilities through multi-frame input, they suffer from
information loss due to the sparse sampling strategy. In contrast, Video Large
Language Mod... | [
{
"version": "v1",
"created": "Wed, 9 Apr 2025 12:51:10 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Wang",
"Ziyi",
""
],
[
"Wu",
"Haoran",
""
],
[
"Rong",
"Yiming",
""
],
[
"Jiang",
"Deyang",
""
],
[
"Zhang",
"Yixin",
""
],
[
"Zhao",
"Yunlong",
""
],
[
"Xu",
"Shuang",
""
],
[
"XU",
"Bo",
... | TITLE: LVC: A Lightweight Compression Framework for Enhancing VLMs in Long
Video Understanding
ABSTRACT: Long video understanding is a complex task that requires both spatial detail
and temporal awareness. While Vision-Language Models (VLMs) obtain frame-level
understanding capabilities through multi-frame input,... |
2504.06836 | Chun Kit Wong | Jakub Maciej Wi\'sniewski, Anders Nymark Christensen, Mary Le Ngo,
Martin Gr{\o}nneb{\ae}k Tolsgaard, Chun Kit Wong | Determining Fetal Orientations From Blind Sweep Ultrasound Video | 10 pages | null | null | null | cs.CV | http://creativecommons.org/licenses/by/4.0/ | Cognitive demands of fetal ultrasound examinations pose unique challenges
among clinicians. With the goal of providing an assistive tool, we developed an
automated pipeline for predicting fetal orientation from ultrasound videos
acquired following a simple blind sweep protocol. Leveraging on a pre-trained
head detect... | [
{
"version": "v1",
"created": "Wed, 9 Apr 2025 12:51:15 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Wiśniewski",
"Jakub Maciej",
""
],
[
"Christensen",
"Anders Nymark",
""
],
[
"Ngo",
"Mary Le",
""
],
[
"Tolsgaard",
"Martin Grønnebæk",
""
],
[
"Wong",
"Chun Kit",
""
]
] | TITLE: Determining Fetal Orientations From Blind Sweep Ultrasound Video
ABSTRACT: Cognitive demands of fetal ultrasound examinations pose unique challenges
among clinicians. With the goal of providing an assistive tool, we developed an
automated pipeline for predicting fetal orientation from ultrasound videos
acqui... |
2504.06841 | Tom Simon | Tom Simon and William Mocaer and Pierrick Tranouez and Clement
Chatelain and Thierry Paquet | Classifying the Unknown: In-Context Learning for Open-Vocabulary Text
and Symbol Recognition | Submitted to ICDAR 2025 | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We introduce Rosetta, a multimodal model that leverages Multimodal In-Context
Learning (MICL) to classify sequences of novel script patterns in documents by
leveraging minimal examples, thus eliminating the need for explicit retraining.
To enhance contextual learning, we designed a dataset generation process that
ens... | [
{
"version": "v1",
"created": "Wed, 9 Apr 2025 12:58:25 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Simon",
"Tom",
""
],
[
"Mocaer",
"William",
""
],
[
"Tranouez",
"Pierrick",
""
],
[
"Chatelain",
"Clement",
""
],
[
"Paquet",
"Thierry",
""
]
] | TITLE: Classifying the Unknown: In-Context Learning for Open-Vocabulary Text
and Symbol Recognition
ABSTRACT: We introduce Rosetta, a multimodal model that leverages Multimodal In-Context
Learning (MICL) to classify sequences of novel script patterns in documents by
leveraging minimal examples, thus eliminating t... |
2504.06857 | Roger Huang | Roger G. Huang, Andrew Cudd, Masaki Kawaue, Tatsuya Kikawa, Benjamin
Nachman, Vinicius Mikuni, Callum Wilkinson | Machine Learning-Assisted Unfolding for Neutrino Cross-section
Measurements | 16 pages, 12 figures, 4 tables | null | null | null | hep-ex hep-ph physics.data-an | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The choice of unfolding method for a cross-section measurement is tightly
coupled to the model dependence of the efficiency correction and the overall
impact of cross-section modeling uncertainties in the analysis. A key issue is
the dimensionality used in unfolding, as the kinematics of all outgoing
particles in an ... | [
{
"version": "v1",
"created": "Wed, 9 Apr 2025 13:08:35 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Huang",
"Roger G.",
""
],
[
"Cudd",
"Andrew",
""
],
[
"Kawaue",
"Masaki",
""
],
[
"Kikawa",
"Tatsuya",
""
],
[
"Nachman",
"Benjamin",
""
],
[
"Mikuni",
"Vinicius",
""
],
[
"Wilkinson",
"Callum",
""
]
] | TITLE: Machine Learning-Assisted Unfolding for Neutrino Cross-section
Measurements
ABSTRACT: The choice of unfolding method for a cross-section measurement is tightly
coupled to the model dependence of the efficiency correction and the overall
impact of cross-section modeling uncertainties in the analysis. A key ... |
2504.06866 | Seunghyeok Back | Seunghyeok Back, Joosoon Lee, Kangmin Kim, Heeseon Rho, Geonhyup Lee,
Raeyoung Kang, Sangbeom Lee, Sangjun Noh, Youngjin Lee, Taeyeop Lee, Kyoobin
Lee | GraspClutter6D: A Large-scale Real-world Dataset for Robust Perception
and Grasping in Cluttered Scenes | null | null | null | null | cs.RO cs.AI cs.CV | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Robust grasping in cluttered environments remains an open challenge in
robotics. While benchmark datasets have significantly advanced deep learning
methods, they mainly focus on simplistic scenes with light occlusion and
insufficient diversity, limiting their applicability to practical scenarios. We
present GraspClut... | [
{
"version": "v1",
"created": "Wed, 9 Apr 2025 13:15:46 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Back",
"Seunghyeok",
""
],
[
"Lee",
"Joosoon",
""
],
[
"Kim",
"Kangmin",
""
],
[
"Rho",
"Heeseon",
""
],
[
"Lee",
"Geonhyup",
""
],
[
"Kang",
"Raeyoung",
""
],
[
"Lee",
"Sangbeom",
""
],
[
"Noh",
... | TITLE: GraspClutter6D: A Large-scale Real-world Dataset for Robust Perception
and Grasping in Cluttered Scenes
ABSTRACT: Robust grasping in cluttered environments remains an open challenge in
robotics. While benchmark datasets have significantly advanced deep learning
methods, they mainly focus on simplistic scen... |
2504.06880 | Rio Kishimoto | Rio Kishimoto and Tetsuya Kanda and Yuki Manabe and Katsuro Inoue and
Shi Qiu and Yoshiki Higo | A Dataset of Software Bill of Materials for Evaluating SBOM Consumption
Tools | 5 pages, to appear in the Proceedings of the 22nd IEEE/ACM
International Conference on Mining Software Repositories (MSR'25) | null | null | null | cs.SE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A Software Bill of Materials (SBOM) is becoming an essential tool for
effective software dependency management. An SBOM is a list of components used
in software, including details such as component names, versions, and licenses.
Using SBOMs, developers can quickly identify software components and assess
whether their... | [
{
"version": "v1",
"created": "Wed, 9 Apr 2025 13:35:02 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Kishimoto",
"Rio",
""
],
[
"Kanda",
"Tetsuya",
""
],
[
"Manabe",
"Yuki",
""
],
[
"Inoue",
"Katsuro",
""
],
[
"Qiu",
"Shi",
""
],
[
"Higo",
"Yoshiki",
""
]
] | TITLE: A Dataset of Software Bill of Materials for Evaluating SBOM Consumption
Tools
ABSTRACT: A Software Bill of Materials (SBOM) is becoming an essential tool for
effective software dependency management. An SBOM is a list of components used
in software, including details such as component names, versions, and ... |
2504.06881 | Ye Luo | Mingbo Li, Liying Liu, Ye Luo | Compound and Parallel Modes of Tropical Convolutional Neural Networks | 28 pages, 5 figures | null | null | null | cs.CV cs.AI | http://creativecommons.org/licenses/by/4.0/ | Convolutional neural networks have become increasingly deep and complex,
leading to higher computational costs. While tropical convolutional neural
networks (TCNNs) reduce multiplications, they underperform compared to standard
CNNs. To address this, we propose two new variants - compound TCNN (cTCNN) and
parallel TC... | [
{
"version": "v1",
"created": "Wed, 9 Apr 2025 13:36:11 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Li",
"Mingbo",
""
],
[
"Liu",
"Liying",
""
],
[
"Luo",
"Ye",
""
]
] | TITLE: Compound and Parallel Modes of Tropical Convolutional Neural Networks
ABSTRACT: Convolutional neural networks have become increasingly deep and complex,
leading to higher computational costs. While tropical convolutional neural
networks (TCNNs) reduce multiplications, they underperform compared to standard
C... |
2504.06884 | Wuyang Liu | Wuyang Liu, Yi Chai, Yongpeng Yan, Yanzhen Ren | Audio-visual Event Localization on Portrait Mode Short Videos | null | null | null | null | cs.MM cs.AI cs.CV | http://creativecommons.org/licenses/by/4.0/ | Audio-visual event localization (AVEL) plays a critical role in multimodal
scene understanding. While existing datasets for AVEL predominantly comprise
landscape-oriented long videos with clean and simple audio context, short
videos have become the primary format of online video content due to the the
proliferation o... | [
{
"version": "v1",
"created": "Wed, 9 Apr 2025 13:38:40 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Liu",
"Wuyang",
""
],
[
"Chai",
"Yi",
""
],
[
"Yan",
"Yongpeng",
""
],
[
"Ren",
"Yanzhen",
""
]
] | TITLE: Audio-visual Event Localization on Portrait Mode Short Videos
ABSTRACT: Audio-visual event localization (AVEL) plays a critical role in multimodal
scene understanding. While existing datasets for AVEL predominantly comprise
landscape-oriented long videos with clean and simple audio context, short
videos have... |
2504.06908 | Abdullah Hamdi | Emmanuelle Bourigault, Amir Jamaludin, Abdullah Hamdi | UKBOB: One Billion MRI Labeled Masks for Generalizable 3D Medical Image
Segmentation | preprint | null | null | null | cs.CV cs.LG | http://creativecommons.org/licenses/by/4.0/ | In medical imaging, the primary challenge is collecting large-scale labeled
data due to privacy concerns, logistics, and high labeling costs. In this work,
we present the UK Biobank Organs and Bones (UKBOB), the largest labeled dataset
of body organs, comprising 51,761 MRI 3D samples (equivalent to 17.9 million 2D
im... | [
{
"version": "v1",
"created": "Wed, 9 Apr 2025 14:10:51 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Bourigault",
"Emmanuelle",
""
],
[
"Jamaludin",
"Amir",
""
],
[
"Hamdi",
"Abdullah",
""
]
] | TITLE: UKBOB: One Billion MRI Labeled Masks for Generalizable 3D Medical Image
Segmentation
ABSTRACT: In medical imaging, the primary challenge is collecting large-scale labeled
data due to privacy concerns, logistics, and high labeling costs. In this work,
we present the UK Biobank Organs and Bones (UKBOB), the ... |
2504.06910 | Sheng Lu | Sheng Lu, Ilia Kuznetsov, Iryna Gurevych | Identifying Aspects in Peer Reviews | null | null | null | null | cs.CL | http://creativecommons.org/licenses/by/4.0/ | Peer review is central to academic publishing, but the growing volume of
submissions is straining the process. This motivates the development of
computational approaches to support peer review. While each review is tailored
to a specific paper, reviewers often make assessments according to certain
aspects such as Nov... | [
{
"version": "v1",
"created": "Wed, 9 Apr 2025 14:14:42 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Lu",
"Sheng",
""
],
[
"Kuznetsov",
"Ilia",
""
],
[
"Gurevych",
"Iryna",
""
]
] | TITLE: Identifying Aspects in Peer Reviews
ABSTRACT: Peer review is central to academic publishing, but the growing volume of
submissions is straining the process. This motivates the development of
computational approaches to support peer review. While each review is tailored
to a specific paper, reviewers often ma... |
2504.06915 | Francisco Mena | Miro Miranda, Francisco Mena, Andreas Dengel | An Analysis of Temporal Dropout in Earth Observation Time Series for
Regression Tasks | Accepted at Symposium on Intelligent Data Analysis (IDA 2025) | null | null | null | cs.LG cs.AI cs.CV | http://creativecommons.org/licenses/by/4.0/ | Missing instances in time series data impose a significant challenge to deep
learning models, particularly in regression tasks. In the Earth Observation
field, satellite failure or cloud occlusion frequently results in missing
time-steps, introducing uncertainties in the predicted output and causing a
decline in pred... | [
{
"version": "v1",
"created": "Wed, 9 Apr 2025 14:23:04 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Miranda",
"Miro",
""
],
[
"Mena",
"Francisco",
""
],
[
"Dengel",
"Andreas",
""
]
] | TITLE: An Analysis of Temporal Dropout in Earth Observation Time Series for
Regression Tasks
ABSTRACT: Missing instances in time series data impose a significant challenge to deep
learning models, particularly in regression tasks. In the Earth Observation
field, satellite failure or cloud occlusion frequently res... |
2504.06917 | Ming Liu | Ming Liu and Massimo Poesio | Data Augmentation for Fake Reviews Detection in Multiple Languages and
Multiple Domains | 32 pages, 15 figures | null | null | null | cs.CL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | With the growth of the Internet, buying habits have changed, and customers
have become more dependent on the online opinions of other customers to guide
their purchases. Identifying fake reviews thus became an important area for
Natural Language Processing (NLP) research. However, developing
high-performance NLP mode... | [
{
"version": "v1",
"created": "Wed, 9 Apr 2025 14:23:54 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Liu",
"Ming",
""
],
[
"Poesio",
"Massimo",
""
]
] | TITLE: Data Augmentation for Fake Reviews Detection in Multiple Languages and
Multiple Domains
ABSTRACT: With the growth of the Internet, buying habits have changed, and customers
have become more dependent on the online opinions of other customers to guide
their purchases. Identifying fake reviews thus became an... |
2504.06920 | Thibaud Ehret | Masquil El\'ias, Mar\'i Roger, Ehret Thibaud, Meinhardt-Llopis Enric,
Mus\'e Pablo, Facciolo Gabriele | S-EO: A Large-Scale Dataset for Geometry-Aware Shadow Detection in
Remote Sensing Applications | Accepted at Earthvision 2025 (CVPR Workshop) | null | null | null | cs.CV | http://creativecommons.org/licenses/by-nc-nd/4.0/ | We introduce the S-EO dataset: a large-scale, high-resolution dataset,
designed to advance geometry-aware shadow detection. Collected from diverse
public-domain sources, including challenge datasets and government providers
such as USGS, our dataset comprises 702 georeferenced tiles across the USA,
each covering 500x... | [
{
"version": "v1",
"created": "Wed, 9 Apr 2025 14:25:35 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Elías",
"Masquil",
""
],
[
"Roger",
"Marí",
""
],
[
"Thibaud",
"Ehret",
""
],
[
"Enric",
"Meinhardt-Llopis",
""
],
[
"Pablo",
"Musé",
""
],
[
"Gabriele",
"Facciolo",
""
]
] | TITLE: S-EO: A Large-Scale Dataset for Geometry-Aware Shadow Detection in
Remote Sensing Applications
ABSTRACT: We introduce the S-EO dataset: a large-scale, high-resolution dataset,
designed to advance geometry-aware shadow detection. Collected from diverse
public-domain sources, including challenge datasets and... |
2504.06921 | Tejas Sudharshan Mathai | Anisa V. Prasad, Tejas Sudharshan Mathai, Pritam Mukherjee, Jianfei
Liu, and Ronald M. Summers | Leveraging Anatomical Priors for Automated Pancreas Segmentation on
Abdominal CT | Published at SPIE Medical Imaging 2025 | null | null | null | eess.IV cs.AI cs.CV | http://creativecommons.org/licenses/by-nc-nd/4.0/ | An accurate segmentation of the pancreas on CT is crucial to identify
pancreatic pathologies and extract imaging-based biomarkers. However, prior
research on pancreas segmentation has primarily focused on modifying the
segmentation model architecture or utilizing pre- and post-processing
techniques. In this article, ... | [
{
"version": "v1",
"created": "Wed, 9 Apr 2025 14:29:08 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Prasad",
"Anisa V.",
""
],
[
"Mathai",
"Tejas Sudharshan",
""
],
[
"Mukherjee",
"Pritam",
""
],
[
"Liu",
"Jianfei",
""
],
[
"Summers",
"Ronald M.",
""
]
] | TITLE: Leveraging Anatomical Priors for Automated Pancreas Segmentation on
Abdominal CT
ABSTRACT: An accurate segmentation of the pancreas on CT is crucial to identify
pancreatic pathologies and extract imaging-based biomarkers. However, prior
research on pancreas segmentation has primarily focused on modifying t... |
2504.06923 | Emiliano De Cristofaro | Georgi Ganev and Meenatchi Sundaram Muthu Selva Annamalai and Sofiane
Mahiou and Emiliano De Cristofaro | The Importance of Being Discrete: Measuring the Impact of Discretization
in End-to-End Differentially Private Synthetic Data | null | null | null | null | cs.CR cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Differentially Private (DP) generative marginal models are often used in the
wild to release synthetic tabular datasets in lieu of sensitive data while
providing formal privacy guarantees. These models approximate low-dimensional
marginals or query workloads; crucially, they require the training data to be
pre-discre... | [
{
"version": "v1",
"created": "Wed, 9 Apr 2025 14:30:30 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Ganev",
"Georgi",
""
],
[
"Annamalai",
"Meenatchi Sundaram Muthu Selva",
""
],
[
"Mahiou",
"Sofiane",
""
],
[
"De Cristofaro",
"Emiliano",
""
]
] | TITLE: The Importance of Being Discrete: Measuring the Impact of Discretization
in End-to-End Differentially Private Synthetic Data
ABSTRACT: Differentially Private (DP) generative marginal models are often used in the
wild to release synthetic tabular datasets in lieu of sensitive data while
providing formal pri... |
2504.06927 | Ur\v{s}ka Matja\v{s}ec | Ur\v{s}ka Matja\v{s}ec, Nikola Simidjievski, Mateja Jamnik | RO-FIGS: Efficient and Expressive Tree-Based Ensembles for Tabular Data | null | null | null | null | cs.LG | http://creativecommons.org/licenses/by/4.0/ | Tree-based models are often robust to uninformative features and can
accurately capture non-smooth, complex decision boundaries. Consequently, they
often outperform neural network-based models on tabular datasets at a
significantly lower computational cost. Nevertheless, the capability of
traditional tree-based ensem... | [
{
"version": "v1",
"created": "Wed, 9 Apr 2025 14:35:24 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Matjašec",
"Urška",
""
],
[
"Simidjievski",
"Nikola",
""
],
[
"Jamnik",
"Mateja",
""
]
] | TITLE: RO-FIGS: Efficient and Expressive Tree-Based Ensembles for Tabular Data
ABSTRACT: Tree-based models are often robust to uninformative features and can
accurately capture non-smooth, complex decision boundaries. Consequently, they
often outperform neural network-based models on tabular datasets at a
significa... |
2504.06935 | Chenyu Hui | Chenyu Hui and Anran Zhang and Xintong Li | ASRL:A robust loss function with potential for development | five pages and three figures | null | null | null | cs.LG | http://creativecommons.org/licenses/by-nc-nd/4.0/ | In this article, we proposed a partition:wise robust loss function based on
the previous robust loss function. The characteristics of this loss function
are that it achieves high robustness and a wide range of applicability through
partition-wise design and adaptive parameter adjustment. Finally, the
advantages and d... | [
{
"version": "v1",
"created": "Wed, 9 Apr 2025 14:40:46 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Hui",
"Chenyu",
""
],
[
"Zhang",
"Anran",
""
],
[
"Li",
"Xintong",
""
]
] | TITLE: ASRL:A robust loss function with potential for development
ABSTRACT: In this article, we proposed a partition:wise robust loss function based on
the previous robust loss function. The characteristics of this loss function
are that it achieves high robustness and a wide range of applicability through
partitio... |
2504.06950 | Sachin Kumar Danisetty | Sachin Kumar Danisetty, Alexandros Graikos, Srikar Yellapragada,
Dimitris Samaras | PathSegDiff: Pathology Segmentation using Diffusion model
representations | null | null | null | null | cs.CV | http://creativecommons.org/licenses/by/4.0/ | Image segmentation is crucial in many computational pathology pipelines,
including accurate disease diagnosis, subtyping, outcome, and survivability
prediction. The common approach for training a segmentation model relies on a
pre-trained feature extractor and a dataset of paired image and mask
annotations. These are... | [
{
"version": "v1",
"created": "Wed, 9 Apr 2025 14:58:21 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Danisetty",
"Sachin Kumar",
""
],
[
"Graikos",
"Alexandros",
""
],
[
"Yellapragada",
"Srikar",
""
],
[
"Samaras",
"Dimitris",
""
]
] | TITLE: PathSegDiff: Pathology Segmentation using Diffusion model
representations
ABSTRACT: Image segmentation is crucial in many computational pathology pipelines,
including accurate disease diagnosis, subtyping, outcome, and survivability
prediction. The common approach for training a segmentation model relies o... |
2504.06957 | Marco Acerbis | Marco Acerbis, Nata\v{s}a Sladoje, Joakim Lindblad | A Comparison of Deep Learning Methods for Cell Detection in Digital
Cytology | 14 pages, 6 figures, SCIA2025 | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Accurate and efficient cell detection is crucial in many biomedical image
analysis tasks. We evaluate the performance of several Deep Learning (DL)
methods for cell detection in Papanicolaou-stained cytological Whole Slide
Images (WSIs), focusing on accuracy of predictions and computational
efficiency. We examine rec... | [
{
"version": "v1",
"created": "Wed, 9 Apr 2025 15:08:12 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Acerbis",
"Marco",
""
],
[
"Sladoje",
"Nataša",
""
],
[
"Lindblad",
"Joakim",
""
]
] | TITLE: A Comparison of Deep Learning Methods for Cell Detection in Digital
Cytology
ABSTRACT: Accurate and efficient cell detection is crucial in many biomedical image
analysis tasks. We evaluate the performance of several Deep Learning (DL)
methods for cell detection in Papanicolaou-stained cytological Whole Sli... |
2504.06961 | Yu Qi | Yu Qi, Yuanchen Ju, Tianming Wei, Chi Chu, Lawson L.S. Wong, Huazhe Xu | Two by Two: Learning Multi-Task Pairwise Objects Assembly for
Generalizable Robot Manipulation | Accepted to CVPR 2025 (Conference on Computer Vision and Pattern
Recognition) | null | null | null | cs.RO cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | 3D assembly tasks, such as furniture assembly and component fitting, play a
crucial role in daily life and represent essential capabilities for future home
robots. Existing benchmarks and datasets predominantly focus on assembling
geometric fragments or factory parts, which fall short in addressing the
complexities o... | [
{
"version": "v1",
"created": "Wed, 9 Apr 2025 15:12:38 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Qi",
"Yu",
""
],
[
"Ju",
"Yuanchen",
""
],
[
"Wei",
"Tianming",
""
],
[
"Chu",
"Chi",
""
],
[
"Wong",
"Lawson L. S.",
""
],
[
"Xu",
"Huazhe",
""
]
] | TITLE: Two by Two: Learning Multi-Task Pairwise Objects Assembly for
Generalizable Robot Manipulation
ABSTRACT: 3D assembly tasks, such as furniture assembly and component fitting, play a
crucial role in daily life and represent essential capabilities for future home
robots. Existing benchmarks and datasets predo... |
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