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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2504.05855 | Zhang Dong | Xingzu Liu, Songhang deng, Mingbang Wang, Zhang Dong, Le Dai, Jiyuan
Li, Ruilin Nong | Enhancing Coreference Resolution with Pretrained Language Models:
Bridging the Gap Between Syntax and Semantics | acl submission | null | null | null | cs.CL cs.AI | http://creativecommons.org/licenses/by-sa/4.0/ | Large language models have made significant advancements in various natural
language processing tasks, including coreference resolution. However,
traditional methods often fall short in effectively distinguishing referential
relationships due to a lack of integration between syntactic and semantic
information. This s... | [
{
"version": "v1",
"created": "Tue, 8 Apr 2025 09:33:09 GMT"
}
] | 2025-04-09T00:00:00 | [
[
"Liu",
"Xingzu",
""
],
[
"deng",
"Songhang",
""
],
[
"Wang",
"Mingbang",
""
],
[
"Dong",
"Zhang",
""
],
[
"Dai",
"Le",
""
],
[
"Li",
"Jiyuan",
""
],
[
"Nong",
"Ruilin",
""
]
] | TITLE: Enhancing Coreference Resolution with Pretrained Language Models:
Bridging the Gap Between Syntax and Semantics
ABSTRACT: Large language models have made significant advancements in various natural
language processing tasks, including coreference resolution. However,
traditional methods often fall short in... |
2504.05866 | Sofia Della Penna | Sofia Della Penna, Roberto Natella, Vittorio Orbinato, Lorenzo
Parracino, Luciano Pianese | CTI-HAL: A Human-Annotated Dataset for Cyber Threat Intelligence
Analysis | Accepted for publication in the Workshop on Attackers and Cybercrime
Operations (WACCO 2025), co-located with IEEE European Symposium on Security
and Privacy 2025 | null | null | null | cs.CR | http://creativecommons.org/licenses/by/4.0/ | Organizations are increasingly targeted by Advanced Persistent Threats
(APTs), which involve complex, multi-stage tactics and diverse techniques.
Cyber Threat Intelligence (CTI) sources, such as incident reports and security
blogs, provide valuable insights, but are often unstructured and in natural
language, making ... | [
{
"version": "v1",
"created": "Tue, 8 Apr 2025 09:47:15 GMT"
}
] | 2025-04-09T00:00:00 | [
[
"Della Penna",
"Sofia",
""
],
[
"Natella",
"Roberto",
""
],
[
"Orbinato",
"Vittorio",
""
],
[
"Parracino",
"Lorenzo",
""
],
[
"Pianese",
"Luciano",
""
]
] | TITLE: CTI-HAL: A Human-Annotated Dataset for Cyber Threat Intelligence
Analysis
ABSTRACT: Organizations are increasingly targeted by Advanced Persistent Threats
(APTs), which involve complex, multi-stage tactics and diverse techniques.
Cyber Threat Intelligence (CTI) sources, such as incident reports and securit... |
2504.05878 | Xingyuan Li | Xingyuan Li, Ruichao Hou, Tongwei Ren, Gangshan Wu | KAN-SAM: Kolmogorov-Arnold Network Guided Segment Anything Model for
RGB-T Salient Object Detection | This paper is accepted by ICME2025 | null | null | null | cs.MM cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Existing RGB-thermal salient object detection (RGB-T SOD) methods aim to
identify visually significant objects by leveraging both RGB and thermal
modalities to enable robust performance in complex scenarios, but they often
suffer from limited generalization due to the constrained diversity of
available datasets and t... | [
{
"version": "v1",
"created": "Tue, 8 Apr 2025 10:07:02 GMT"
}
] | 2025-04-09T00:00:00 | [
[
"Li",
"Xingyuan",
""
],
[
"Hou",
"Ruichao",
""
],
[
"Ren",
"Tongwei",
""
],
[
"Wu",
"Gangshan",
""
]
] | TITLE: KAN-SAM: Kolmogorov-Arnold Network Guided Segment Anything Model for
RGB-T Salient Object Detection
ABSTRACT: Existing RGB-thermal salient object detection (RGB-T SOD) methods aim to
identify visually significant objects by leveraging both RGB and thermal
modalities to enable robust performance in complex ... |
2504.05882 | Luca Barco | Luca Barco, Giacomo Blanco, Gaetano Chiriaco, Alessia Intini, Luigi La
Riccia, Vittorio Scolamiero, Piero Boccardo, Paolo Garza, Fabrizio Dominici | Turin3D: Evaluating Adaptation Strategies under Label Scarcity in Urban
LiDAR Segmentation with Semi-Supervised Techniques | Accepted at CVPRW2025 - USM3D | null | null | null | cs.CV cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | 3D semantic segmentation plays a critical role in urban modelling, enabling
detailed understanding and mapping of city environments. In this paper, we
introduce Turin3D: a new aerial LiDAR dataset for point cloud semantic
segmentation covering an area of around 1.43 km2 in the city centre of Turin
with almost 70M poi... | [
{
"version": "v1",
"created": "Tue, 8 Apr 2025 10:17:14 GMT"
}
] | 2025-04-09T00:00:00 | [
[
"Barco",
"Luca",
""
],
[
"Blanco",
"Giacomo",
""
],
[
"Chiriaco",
"Gaetano",
""
],
[
"Intini",
"Alessia",
""
],
[
"La Riccia",
"Luigi",
""
],
[
"Scolamiero",
"Vittorio",
""
],
[
"Boccardo",
"Piero",
""
],... | TITLE: Turin3D: Evaluating Adaptation Strategies under Label Scarcity in Urban
LiDAR Segmentation with Semi-Supervised Techniques
ABSTRACT: 3D semantic segmentation plays a critical role in urban modelling, enabling
detailed understanding and mapping of city environments. In this paper, we
introduce Turin3D: a ne... |
2504.05888 | Guillaume Gautier | Guillaume Gautier, Alexandre Mercat, Louis Fr\'eneau, Mikko
Pitk\"anen, and Jarno Vanne | UVG-VPC: Voxelized Point Cloud Dataset for Visual Volumetric Video-based
Coding | Point cloud compression;Geometry;Visualization;Three-dimensional
displays;Video sequences;Transform coding;Media;Open dataset;point
cloud;Visual Volumetric Video-based Coding (V3C);Video-based Point Cloud
Compression (V-PCC);Extended Reality (XR) | 2023 15th International Conference on Quality of Multimedia
Experience (QoMEX), Ghent, Belgium, 2023, pp. 244-247 | 10.1109/QoMEX58391.2023.10178589 | null | cs.MM cs.CV | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Point cloud compression has become a crucial factor in immersive visual media
processing and streaming. This paper presents a new open dataset called UVG-VPC
for the development, evaluation, and validation of MPEG Visual Volumetric
Video-based Coding (V3C) technology. The dataset is distributed under its own
non-comm... | [
{
"version": "v1",
"created": "Tue, 8 Apr 2025 10:27:53 GMT"
}
] | 2025-04-09T00:00:00 | [
[
"Gautier",
"Guillaume",
""
],
[
"Mercat",
"Alexandre",
""
],
[
"Fréneau",
"Louis",
""
],
[
"Pitkänen",
"Mikko",
""
],
[
"Vanne",
"Jarno",
""
]
] | TITLE: UVG-VPC: Voxelized Point Cloud Dataset for Visual Volumetric Video-based
Coding
ABSTRACT: Point cloud compression has become a crucial factor in immersive visual media
processing and streaming. This paper presents a new open dataset called UVG-VPC
for the development, evaluation, and validation of MPEG Vis... |
2504.05894 | Ivan Svetunkov | Ivan Svetunkov and Anna Sroginis | Why do zeroes happen? A model-based approach for demand classification | 39 pages, 11 figures, 3 tables | null | null | null | cs.LG stat.ME | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Effective demand forecasting is critical for inventory management, production
planning, and decision making across industries. Selecting the appropriate
model and suitable features to efficiently capture patterns in the data is one
of the main challenges in demand forecasting. In reality, this becomes even
more compl... | [
{
"version": "v1",
"created": "Tue, 8 Apr 2025 10:45:30 GMT"
}
] | 2025-04-09T00:00:00 | [
[
"Svetunkov",
"Ivan",
""
],
[
"Sroginis",
"Anna",
""
]
] | TITLE: Why do zeroes happen? A model-based approach for demand classification
ABSTRACT: Effective demand forecasting is critical for inventory management, production
planning, and decision making across industries. Selecting the appropriate
model and suitable features to efficiently capture patterns in the data is ... |
2504.05904 | Xiangyu Zheng | Xiangyu Zheng, Wanyun Li, Songcheng He, Xiaoqiang Li, We Zhang | Intrinsic Saliency Guided Trunk-Collateral Network for Unsupervised
Video Object Segmentation | null | null | null | null | cs.CV cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recent unsupervised video object segmentation (UVOS) methods predominantly
adopt the motion-appearance paradigm. Mainstream motion-appearance approaches
use either the two-encoder structure to separately encode motion and appearance
features, or the single-encoder structure for joint encoding. However, these
methods ... | [
{
"version": "v1",
"created": "Tue, 8 Apr 2025 11:02:14 GMT"
}
] | 2025-04-09T00:00:00 | [
[
"Zheng",
"Xiangyu",
""
],
[
"Li",
"Wanyun",
""
],
[
"He",
"Songcheng",
""
],
[
"Li",
"Xiaoqiang",
""
],
[
"Zhang",
"We",
""
]
] | TITLE: Intrinsic Saliency Guided Trunk-Collateral Network for Unsupervised
Video Object Segmentation
ABSTRACT: Recent unsupervised video object segmentation (UVOS) methods predominantly
adopt the motion-appearance paradigm. Mainstream motion-appearance approaches
use either the two-encoder structure to separately... |
2504.05908 | Sriram Mandalika | Sriram Mandalika, Lalitha V, Athira Nambiar | PRIMEDrive-CoT: A Precognitive Chain-of-Thought Framework for
Uncertainty-Aware Object Interaction in Driving Scene Scenario | Accepted at The IEEE/CVF Conference on Computer Vision and Pattern
Recognition 2025 - CVPRW | null | null | null | cs.CV cs.AI cs.LG | http://creativecommons.org/licenses/by/4.0/ | Driving scene understanding is a critical real-world problem that involves
interpreting and associating various elements of a driving environment, such as
vehicles, pedestrians, and traffic signals. Despite advancements in autonomous
driving, traditional pipelines rely on deterministic models that fail to
capture the... | [
{
"version": "v1",
"created": "Tue, 8 Apr 2025 11:06:02 GMT"
}
] | 2025-04-09T00:00:00 | [
[
"Mandalika",
"Sriram",
""
],
[
"V",
"Lalitha",
""
],
[
"Nambiar",
"Athira",
""
]
] | TITLE: PRIMEDrive-CoT: A Precognitive Chain-of-Thought Framework for
Uncertainty-Aware Object Interaction in Driving Scene Scenario
ABSTRACT: Driving scene understanding is a critical real-world problem that involves
interpreting and associating various elements of a driving environment, such as
vehicles, pedestr... |
2504.05914 | Abhiram Reddy Yanampally | Abhiram Reddy Yanampally | High-Resource Translation:Turning Abundance into Accessibility | 6 pages, 2 figures | null | null | null | cs.CL | http://creativecommons.org/licenses/by/4.0/ | This paper presents a novel approach to constructing an English-to-Telugu
translation model by leveraging transfer learning techniques and addressing the
challenges associated with low-resource languages. Utilizing the Bharat
Parallel Corpus Collection (BPCC) as the primary dataset, the model
incorporates iterative b... | [
{
"version": "v1",
"created": "Tue, 8 Apr 2025 11:09:51 GMT"
}
] | 2025-04-09T00:00:00 | [
[
"Yanampally",
"Abhiram Reddy",
""
]
] | TITLE: High-Resource Translation:Turning Abundance into Accessibility
ABSTRACT: This paper presents a novel approach to constructing an English-to-Telugu
translation model by leveraging transfer learning techniques and addressing the
challenges associated with low-resource languages. Utilizing the Bharat
Parallel C... |
2504.05917 | Solon Pissis | Giulia Bernardini and Huiping Chen and Alessio Conte and Roberto
Grossi and Veronica Guerrini and Grigorios Loukides and Nadia Pisanti and and
Solon P. Pissis | Indexing Strings with Utilities | ICDE 2025 (abstract abridged to satisfy arXiv requirements) | null | null | null | cs.DS cs.DB | http://creativecommons.org/licenses/by/4.0/ | Applications in domains ranging from bioinformatics to advertising feature
strings that come with numerical scores (utilities). The utilities quantify the
importance, interest, profit, or risk of the letters occurring at every
position of a string. Motivated by the ever-increasing rate of generating such
data, as wel... | [
{
"version": "v1",
"created": "Tue, 8 Apr 2025 11:13:53 GMT"
}
] | 2025-04-09T00:00:00 | [
[
"Bernardini",
"Giulia",
""
],
[
"Chen",
"Huiping",
""
],
[
"Conte",
"Alessio",
""
],
[
"Grossi",
"Roberto",
""
],
[
"Guerrini",
"Veronica",
""
],
[
"Loukides",
"Grigorios",
""
],
[
"Pisanti",
"Nadia",
""
... | TITLE: Indexing Strings with Utilities
ABSTRACT: Applications in domains ranging from bioinformatics to advertising feature
strings that come with numerical scores (utilities). The utilities quantify the
importance, interest, profit, or risk of the letters occurring at every
position of a string. Motivated by the e... |
2504.05923 | Juliett Su\'arez Ferreira | Juliett Su\'arez Ferreira, Marija Slavkovik, Jorge Casillas | Uncovering Fairness through Data Complexity as an Early Indicator | null | null | null | null | cs.LG cs.AI cs.DS | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Fairness constitutes a concern within machine learning (ML) applications.
Currently, there is no study on how disparities in classification complexity
between privileged and unprivileged groups could influence the fairness of
solutions, which serves as a preliminary indicator of potential unfairness. In
this work, we... | [
{
"version": "v1",
"created": "Tue, 8 Apr 2025 11:28:40 GMT"
}
] | 2025-04-09T00:00:00 | [
[
"Ferreira",
"Juliett Suárez",
""
],
[
"Slavkovik",
"Marija",
""
],
[
"Casillas",
"Jorge",
""
]
] | TITLE: Uncovering Fairness through Data Complexity as an Early Indicator
ABSTRACT: Fairness constitutes a concern within machine learning (ML) applications.
Currently, there is no study on how disparities in classification complexity
between privileged and unprivileged groups could influence the fairness of
solutio... |
2504.05957 | Julian Agudelo | Julian Agudelo and Vincent Guigue and Cristina Manfredotti and Hadrien
Piot | Drought forecasting using a hybrid neural architecture for integrating
time series and static data | 10 pages, 3 figures, published as a workshop paper at Tackling
Climate Change with Machine Learning at ICLR 2025, Tackling Climate Change
with Machine Learning is a non-archival workshop | null | null | null | cs.LG | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Reliable forecasting is critical for early warning systems and adaptive
drought management. Most previous deep learning approaches focus solely on
homogeneous regions and rely on single-structured data. This paper presents a
hybrid neural architecture that integrates time series and static data,
achieving state-of-th... | [
{
"version": "v1",
"created": "Tue, 8 Apr 2025 12:11:34 GMT"
}
] | 2025-04-09T00:00:00 | [
[
"Agudelo",
"Julian",
""
],
[
"Guigue",
"Vincent",
""
],
[
"Manfredotti",
"Cristina",
""
],
[
"Piot",
"Hadrien",
""
]
] | TITLE: Drought forecasting using a hybrid neural architecture for integrating
time series and static data
ABSTRACT: Reliable forecasting is critical for early warning systems and adaptive
drought management. Most previous deep learning approaches focus solely on
homogeneous regions and rely on single-structured d... |
2504.05966 | Xiaolin Fan | Xiaolin Fan, Yan Wang, Yingying Zhang, Mingkun Bao, Bosen Jia, Dong
Lu, Yifan Gu, Jian Cheng, and Haogang Zhu | AVP-AP: Self-supervised Automatic View Positioning in 3D cardiac CT via
Atlas Prompting | 12 pages, 8 figures, published to TMI | IEEE TRANSACTIONS ON MEDICAL IMAGING, March 2025 | 10.1109/TMI.2025.3554785 | null | eess.IV cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Automatic view positioning is crucial for cardiac computed tomography (CT)
examinations, including disease diagnosis and surgical planning. However, it is
highly challenging due to individual variability and large 3D search space.
Existing work needs labor-intensive and time-consuming manual annotations to
train view... | [
{
"version": "v1",
"created": "Tue, 8 Apr 2025 12:24:37 GMT"
}
] | 2025-04-09T00:00:00 | [
[
"Fan",
"Xiaolin",
""
],
[
"Wang",
"Yan",
""
],
[
"Zhang",
"Yingying",
""
],
[
"Bao",
"Mingkun",
""
],
[
"Jia",
"Bosen",
""
],
[
"Lu",
"Dong",
""
],
[
"Gu",
"Yifan",
""
],
[
"Cheng",
"Jian",
... | TITLE: AVP-AP: Self-supervised Automatic View Positioning in 3D cardiac CT via
Atlas Prompting
ABSTRACT: Automatic view positioning is crucial for cardiac computed tomography (CT)
examinations, including disease diagnosis and surgical planning. However, it is
highly challenging due to individual variability and l... |
2504.05977 | Jakob Christensen | Jakob L{\o}nborg Christensen, Morten Rieger Hannemose, Anders Bjorholm
Dahl, Vedrana Andersen Dahl | Diffusion Based Ambiguous Image Segmentation | Accepted at SCIA25 | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Medical image segmentation often involves inherent uncertainty due to
variations in expert annotations. Capturing this uncertainty is an important
goal and previous works have used various generative image models for the
purpose of representing the full distribution of plausible expert ground
truths. In this work, we... | [
{
"version": "v1",
"created": "Tue, 8 Apr 2025 12:33:26 GMT"
}
] | 2025-04-09T00:00:00 | [
[
"Christensen",
"Jakob Lønborg",
""
],
[
"Hannemose",
"Morten Rieger",
""
],
[
"Dahl",
"Anders Bjorholm",
""
],
[
"Dahl",
"Vedrana Andersen",
""
]
] | TITLE: Diffusion Based Ambiguous Image Segmentation
ABSTRACT: Medical image segmentation often involves inherent uncertainty due to
variations in expert annotations. Capturing this uncertainty is an important
goal and previous works have used various generative image models for the
purpose of representing the full ... |
2504.05992 | Jie Yang | Jie Yang, Chang Su, Yuhan Zhang, Jianjun Zhu and Jianli Wang | Under-Sampled High-Dimensional Data Recovery via Symbiotic Multi-Prior
Tensor Reconstruction | null | null | null | null | eess.IV cs.CV | http://creativecommons.org/licenses/by/4.0/ | The advancement of sensing technology has driven the widespread application
of high-dimensional data. However, issues such as missing entries during
acquisition and transmission negatively impact the accuracy of subsequent
tasks. Tensor reconstruction aims to recover the underlying complete data from
under-sampled ob... | [
{
"version": "v1",
"created": "Tue, 8 Apr 2025 12:55:18 GMT"
}
] | 2025-04-09T00:00:00 | [
[
"Yang",
"Jie",
""
],
[
"Su",
"Chang",
""
],
[
"Zhang",
"Yuhan",
""
],
[
"Zhu",
"Jianjun",
""
],
[
"Wang",
"Jianli",
""
]
] | TITLE: Under-Sampled High-Dimensional Data Recovery via Symbiotic Multi-Prior
Tensor Reconstruction
ABSTRACT: The advancement of sensing technology has driven the widespread application
of high-dimensional data. However, issues such as missing entries during
acquisition and transmission negatively impact the accu... |
2504.05995 | Firoj Alam | Firoj Alam, Md Arid Hasan, Sahinur Rahman Laskar, Mucahid Kutlu,
Shammur Absar Chowdhury | NativQA Framework: Enabling LLMs with Native, Local, and Everyday
Knowledge | LLMs, Native, Multilingual, Language Diversity, Contextual
Understanding, Minority Languages, Culturally Informed, Foundation Models,
Large Language Models | null | null | null | cs.CL cs.AI | http://creativecommons.org/licenses/by-nc-sa/4.0/ | The rapid advancement of large language models (LLMs) has raised concerns
about cultural bias, fairness, and their applicability in diverse linguistic
and underrepresented regional contexts. To enhance and benchmark the
capabilities of LLMs, there is a need to develop large-scale resources focused
on multilingual, lo... | [
{
"version": "v1",
"created": "Tue, 8 Apr 2025 13:01:51 GMT"
}
] | 2025-04-09T00:00:00 | [
[
"Alam",
"Firoj",
""
],
[
"Hasan",
"Md Arid",
""
],
[
"Laskar",
"Sahinur Rahman",
""
],
[
"Kutlu",
"Mucahid",
""
],
[
"Chowdhury",
"Shammur Absar",
""
]
] | TITLE: NativQA Framework: Enabling LLMs with Native, Local, and Everyday
Knowledge
ABSTRACT: The rapid advancement of large language models (LLMs) has raised concerns
about cultural bias, fairness, and their applicability in diverse linguistic
and underrepresented regional contexts. To enhance and benchmark the
c... |
2504.06003 | Can Zhang | Can Zhang and Gim Hee Lee | econSG: Efficient and Multi-view Consistent Open-Vocabulary 3D Semantic
Gaussians | null | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The primary focus of most recent works on open-vocabulary neural fields is
extracting precise semantic features from the VLMs and then consolidating them
efficiently into a multi-view consistent 3D neural fields representation.
However, most existing works over-trusted SAM to regularize image-level CLIP
without any f... | [
{
"version": "v1",
"created": "Tue, 8 Apr 2025 13:12:31 GMT"
}
] | 2025-04-09T00:00:00 | [
[
"Zhang",
"Can",
""
],
[
"Lee",
"Gim Hee",
""
]
] | TITLE: econSG: Efficient and Multi-view Consistent Open-Vocabulary 3D Semantic
Gaussians
ABSTRACT: The primary focus of most recent works on open-vocabulary neural fields is
extracting precise semantic features from the VLMs and then consolidating them
efficiently into a multi-view consistent 3D neural fields rep... |
2504.06004 | Mrityunjoy Gain | Mrityunjoy Gain, Kitae Kim, Avi Deb Raha, Apurba Adhikary, Eui-Nam
Huh, Zhu Han, and Choong Seon Hong | FedFeat+: A Robust Federated Learning Framework Through Federated
Aggregation and Differentially Private Feature-Based Classifier Retraining | null | null | null | null | cs.CV | http://creativecommons.org/licenses/by-nc-nd/4.0/ | In this paper, we propose the FedFeat+ framework, which distinctively
separates feature extraction from classification. We develop a two-tiered model
training process: following local training, clients transmit their weights and
some features extracted from the feature extractor from the final local epochs
to the ser... | [
{
"version": "v1",
"created": "Tue, 8 Apr 2025 13:12:38 GMT"
}
] | 2025-04-09T00:00:00 | [
[
"Gain",
"Mrityunjoy",
""
],
[
"Kim",
"Kitae",
""
],
[
"Raha",
"Avi Deb",
""
],
[
"Adhikary",
"Apurba",
""
],
[
"Huh",
"Eui-Nam",
""
],
[
"Han",
"Zhu",
""
],
[
"Hong",
"Choong Seon",
""
]
] | TITLE: FedFeat+: A Robust Federated Learning Framework Through Federated
Aggregation and Differentially Private Feature-Based Classifier Retraining
ABSTRACT: In this paper, we propose the FedFeat+ framework, which distinctively
separates feature extraction from classification. We develop a two-tiered model
traini... |
2504.06006 | Roman Kochnev | Roman Kochnev, Arash Torabi Goodarzi, Zofia Antonina Bentyn, Dmitry
Ignatov, Radu Timofte | Optuna vs Code Llama: Are LLMs a New Paradigm for Hyperparameter Tuning? | null | null | null | null | cs.LG cs.AI cs.NE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Optimal hyperparameter selection is critical for maximizing neural network
performance, especially as models grow in complexity. This work investigates
the viability of using large language models (LLMs) for hyperparameter
optimization by employing a fine-tuned version of Code Llama. Through
parameter-efficient fine-... | [
{
"version": "v1",
"created": "Tue, 8 Apr 2025 13:15:47 GMT"
}
] | 2025-04-09T00:00:00 | [
[
"Kochnev",
"Roman",
""
],
[
"Goodarzi",
"Arash Torabi",
""
],
[
"Bentyn",
"Zofia Antonina",
""
],
[
"Ignatov",
"Dmitry",
""
],
[
"Timofte",
"Radu",
""
]
] | TITLE: Optuna vs Code Llama: Are LLMs a New Paradigm for Hyperparameter Tuning?
ABSTRACT: Optimal hyperparameter selection is critical for maximizing neural network
performance, especially as models grow in complexity. This work investigates
the viability of using large language models (LLMs) for hyperparameter
opt... |
2504.06010 | Stefanos-Iordanis Papadopoulos | Stefanos-Iordanis Papadopoulos, Christos Koutlis, Symeon Papadopoulos,
Panagiotis C. Petrantonakis | Latent Multimodal Reconstruction for Misinformation Detection | null | null | null | null | cs.CV cs.MM | http://creativecommons.org/licenses/by-sa/4.0/ | Multimodal misinformation, such as miscaptioned images, where captions
misrepresent an image's origin, context, or meaning, poses a growing challenge
in the digital age. To support fact-checkers, researchers have been focusing on
creating datasets and developing methods for multimodal misinformation
detection (MMD). ... | [
{
"version": "v1",
"created": "Tue, 8 Apr 2025 13:16:48 GMT"
}
] | 2025-04-09T00:00:00 | [
[
"Papadopoulos",
"Stefanos-Iordanis",
""
],
[
"Koutlis",
"Christos",
""
],
[
"Papadopoulos",
"Symeon",
""
],
[
"Petrantonakis",
"Panagiotis C.",
""
]
] | TITLE: Latent Multimodal Reconstruction for Misinformation Detection
ABSTRACT: Multimodal misinformation, such as miscaptioned images, where captions
misrepresent an image's origin, context, or meaning, poses a growing challenge
in the digital age. To support fact-checkers, researchers have been focusing on
creatin... |
2504.06022 | Luis Denninger | Luis Denninger, Sina Mokhtarzadeh Azar, Juergen Gall | CamContextI2V: Context-aware Controllable Video Generation | null | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recently, image-to-video (I2V) diffusion models have demonstrated impressive
scene understanding and generative quality, incorporating image conditions to
guide generation. However, these models primarily animate static images without
extending beyond their provided context. Introducing additional constraints,
such a... | [
{
"version": "v1",
"created": "Tue, 8 Apr 2025 13:26:59 GMT"
}
] | 2025-04-09T00:00:00 | [
[
"Denninger",
"Luis",
""
],
[
"Azar",
"Sina Mokhtarzadeh",
""
],
[
"Gall",
"Juergen",
""
]
] | TITLE: CamContextI2V: Context-aware Controllable Video Generation
ABSTRACT: Recently, image-to-video (I2V) diffusion models have demonstrated impressive
scene understanding and generative quality, incorporating image conditions to
guide generation. However, these models primarily animate static images without
exten... |
2504.06039 | Julia Werner | Julia Werner, Christoph Gerum, Jorg Nick, Maxime Le Floch, Franz
Brinkmann, Jochen Hampe, and Oliver Bringmann | Enhanced Anomaly Detection for Capsule Endoscopy Using Ensemble Learning
Strategies | Accepted at the 47th Annual International Conference of the IEEE
Engineering in Medicine and Biology Society (EMBS EMBC) | null | null | null | cs.CV | http://creativecommons.org/licenses/by/4.0/ | Capsule endoscopy is a method to capture images of the gastrointestinal tract
and screen for diseases which might remain hidden if investigated with standard
endoscopes. Due to the limited size of a video capsule, embedding AI models
directly into the capsule demands careful consideration of the model size and
thus c... | [
{
"version": "v1",
"created": "Tue, 8 Apr 2025 13:39:39 GMT"
}
] | 2025-04-09T00:00:00 | [
[
"Werner",
"Julia",
""
],
[
"Gerum",
"Christoph",
""
],
[
"Nick",
"Jorg",
""
],
[
"Floch",
"Maxime Le",
""
],
[
"Brinkmann",
"Franz",
""
],
[
"Hampe",
"Jochen",
""
],
[
"Bringmann",
"Oliver",
""
]
] | TITLE: Enhanced Anomaly Detection for Capsule Endoscopy Using Ensemble Learning
Strategies
ABSTRACT: Capsule endoscopy is a method to capture images of the gastrointestinal tract
and screen for diseases which might remain hidden if investigated with standard
endoscopes. Due to the limited size of a video capsule,... |
2504.06055 | Panagiota Rempi | Panagiota Rempi, Sotiris Pelekis, Alexandros Menelaos Tzortzis,
Evangelos Karakolis, Christos Ntanos, Dimitris Askounis | Explainable AI for building energy retrofitting under data scarcity | null | null | null | null | cs.LG | http://creativecommons.org/licenses/by/4.0/ | Enhancing energy efficiency in residential buildings is a crucial step toward
mitigating climate change and reducing greenhouse gas emissions. Retrofitting
existing buildings, which account for a significant portion of energy
consumption, is critical particularly in regions with outdated and inefficient
building stoc... | [
{
"version": "v1",
"created": "Tue, 8 Apr 2025 14:00:08 GMT"
}
] | 2025-04-09T00:00:00 | [
[
"Rempi",
"Panagiota",
""
],
[
"Pelekis",
"Sotiris",
""
],
[
"Tzortzis",
"Alexandros Menelaos",
""
],
[
"Karakolis",
"Evangelos",
""
],
[
"Ntanos",
"Christos",
""
],
[
"Askounis",
"Dimitris",
""
]
] | TITLE: Explainable AI for building energy retrofitting under data scarcity
ABSTRACT: Enhancing energy efficiency in residential buildings is a crucial step toward
mitigating climate change and reducing greenhouse gas emissions. Retrofitting
existing buildings, which account for a significant portion of energy
consu... |
2504.06069 | Reza Masoudian Saadabad | Hanieh Masoudian Saadabad, Lingraj Kumar, Reza Masoudian Saadabad, and
Maja Colautti | Physics-Constrained Neural Network for Metasurface Optical Response
Prediction | null | null | null | null | physics.optics | http://creativecommons.org/licenses/by/4.0/ | A physics-constrained neural network is presented for predicting the optical
response of metasurfaces. Our approach incorporates physical laws directly into
the neural network architecture and loss function, addressing critical
challenges in the modeling of metasurfaces. Unlike methods that require
specialized weight... | [
{
"version": "v1",
"created": "Tue, 8 Apr 2025 14:10:28 GMT"
}
] | 2025-04-09T00:00:00 | [
[
"Saadabad",
"Hanieh Masoudian",
""
],
[
"Kumar",
"Lingraj",
""
],
[
"Saadabad",
"Reza Masoudian",
""
],
[
"Colautti",
"Maja",
""
]
] | TITLE: Physics-Constrained Neural Network for Metasurface Optical Response
Prediction
ABSTRACT: A physics-constrained neural network is presented for predicting the optical
response of metasurfaces. Our approach incorporates physical laws directly into
the neural network architecture and loss function, addressing... |
2504.06084 | Alexey Gavryushin | Alexey Gavryushin, Xi Wang, Robert J. S. Malate, Chenyu Yang, Xiangyi
Jia, Shubh Goel, Davide Liconti, Ren\'e Zurbr\"ugg, Robert K. Katzschmann,
Marc Pollefeys | MAPLE: Encoding Dexterous Robotic Manipulation Priors Learned From
Egocentric Videos | null | null | null | null | cs.RO cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Large-scale egocentric video datasets capture diverse human activities across
a wide range of scenarios, offering rich and detailed insights into how humans
interact with objects, especially those that require fine-grained dexterous
control. Such complex, dexterous skills with precise controls are crucial for
many ro... | [
{
"version": "v1",
"created": "Tue, 8 Apr 2025 14:25:25 GMT"
}
] | 2025-04-09T00:00:00 | [
[
"Gavryushin",
"Alexey",
""
],
[
"Wang",
"Xi",
""
],
[
"Malate",
"Robert J. S.",
""
],
[
"Yang",
"Chenyu",
""
],
[
"Jia",
"Xiangyi",
""
],
[
"Goel",
"Shubh",
""
],
[
"Liconti",
"Davide",
""
],
[
"Zur... | TITLE: MAPLE: Encoding Dexterous Robotic Manipulation Priors Learned From
Egocentric Videos
ABSTRACT: Large-scale egocentric video datasets capture diverse human activities across
a wide range of scenarios, offering rich and detailed insights into how humans
interact with objects, especially those that require fi... |
2504.06088 | Pramit Saha | Divyanshu Mishra, Pramit Saha, He Zhao, Netzahualcoyotl
Hernandez-Cruz, Olga Patey, Aris Papageorghiou, J. Alison Noble | MCAT: Visual Query-Based Localization of Standard Anatomical Clips in
Fetal Ultrasound Videos Using Multi-Tier Class-Aware Token Transformer | Accepted in AAAI 2025 | null | null | null | cs.CV cs.AI cs.LG | http://creativecommons.org/licenses/by/4.0/ | Accurate standard plane acquisition in fetal ultrasound (US) videos is
crucial for fetal growth assessment, anomaly detection, and adherence to
clinical guidelines. However, manually selecting standard frames is
time-consuming and prone to intra- and inter-sonographer variability. Existing
methods primarily rely on i... | [
{
"version": "v1",
"created": "Tue, 8 Apr 2025 14:29:15 GMT"
}
] | 2025-04-09T00:00:00 | [
[
"Mishra",
"Divyanshu",
""
],
[
"Saha",
"Pramit",
""
],
[
"Zhao",
"He",
""
],
[
"Hernandez-Cruz",
"Netzahualcoyotl",
""
],
[
"Patey",
"Olga",
""
],
[
"Papageorghiou",
"Aris",
""
],
[
"Noble",
"J. Alison",
""... | TITLE: MCAT: Visual Query-Based Localization of Standard Anatomical Clips in
Fetal Ultrasound Videos Using Multi-Tier Class-Aware Token Transformer
ABSTRACT: Accurate standard plane acquisition in fetal ultrasound (US) videos is
crucial for fetal growth assessment, anomaly detection, and adherence to
clinical gui... |
2504.06099 | \v{S}imon Bil\'ik | Samuel Bielik, Simon Bilik | Towards Varroa destructor mite detection using a narrow spectra
illumination | null | null | null | null | cs.CV cs.AI cs.LG | http://creativecommons.org/licenses/by/4.0/ | This paper focuses on the development and modification of a beehive
monitoring device and Varroa destructor detection on the bees with the help of
hyperspectral imagery while utilizing a U-net, semantic segmentation
architecture, and conventional computer vision methods. The main objectives
were to collect a dataset ... | [
{
"version": "v1",
"created": "Tue, 8 Apr 2025 14:41:42 GMT"
}
] | 2025-04-09T00:00:00 | [
[
"Bielik",
"Samuel",
""
],
[
"Bilik",
"Simon",
""
]
] | TITLE: Towards Varroa destructor mite detection using a narrow spectra
illumination
ABSTRACT: This paper focuses on the development and modification of a beehive
monitoring device and Varroa destructor detection on the bees with the help of
hyperspectral imagery while utilizing a U-net, semantic segmentation
arch... |
2504.06102 | Eric Wagner | Eric Wagner and Lennart Bader and Konrad Wolsing and Martin Serror | Sherlock: A Dataset for Process-aware Intrusion Detection Research on
Power Grid Networks | accepted at CODASPY'25 | null | 10.1145/3714393.3726006 | null | cs.CR cs.NI | http://creativecommons.org/licenses/by/4.0/ | Physically distributed components and legacy protocols make the protection of
power grids against increasing cyberattack threats challenging. Infamously, the
2015 and 2016 blackouts in Ukraine were caused by cyberattacks, and the German
Federal Office for Information Security (BSI) recorded over 200 cyber incidents
a... | [
{
"version": "v1",
"created": "Tue, 8 Apr 2025 14:46:35 GMT"
}
] | 2025-04-09T00:00:00 | [
[
"Wagner",
"Eric",
""
],
[
"Bader",
"Lennart",
""
],
[
"Wolsing",
"Konrad",
""
],
[
"Serror",
"Martin",
""
]
] | TITLE: Sherlock: A Dataset for Process-aware Intrusion Detection Research on
Power Grid Networks
ABSTRACT: Physically distributed components and legacy protocols make the protection of
power grids against increasing cyberattack threats challenging. Infamously, the
2015 and 2016 blackouts in Ukraine were caused by... |
2504.06105 | Abinav Kalyanasundaram | Abinav Kalyanasundaram, Karthikeyan Chandra Sekaran, Philipp Stauber,
Michael Lange, Wolfgang Utschick and Michael Botsch | Uncertainty-Aware Hybrid Machine Learning in Virtual Sensors for Vehicle
Sideslip Angle Estimation | Accepted at the 2025 IEEE Intelligent Vehicles Symposium (IV) | null | null | null | cs.RO cs.AI cs.LG | http://creativecommons.org/licenses/by/4.0/ | Precise vehicle state estimation is crucial for safe and reliable autonomous
driving. The number of measurable states and their precision offered by the
onboard vehicle sensor system are often constrained by cost. For instance,
measuring critical quantities such as the Vehicle Sideslip Angle (VSA) poses
significant c... | [
{
"version": "v1",
"created": "Tue, 8 Apr 2025 14:49:58 GMT"
}
] | 2025-04-09T00:00:00 | [
[
"Kalyanasundaram",
"Abinav",
""
],
[
"Sekaran",
"Karthikeyan Chandra",
""
],
[
"Stauber",
"Philipp",
""
],
[
"Lange",
"Michael",
""
],
[
"Utschick",
"Wolfgang",
""
],
[
"Botsch",
"Michael",
""
]
] | TITLE: Uncertainty-Aware Hybrid Machine Learning in Virtual Sensors for Vehicle
Sideslip Angle Estimation
ABSTRACT: Precise vehicle state estimation is crucial for safe and reliable autonomous
driving. The number of measurable states and their precision offered by the
onboard vehicle sensor system are often const... |
2504.06116 | Davide Sferrazza | Davide Sferrazza, Gabriele Berton, Gabriele Trivigno, Carlo Masone | To Match or Not to Match: Revisiting Image Matching for Reliable Visual
Place Recognition | CVPRW 2025 | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Visual Place Recognition (VPR) is a critical task in computer vision,
traditionally enhanced by re-ranking retrieval results with image matching.
However, recent advancements in VPR methods have significantly improved
performance, challenging the necessity of re-ranking. In this work, we show
that modern retrieval sy... | [
{
"version": "v1",
"created": "Tue, 8 Apr 2025 15:10:10 GMT"
}
] | 2025-04-09T00:00:00 | [
[
"Sferrazza",
"Davide",
""
],
[
"Berton",
"Gabriele",
""
],
[
"Trivigno",
"Gabriele",
""
],
[
"Masone",
"Carlo",
""
]
] | TITLE: To Match or Not to Match: Revisiting Image Matching for Reliable Visual
Place Recognition
ABSTRACT: Visual Place Recognition (VPR) is a critical task in computer vision,
traditionally enhanced by re-ranking retrieval results with image matching.
However, recent advancements in VPR methods have significantl... |
2504.06120 | Yuanpei Liu | Yuanpei Liu, Zhenqi He, Kai Han | Hyperbolic Category Discovery | Accepted as a conference paper at CVPR 2025 | null | null | null | cs.CV | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Generalized Category Discovery (GCD) is an intriguing open-world problem that
has garnered increasing attention. Given a dataset that includes both labelled
and unlabelled images, GCD aims to categorize all images in the unlabelled
subset, regardless of whether they belong to known or unknown classes. In GCD,
the com... | [
{
"version": "v1",
"created": "Tue, 8 Apr 2025 15:12:33 GMT"
}
] | 2025-04-09T00:00:00 | [
[
"Liu",
"Yuanpei",
""
],
[
"He",
"Zhenqi",
""
],
[
"Han",
"Kai",
""
]
] | TITLE: Hyperbolic Category Discovery
ABSTRACT: Generalized Category Discovery (GCD) is an intriguing open-world problem that
has garnered increasing attention. Given a dataset that includes both labelled
and unlabelled images, GCD aims to categorize all images in the unlabelled
subset, regardless of whether they be... |
2504.06121 | Yuhang Ma | Ronghui Zhang, Yuhang Ma, Tengfei Li, Ziyu Lin, Yueying Wu, Junzhou
Chen, Lin Zhang, Jia Hu, Tony Z. Qiu and Konghui Guo | A Robust Real-Time Lane Detection Method with Fog-Enhanced Feature
Fusion for Foggy Conditions | null | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Lane detection is a critical component of Advanced Driver Assistance Systems
(ADAS). Existing lane detection algorithms generally perform well under
favorable weather conditions. However, their performance degrades significantly
in adverse conditions, such as fog, which increases the risk of traffic
accidents. This c... | [
{
"version": "v1",
"created": "Tue, 8 Apr 2025 15:13:01 GMT"
}
] | 2025-04-09T00:00:00 | [
[
"Zhang",
"Ronghui",
""
],
[
"Ma",
"Yuhang",
""
],
[
"Li",
"Tengfei",
""
],
[
"Lin",
"Ziyu",
""
],
[
"Wu",
"Yueying",
""
],
[
"Chen",
"Junzhou",
""
],
[
"Zhang",
"Lin",
""
],
[
"Hu",
"Jia",
"... | TITLE: A Robust Real-Time Lane Detection Method with Fog-Enhanced Feature
Fusion for Foggy Conditions
ABSTRACT: Lane detection is a critical component of Advanced Driver Assistance Systems
(ADAS). Existing lane detection algorithms generally perform well under
favorable weather conditions. However, their performa... |
2504.06136 | Movina Moses | Movina Moses, Mohab Elkaref, James Barry, Shinnosuke Tanaka, Vishnudev
Kuruvanthodi, Nathan Herr, Campbell D Watson, Geeth De Mel | QGen Studio: An Adaptive Question-Answer Generation, Training and
Evaluation Platform | null | null | null | null | cs.CL cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | We present QGen Studio: an adaptive question-answer generation, training, and
evaluation platform. QGen Studio enables users to leverage large language
models (LLMs) to create custom question-answer datasets and fine-tune models on
this synthetic data. It features a dataset viewer and model explorer to
streamline thi... | [
{
"version": "v1",
"created": "Tue, 8 Apr 2025 15:32:09 GMT"
}
] | 2025-04-09T00:00:00 | [
[
"Moses",
"Movina",
""
],
[
"Elkaref",
"Mohab",
""
],
[
"Barry",
"James",
""
],
[
"Tanaka",
"Shinnosuke",
""
],
[
"Kuruvanthodi",
"Vishnudev",
""
],
[
"Herr",
"Nathan",
""
],
[
"Watson",
"Campbell D",
""
]... | TITLE: QGen Studio: An Adaptive Question-Answer Generation, Training and
Evaluation Platform
ABSTRACT: We present QGen Studio: an adaptive question-answer generation, training, and
evaluation platform. QGen Studio enables users to leverage large language
models (LLMs) to create custom question-answer datasets and... |
2504.06148 | Xiangxi Zheng | Xiangxi Zheng, Linjie Li, Zhengyuan Yang, Ping Yu, Alex Jinpeng Wang,
Rui Yan, Yuan Yao, Lijuan Wang | V-MAGE: A Game Evaluation Framework for Assessing Visual-Centric
Capabilities in Multimodal Large Language Models | null | null | null | null | cs.CV | http://creativecommons.org/licenses/by/4.0/ | Recent advancements in Multimodal Large Language Models (MLLMs) have led to
significant improvements across various multimodal benchmarks. However, as
evaluations shift from static datasets to open-world, dynamic environments,
current game-based benchmarks remain inadequate because they lack
visual-centric tasks and ... | [
{
"version": "v1",
"created": "Tue, 8 Apr 2025 15:43:01 GMT"
}
] | 2025-04-09T00:00:00 | [
[
"Zheng",
"Xiangxi",
""
],
[
"Li",
"Linjie",
""
],
[
"Yang",
"Zhengyuan",
""
],
[
"Yu",
"Ping",
""
],
[
"Wang",
"Alex Jinpeng",
""
],
[
"Yan",
"Rui",
""
],
[
"Yao",
"Yuan",
""
],
[
"Wang",
"Lijua... | TITLE: V-MAGE: A Game Evaluation Framework for Assessing Visual-Centric
Capabilities in Multimodal Large Language Models
ABSTRACT: Recent advancements in Multimodal Large Language Models (MLLMs) have led to
significant improvements across various multimodal benchmarks. However, as
evaluations shift from static da... |
2504.06153 | Akash Kumar | Akash Kumar, Ashlesha Kumar, Vibhav Vineet, Yogesh S Rawat | A Large-Scale Analysis on Contextual Self-Supervised Video
Representation Learning | CVPR'25 Workshop: 6th Data-Efficient Workshop | null | null | null | cs.CV | http://creativecommons.org/licenses/by/4.0/ | Self-supervised learning has emerged as a powerful paradigm for label-free
model pretraining, particularly in the video domain, where manual annotation is
costly and time-intensive. However, existing self-supervised approaches employ
diverse experimental setups, making direct comparisons challenging due to the
absenc... | [
{
"version": "v1",
"created": "Tue, 8 Apr 2025 15:47:58 GMT"
}
] | 2025-04-09T00:00:00 | [
[
"Kumar",
"Akash",
""
],
[
"Kumar",
"Ashlesha",
""
],
[
"Vineet",
"Vibhav",
""
],
[
"Rawat",
"Yogesh S",
""
]
] | TITLE: A Large-Scale Analysis on Contextual Self-Supervised Video
Representation Learning
ABSTRACT: Self-supervised learning has emerged as a powerful paradigm for label-free
model pretraining, particularly in the video domain, where manual annotation is
costly and time-intensive. However, existing self-supervise... |
2504.06156 | Chuanyu Li | Fangchen Liu, Chuanyu Li, Yihua Qin, Ankit Shaw, Jing Xu, Pieter
Abbeel, Rui Chen | ViTaMIn: Learning Contact-Rich Tasks Through Robot-Free Visuo-Tactile
Manipulation Interface | null | null | null | null | cs.RO | http://creativecommons.org/licenses/by/4.0/ | Tactile information plays a crucial role for humans and robots to interact
effectively with their environment, particularly for tasks requiring the
understanding of contact properties. Solving such dexterous manipulation tasks
often relies on imitation learning from demonstration datasets, which are
typically collect... | [
{
"version": "v1",
"created": "Tue, 8 Apr 2025 15:51:18 GMT"
}
] | 2025-04-09T00:00:00 | [
[
"Liu",
"Fangchen",
""
],
[
"Li",
"Chuanyu",
""
],
[
"Qin",
"Yihua",
""
],
[
"Shaw",
"Ankit",
""
],
[
"Xu",
"Jing",
""
],
[
"Abbeel",
"Pieter",
""
],
[
"Chen",
"Rui",
""
]
] | TITLE: ViTaMIn: Learning Contact-Rich Tasks Through Robot-Free Visuo-Tactile
Manipulation Interface
ABSTRACT: Tactile information plays a crucial role for humans and robots to interact
effectively with their environment, particularly for tasks requiring the
understanding of contact properties. Solving such dexter... |
2504.06158 | Saad Wazir | Saad Wazir, Daeyoung Kim | Rethinking the Nested U-Net Approach: Enhancing Biomarker Segmentation
with Attention Mechanisms and Multiscale Feature Fusion | Published in the Proceedings of the 2024 International Conference on
Medical Imaging and Computer-Aided Diagnosis (MICAD 2024), Lecture Notes in
Electrical Engineering (LNEE), Volume 1372, Springer Nature, Singapore | Lecture Notes in Electrical Engineering, vol. 1372, pp. 175-186,
Springer Nature, Singapore, 2025 | 10.1007/978-981-96-3863-5_17 | null | eess.IV cs.CV | http://creativecommons.org/licenses/by/4.0/ | Identifying biomarkers in medical images is vital for a wide range of biotech
applications. However, recent Transformer and CNN based methods often struggle
with variations in morphology and staining, which limits their feature
extraction capabilities. In medical image segmentation, where data samples are
often limit... | [
{
"version": "v1",
"created": "Tue, 8 Apr 2025 15:53:46 GMT"
}
] | 2025-04-09T00:00:00 | [
[
"Wazir",
"Saad",
""
],
[
"Kim",
"Daeyoung",
""
]
] | TITLE: Rethinking the Nested U-Net Approach: Enhancing Biomarker Segmentation
with Attention Mechanisms and Multiscale Feature Fusion
ABSTRACT: Identifying biomarkers in medical images is vital for a wide range of biotech
applications. However, recent Transformer and CNN based methods often struggle
with variatio... |
2504.06166 | Montgomery Gole | Montgomery Gole and Andriy Miranskyy | Assessing how hyperparameters impact Large Language Models' sarcasm
detection performance | null | null | null | null | cs.CL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Sarcasm detection is challenging for both humans and machines. This work
explores how model characteristics impact sarcasm detection in OpenAI's GPT,
and Meta's Llama-2 models, given their strong natural language understanding,
and popularity. We evaluate fine-tuned and zero-shot models across various
sizes, releases... | [
{
"version": "v1",
"created": "Tue, 8 Apr 2025 16:05:25 GMT"
}
] | 2025-04-09T00:00:00 | [
[
"Gole",
"Montgomery",
""
],
[
"Miranskyy",
"Andriy",
""
]
] | TITLE: Assessing how hyperparameters impact Large Language Models' sarcasm
detection performance
ABSTRACT: Sarcasm detection is challenging for both humans and machines. This work
explores how model characteristics impact sarcasm detection in OpenAI's GPT,
and Meta's Llama-2 models, given their strong natural lan... |
2504.06176 | Ian Groves | Ian Groves, Andrew Campbell, James Fernandes, Diego Rodriguez, Paul
Murray, Massimiliano Vasile, Victoria Nockles | A Self-Supervised Framework for Space Object Behaviour Characterisation | 15 pages, 10 figures | null | null | null | cs.LG cs.AI physics.space-ph | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Foundation Models, pre-trained on large unlabelled datasets before
task-specific fine-tuning, are increasingly being applied to specialised
domains. Recent examples include ClimaX for climate and Clay for satellite
Earth observation, but a Foundation Model for Space Object Behavioural Analysis
has not yet been develo... | [
{
"version": "v1",
"created": "Tue, 8 Apr 2025 16:19:19 GMT"
}
] | 2025-04-09T00:00:00 | [
[
"Groves",
"Ian",
""
],
[
"Campbell",
"Andrew",
""
],
[
"Fernandes",
"James",
""
],
[
"Rodriguez",
"Diego",
""
],
[
"Murray",
"Paul",
""
],
[
"Vasile",
"Massimiliano",
""
],
[
"Nockles",
"Victoria",
""
]
] | TITLE: A Self-Supervised Framework for Space Object Behaviour Characterisation
ABSTRACT: Foundation Models, pre-trained on large unlabelled datasets before
task-specific fine-tuning, are increasingly being applied to specialised
domains. Recent examples include ClimaX for climate and Clay for satellite
Earth observ... |
2504.06185 | Vanessa Borst | Vanessa Borst, Timo Dittus, Tassilo Dege, Astrid Schmieder, and Samuel
Kounev | WoundAmbit: Bridging State-of-the-Art Semantic Segmentation and
Real-World Wound Care | Main paper: 17 pages; supplementary material: 16 pages; paper
submitted to the application track of the European Conference on Machine
Learning and Principles and Practice of Knowledge Discovery in Databases
(ECML PKDD 2025) | null | null | null | cs.CV cs.AI | http://creativecommons.org/licenses/by/4.0/ | Chronic wounds affect a large population, particularly the elderly and
diabetic patients, who often exhibit limited mobility and co-existing health
conditions. Automated wound monitoring via mobile image capture can reduce
in-person physician visits by enabling remote tracking of wound size. Semantic
segmentation is ... | [
{
"version": "v1",
"created": "Tue, 8 Apr 2025 16:25:59 GMT"
}
] | 2025-04-09T00:00:00 | [
[
"Borst",
"Vanessa",
""
],
[
"Dittus",
"Timo",
""
],
[
"Dege",
"Tassilo",
""
],
[
"Schmieder",
"Astrid",
""
],
[
"Kounev",
"Samuel",
""
]
] | TITLE: WoundAmbit: Bridging State-of-the-Art Semantic Segmentation and
Real-World Wound Care
ABSTRACT: Chronic wounds affect a large population, particularly the elderly and
diabetic patients, who often exhibit limited mobility and co-existing health
conditions. Automated wound monitoring via mobile image capture... |
2504.06193 | Zongyue Qin | Zongyue Qin, Shichang Zhang, Mingxuan Ju, Tong Zhao, Neil Shah, Yizhou
Sun | Heuristic Methods are Good Teachers to Distill MLPs for Graph Link
Prediction | null | null | null | null | cs.LG cs.AI | http://creativecommons.org/licenses/by/4.0/ | Link prediction is a crucial graph-learning task with applications including
citation prediction and product recommendation. Distilling Graph Neural
Networks (GNNs) teachers into Multi-Layer Perceptrons (MLPs) students has
emerged as an effective approach to achieve strong performance and reducing
computational cost ... | [
{
"version": "v1",
"created": "Tue, 8 Apr 2025 16:35:11 GMT"
}
] | 2025-04-09T00:00:00 | [
[
"Qin",
"Zongyue",
""
],
[
"Zhang",
"Shichang",
""
],
[
"Ju",
"Mingxuan",
""
],
[
"Zhao",
"Tong",
""
],
[
"Shah",
"Neil",
""
],
[
"Sun",
"Yizhou",
""
]
] | TITLE: Heuristic Methods are Good Teachers to Distill MLPs for Graph Link
Prediction
ABSTRACT: Link prediction is a crucial graph-learning task with applications including
citation prediction and product recommendation. Distilling Graph Neural
Networks (GNNs) teachers into Multi-Layer Perceptrons (MLPs) students ... |
2504.06196 | Shekoofeh Azizi | Eric Wang, Samuel Schmidgall, Paul F. Jaeger, Fan Zhang, Rory Pilgrim,
Yossi Matias, Joelle Barral, David Fleet, Shekoofeh Azizi | TxGemma: Efficient and Agentic LLMs for Therapeutics | null | null | null | null | cs.AI cs.CL cs.LG | http://creativecommons.org/licenses/by/4.0/ | Therapeutic development is a costly and high-risk endeavor that is often
plagued by high failure rates. To address this, we introduce TxGemma, a suite
of efficient, generalist large language models (LLMs) capable of therapeutic
property prediction as well as interactive reasoning and explainability. Unlike
task-speci... | [
{
"version": "v1",
"created": "Tue, 8 Apr 2025 16:39:02 GMT"
}
] | 2025-04-09T00:00:00 | [
[
"Wang",
"Eric",
""
],
[
"Schmidgall",
"Samuel",
""
],
[
"Jaeger",
"Paul F.",
""
],
[
"Zhang",
"Fan",
""
],
[
"Pilgrim",
"Rory",
""
],
[
"Matias",
"Yossi",
""
],
[
"Barral",
"Joelle",
""
],
[
"Fleet"... | TITLE: TxGemma: Efficient and Agentic LLMs for Therapeutics
ABSTRACT: Therapeutic development is a costly and high-risk endeavor that is often
plagued by high failure rates. To address this, we introduce TxGemma, a suite
of efficient, generalist large language models (LLMs) capable of therapeutic
property predictio... |
2504.06207 | Moncef Garouani | Moncef Garouani | An experimental survey and Perspective View on Meta-Learning for
Automated Algorithms Selection and Parametrization | null | null | null | null | cs.LG cs.AI | http://creativecommons.org/licenses/by/4.0/ | Considerable progress has been made in the recent literature studies to
tackle the Algorithms Selection and Parametrization (ASP) problem, which is
diversified in multiple meta-learning setups. Yet there is a lack of surveys
and comparative evaluations that critically analyze, summarize and assess the
performance of ... | [
{
"version": "v1",
"created": "Tue, 8 Apr 2025 16:51:22 GMT"
}
] | 2025-04-09T00:00:00 | [
[
"Garouani",
"Moncef",
""
]
] | TITLE: An experimental survey and Perspective View on Meta-Learning for
Automated Algorithms Selection and Parametrization
ABSTRACT: Considerable progress has been made in the recent literature studies to
tackle the Algorithms Selection and Parametrization (ASP) problem, which is
diversified in multiple meta-lear... |
2504.06219 | Dongyang Fan | Dongyang Fan, Vinko Sabol\v{c}ec, Matin Ansaripour, Ayush Kumar Tarun,
Martin Jaggi, Antoine Bosselut, Imanol Schlag | Can Performant LLMs Be Ethical? Quantifying the Impact of Web Crawling
Opt-Outs | null | null | null | null | cs.CL cs.LG | http://creativecommons.org/licenses/by/4.0/ | The increasing adoption of web crawling opt-outs by copyright holders of
online content raises critical questions about the impact of data compliance on
large language model (LLM) performance. However, little is known about how
these restrictions (and the resultant filtering of pretraining datasets) affect
the capabi... | [
{
"version": "v1",
"created": "Tue, 8 Apr 2025 17:08:06 GMT"
}
] | 2025-04-09T00:00:00 | [
[
"Fan",
"Dongyang",
""
],
[
"Sabolčec",
"Vinko",
""
],
[
"Ansaripour",
"Matin",
""
],
[
"Tarun",
"Ayush Kumar",
""
],
[
"Jaggi",
"Martin",
""
],
[
"Bosselut",
"Antoine",
""
],
[
"Schlag",
"Imanol",
""
]
] | TITLE: Can Performant LLMs Be Ethical? Quantifying the Impact of Web Crawling
Opt-Outs
ABSTRACT: The increasing adoption of web crawling opt-outs by copyright holders of
online content raises critical questions about the impact of data compliance on
large language model (LLM) performance. However, little is known... |
2504.06227 | Krithi Shailya | Krithi Shailya, Shreya Rajpal, Gokul S Krishnan, Balaraman Ravindran | LExT: Towards Evaluating Trustworthiness of Natural Language
Explanations | null | null | null | null | cs.CL | http://creativecommons.org/licenses/by/4.0/ | As Large Language Models (LLMs) become increasingly integrated into
high-stakes domains, there have been several approaches proposed toward
generating natural language explanations. These explanations are crucial for
enhancing the interpretability of a model, especially in sensitive domains like
healthcare, where tra... | [
{
"version": "v1",
"created": "Tue, 8 Apr 2025 17:16:52 GMT"
}
] | 2025-04-09T00:00:00 | [
[
"Shailya",
"Krithi",
""
],
[
"Rajpal",
"Shreya",
""
],
[
"Krishnan",
"Gokul S",
""
],
[
"Ravindran",
"Balaraman",
""
]
] | TITLE: LExT: Towards Evaluating Trustworthiness of Natural Language
Explanations
ABSTRACT: As Large Language Models (LLMs) become increasingly integrated into
high-stakes domains, there have been several approaches proposed toward
generating natural language explanations. These explanations are crucial for
enhanc... |
2504.06235 | Shahryar Zehtabi | Shahryar Zehtabi, Dong-Jun Han, Seyyedali Hosseinalipour, Christopher
G. Brinton | Decentralized Federated Domain Generalization with Style Sharing: A
Formal Modeling and Convergence Analysis | null | null | null | null | cs.LG cs.AI | http://creativecommons.org/licenses/by/4.0/ | Much of the federated learning (FL) literature focuses on settings where
local dataset statistics remain the same between training and testing time.
Recent advances in domain generalization (DG) aim to use data from source
(training) domains to train a model that generalizes well to data from unseen
target (testing) ... | [
{
"version": "v1",
"created": "Tue, 8 Apr 2025 17:32:56 GMT"
}
] | 2025-04-09T00:00:00 | [
[
"Zehtabi",
"Shahryar",
""
],
[
"Han",
"Dong-Jun",
""
],
[
"Hosseinalipour",
"Seyyedali",
""
],
[
"Brinton",
"Christopher G.",
""
]
] | TITLE: Decentralized Federated Domain Generalization with Style Sharing: A
Formal Modeling and Convergence Analysis
ABSTRACT: Much of the federated learning (FL) literature focuses on settings where
local dataset statistics remain the same between training and testing time.
Recent advances in domain generalizatio... |
2504.06237 | Mina Bishay | Mina Bishay, Graham Page, Waleed Emad, and Mohammad Mavadati | Monitoring Viewer Attention During Online Ads | Presented at the ECCV 2024 Workshops | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Nowadays, video ads spread through numerous online platforms, and are being
watched by millions of viewers worldwide. Big brands gauge the liking and
purchase intent of their new ads, by analyzing the facial responses of viewers
recruited online to watch the ads from home or work. Although this approach
captures natu... | [
{
"version": "v1",
"created": "Tue, 8 Apr 2025 17:34:02 GMT"
}
] | 2025-04-09T00:00:00 | [
[
"Bishay",
"Mina",
""
],
[
"Page",
"Graham",
""
],
[
"Emad",
"Waleed",
""
],
[
"Mavadati",
"Mohammad",
""
]
] | TITLE: Monitoring Viewer Attention During Online Ads
ABSTRACT: Nowadays, video ads spread through numerous online platforms, and are being
watched by millions of viewers worldwide. Big brands gauge the liking and
purchase intent of their new ads, by analyzing the facial responses of viewers
recruited online to watc... |
2504.06263 | Yiying Yang | Yiying Yang, Wei Cheng, Sijin Chen, Xianfang Zeng, Jiaxu Zhang, Liao
Wang, Gang Yu, Xingjun Ma, Yu-Gang Jiang | OmniSVG: A Unified Scalable Vector Graphics Generation Model | 18 pages; Project Page: https://omnisvg.github.io/ | null | null | null | cs.CV | http://creativecommons.org/licenses/by/4.0/ | Scalable Vector Graphics (SVG) is an important image format widely adopted in
graphic design because of their resolution independence and editability. The
study of generating high-quality SVG has continuously drawn attention from both
designers and researchers in the AIGC community. However, existing methods
either p... | [
{
"version": "v1",
"created": "Tue, 8 Apr 2025 17:59:49 GMT"
}
] | 2025-04-09T00:00:00 | [
[
"Yang",
"Yiying",
""
],
[
"Cheng",
"Wei",
""
],
[
"Chen",
"Sijin",
""
],
[
"Zeng",
"Xianfang",
""
],
[
"Zhang",
"Jiaxu",
""
],
[
"Wang",
"Liao",
""
],
[
"Yu",
"Gang",
""
],
[
"Ma",
"Xingjun",
... | TITLE: OmniSVG: A Unified Scalable Vector Graphics Generation Model
ABSTRACT: Scalable Vector Graphics (SVG) is an important image format widely adopted in
graphic design because of their resolution independence and editability. The
study of generating high-quality SVG has continuously drawn attention from both
des... |
2504.06264 | Jisang Han | Jisang Han, Honggyu An, Jaewoo Jung, Takuya Narihira, Junyoung Seo,
Kazumi Fukuda, Chaehyun Kim, Sunghwan Hong, Yuki Mitsufuji, Seungryong Kim | D^2USt3R: Enhancing 3D Reconstruction with 4D Pointmaps for Dynamic
Scenes | project page: https://cvlab-kaist.github.io/DDUSt3R/ | null | null | null | cs.CV | http://creativecommons.org/licenses/by/4.0/ | We address the task of 3D reconstruction in dynamic scenes, where object
motions degrade the quality of previous 3D pointmap regression methods, such as
DUSt3R, originally designed for static 3D scene reconstruction. Although these
methods provide an elegant and powerful solution in static settings, they
struggle in ... | [
{
"version": "v1",
"created": "Tue, 8 Apr 2025 17:59:50 GMT"
}
] | 2025-04-09T00:00:00 | [
[
"Han",
"Jisang",
""
],
[
"An",
"Honggyu",
""
],
[
"Jung",
"Jaewoo",
""
],
[
"Narihira",
"Takuya",
""
],
[
"Seo",
"Junyoung",
""
],
[
"Fukuda",
"Kazumi",
""
],
[
"Kim",
"Chaehyun",
""
],
[
"Hong",
... | TITLE: D^2USt3R: Enhancing 3D Reconstruction with 4D Pointmaps for Dynamic
Scenes
ABSTRACT: We address the task of 3D reconstruction in dynamic scenes, where object
motions degrade the quality of previous 3D pointmap regression methods, such as
DUSt3R, originally designed for static 3D scene reconstruction. Altho... |
2108.11328 | Shibal Ibrahim | Shibal Ibrahim, Peter Radchenko, Emanuel Ben-David, Rahul Mazumder | Predicting Census Survey Response Rates With Parsimonious Additive
Models and Structured Interactions | Published in Annals of Applied Statistics | The Annals of Applied Statistics 2025, Vol. 19, No. 1, 94-120 | 10.1214/24-AOAS1929 | null | stat.ML cs.LG stat.AP stat.CO | http://creativecommons.org/licenses/by/4.0/ | In this paper, we consider the problem of predicting survey response rates
using a family of flexible and interpretable nonparametric models. The study is
motivated by the US Census Bureau's well-known ROAM application, which uses a
linear regression model trained on the US Census Planning Database data to
identify h... | [
{
"version": "v1",
"created": "Tue, 24 Aug 2021 17:49:55 GMT"
},
{
"version": "v2",
"created": "Wed, 8 Jun 2022 16:09:18 GMT"
},
{
"version": "v3",
"created": "Fri, 26 May 2023 17:10:01 GMT"
},
{
"version": "v4",
"created": "Thu, 7 Dec 2023 19:05:08 GMT"
},
{
"ver... | 2025-04-08T00:00:00 | [
[
"Ibrahim",
"Shibal",
""
],
[
"Radchenko",
"Peter",
""
],
[
"Ben-David",
"Emanuel",
""
],
[
"Mazumder",
"Rahul",
""
]
] | TITLE: Predicting Census Survey Response Rates With Parsimonious Additive
Models and Structured Interactions
ABSTRACT: In this paper, we consider the problem of predicting survey response rates
using a family of flexible and interpretable nonparametric models. The study is
motivated by the US Census Bureau's well... |
2201.12577 | John Chiang | John Chiang | Volley Revolver: A Novel Matrix-Encoding Method for Privacy-Preserving
Neural Networks (Inference) | The encoding method we proposed in this work, $\texttt{Volley
Revolver}$, is particularly tailored for privacy-preserving neural networks.
There is a great chance that it can be used to assist the private neural
networks training, in which case for the backpropagation algorithm of the
fully-connected layer the ... | null | null | null | cs.CR cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this work, we present a novel matrix-encoding method that is particularly
convenient for neural networks to make predictions in a privacy-preserving
manner using homomorphic encryption. Based on this encoding method, we
implement a convolutional neural network for handwritten image classification
over encryption. ... | [
{
"version": "v1",
"created": "Sat, 29 Jan 2022 12:40:19 GMT"
},
{
"version": "v2",
"created": "Sun, 14 Aug 2022 06:44:34 GMT"
},
{
"version": "v3",
"created": "Wed, 29 Mar 2023 12:14:21 GMT"
},
{
"version": "v4",
"created": "Tue, 9 Jan 2024 00:52:21 GMT"
},
{
"ve... | 2025-04-08T00:00:00 | [
[
"Chiang",
"John",
""
]
] | TITLE: Volley Revolver: A Novel Matrix-Encoding Method for Privacy-Preserving
Neural Networks (Inference)
ABSTRACT: In this work, we present a novel matrix-encoding method that is particularly
convenient for neural networks to make predictions in a privacy-preserving
manner using homomorphic encryption. Based on ... |
2305.12352 | Wenzhi Gao | Yanguang Chen, Wenzhi Gao, Wanyu Zhang, Dongdong Ge, Huikang Liu,
Yinyu Ye | Data-driven Mixed Integer Optimization through Probabilistic
Multi-variable Branching | null | null | null | null | math.OC cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we propose a Pre-trained Mixed Integer Optimization framework
(PreMIO) that accelerates online mixed integer program (MIP) solving with
offline datasets and machine learning models. Our method is based on a
data-driven multi-variable cardinality branching procedure that splits the MIP
feasible region u... | [
{
"version": "v1",
"created": "Sun, 21 May 2023 05:11:30 GMT"
},
{
"version": "v2",
"created": "Tue, 5 Nov 2024 21:46:50 GMT"
},
{
"version": "v3",
"created": "Fri, 4 Apr 2025 18:09:21 GMT"
}
] | 2025-04-08T00:00:00 | [
[
"Chen",
"Yanguang",
""
],
[
"Gao",
"Wenzhi",
""
],
[
"Zhang",
"Wanyu",
""
],
[
"Ge",
"Dongdong",
""
],
[
"Liu",
"Huikang",
""
],
[
"Ye",
"Yinyu",
""
]
] | TITLE: Data-driven Mixed Integer Optimization through Probabilistic
Multi-variable Branching
ABSTRACT: In this paper, we propose a Pre-trained Mixed Integer Optimization framework
(PreMIO) that accelerates online mixed integer program (MIP) solving with
offline datasets and machine learning models. Our method is ... |
2307.00976 | Ruitao Xie | Ruimin Ma, Ruitao Xie, Yanlin Wang, Jintao Meng, Yanjie Wei, Wenhui
Xi, Yi Pan | Autism Spectrum Disorder Classification with Interpretability in
Children based on Structural MRI Features Extracted using Contrastive
Variational Autoencoder | null | Big Data Mining and Analytics, 2024, 7(3): 781-793 | 10.26599/BDMA.2024.9020004 | null | cs.CV | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Autism spectrum disorder (ASD) is a highly disabling mental disease that
brings significant impairments of social interaction ability to the patients,
making early screening and intervention of ASD critical. With the development
of the machine learning and neuroimaging technology, extensive research has
been conducte... | [
{
"version": "v1",
"created": "Mon, 3 Jul 2023 12:46:19 GMT"
},
{
"version": "v2",
"created": "Mon, 7 Apr 2025 08:32:48 GMT"
}
] | 2025-04-08T00:00:00 | [
[
"Ma",
"Ruimin",
""
],
[
"Xie",
"Ruitao",
""
],
[
"Wang",
"Yanlin",
""
],
[
"Meng",
"Jintao",
""
],
[
"Wei",
"Yanjie",
""
],
[
"Xi",
"Wenhui",
""
],
[
"Pan",
"Yi",
""
]
] | TITLE: Autism Spectrum Disorder Classification with Interpretability in
Children based on Structural MRI Features Extracted using Contrastive
Variational Autoencoder
ABSTRACT: Autism spectrum disorder (ASD) is a highly disabling mental disease that
brings significant impairments of social interaction ability to... |
2307.14591 | Junchao Huang | Junchao Huang, Xiaoqi He Yebo Wu and Sheng Zhao | The detection and rectification for identity-switch based on unfalsified
control | null | null | null | null | cs.CV cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The purpose of multi-object tracking (MOT) is to continuously track and
identify objects detected in videos. Currently, most methods for multi-object
tracking model the motion information and combine it with appearance
information to determine and track objects. In this paper, unfalsified control
is employed to addre... | [
{
"version": "v1",
"created": "Thu, 27 Jul 2023 02:30:12 GMT"
},
{
"version": "v2",
"created": "Sun, 6 Apr 2025 13:11:14 GMT"
}
] | 2025-04-08T00:00:00 | [
[
"Huang",
"Junchao",
""
],
[
"Wu",
"Xiaoqi He Yebo",
""
],
[
"Zhao",
"Sheng",
""
]
] | TITLE: The detection and rectification for identity-switch based on unfalsified
control
ABSTRACT: The purpose of multi-object tracking (MOT) is to continuously track and
identify objects detected in videos. Currently, most methods for multi-object
tracking model the motion information and combine it with appearan... |
2307.16082 | Mohammadali Sefidi Esfahani | Mohammadali Sefidi Esfahani, Mohammad Akbari | EnrichEvent: Enriching Social Data with Contextual Information for
Emerging Event Extraction | null | null | null | null | cs.CL cs.AI | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Social platforms have emerged as crucial platforms for distributing
information and discussing social events, offering researchers an excellent
opportunity to design and implement novel event detection frameworks.
Identifying unspecified events and detecting events without prior knowledge
enables governments, aid age... | [
{
"version": "v1",
"created": "Sat, 29 Jul 2023 21:37:55 GMT"
},
{
"version": "v2",
"created": "Wed, 16 Aug 2023 09:00:25 GMT"
},
{
"version": "v3",
"created": "Mon, 25 Dec 2023 14:27:55 GMT"
},
{
"version": "v4",
"created": "Wed, 27 Dec 2023 09:58:25 GMT"
},
{
"v... | 2025-04-08T00:00:00 | [
[
"Esfahani",
"Mohammadali Sefidi",
""
],
[
"Akbari",
"Mohammad",
""
]
] | TITLE: EnrichEvent: Enriching Social Data with Contextual Information for
Emerging Event Extraction
ABSTRACT: Social platforms have emerged as crucial platforms for distributing
information and discussing social events, offering researchers an excellent
opportunity to design and implement novel event detection fr... |
2309.02712 | Amir H Gandomi | Shams Forruque Ahmed, Md. Sakib Bin Alam, Maliha Kabir, Shaila Afrin,
Sabiha Jannat Rafa, Aanushka Mehjabin, Amir H. Gandomi | Unveiling the frontiers of deep learning: innovations shaping diverse
domains | 88 pages, 11 figures, 7 tables | Applied Intelligence, 55(7), 573 (2025) | 10.1007/s10489-025-06259-x | null | cs.LG cs.AI cs.NE | http://creativecommons.org/licenses/by/4.0/ | Deep learning (DL) allows computer models to learn, visualize, optimize,
refine, and predict data. To understand its present state, examining the most
recent advancements and applications of deep learning across various domains is
essential. However, prior reviews focused on DL applications in only one or two
domains... | [
{
"version": "v1",
"created": "Wed, 6 Sep 2023 04:50:39 GMT"
},
{
"version": "v2",
"created": "Sat, 5 Apr 2025 01:29:03 GMT"
}
] | 2025-04-08T00:00:00 | [
[
"Ahmed",
"Shams Forruque",
""
],
[
"Alam",
"Md. Sakib Bin",
""
],
[
"Kabir",
"Maliha",
""
],
[
"Afrin",
"Shaila",
""
],
[
"Rafa",
"Sabiha Jannat",
""
],
[
"Mehjabin",
"Aanushka",
""
],
[
"Gandomi",
"Amir H.",
... | TITLE: Unveiling the frontiers of deep learning: innovations shaping diverse
domains
ABSTRACT: Deep learning (DL) allows computer models to learn, visualize, optimize,
refine, and predict data. To understand its present state, examining the most
recent advancements and applications of deep learning across various... |
2309.14770 | Haotian Li | Haotian Li, Bin Yu, Yuliang Wei, Kai Wang, Richard Yi Da Xu, Bailing
Wang | KERMIT: Knowledge Graph Completion of Enhanced Relation Modeling with
Inverse Transformation | Accepted to Knowledge-Based Systems | null | null | null | cs.CL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Knowledge graph completion (KGC) revolves around populating missing triples
in a knowledge graph using available information. Text-based methods, which
depend on textual descriptions of triples, often encounter difficulties when
these descriptions lack sufficient information for accurate prediction-an issue
inherent ... | [
{
"version": "v1",
"created": "Tue, 26 Sep 2023 09:03:25 GMT"
},
{
"version": "v2",
"created": "Sat, 3 Aug 2024 13:34:24 GMT"
},
{
"version": "v3",
"created": "Mon, 7 Apr 2025 03:07:25 GMT"
}
] | 2025-04-08T00:00:00 | [
[
"Li",
"Haotian",
""
],
[
"Yu",
"Bin",
""
],
[
"Wei",
"Yuliang",
""
],
[
"Wang",
"Kai",
""
],
[
"Da Xu",
"Richard Yi",
""
],
[
"Wang",
"Bailing",
""
]
] | TITLE: KERMIT: Knowledge Graph Completion of Enhanced Relation Modeling with
Inverse Transformation
ABSTRACT: Knowledge graph completion (KGC) revolves around populating missing triples
in a knowledge graph using available information. Text-based methods, which
depend on textual descriptions of triples, often enc... |
2310.08453 | Jian Wu | Jian Wu, Carol Flannagan, Ulrich Sander, and Jonas B\"argman | Modeling Lead-vehicle Kinematics For Rear-end Crash Scenario Generation | null | IEEETrans.Intell.Transp.Syst. 25 (2024) 3176-3186 | 10.1109/TITS.2024.3369097 | null | cs.RO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The use of virtual safety assessment as the primary method for evaluating
vehicle safety technologies has emphasized the importance of crash scenario
generation. One of the most common crash types is the rear-end crash, which
involves a lead vehicle and a following vehicle. Most studies have focused on
the following ... | [
{
"version": "v1",
"created": "Fri, 22 Sep 2023 10:21:17 GMT"
},
{
"version": "v2",
"created": "Fri, 13 Oct 2023 07:16:21 GMT"
}
] | 2025-04-08T00:00:00 | [
[
"Wu",
"Jian",
""
],
[
"Flannagan",
"Carol",
""
],
[
"Sander",
"Ulrich",
""
],
[
"Bärgman",
"Jonas",
""
]
] | TITLE: Modeling Lead-vehicle Kinematics For Rear-end Crash Scenario Generation
ABSTRACT: The use of virtual safety assessment as the primary method for evaluating
vehicle safety technologies has emphasized the importance of crash scenario
generation. One of the most common crash types is the rear-end crash, which
i... |
2310.11439 | Quentin Bouniot | Quentin Bouniot, Ievgen Redko, Anton Mallasto, Charlotte Laclau,
Oliver Struckmeier, Karol Arndt, Markus Heinonen, Ville Kyrki, Samuel Kaski | From Alexnet to Transformers: Measuring the Non-linearity of Deep Neural
Networks with Affine Optimal Transport | Code available at https://github.com/qbouniot/AffScoreDeep | null | null | null | cs.LG cs.AI stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In the last decade, we have witnessed the introduction of several novel deep
neural network (DNN) architectures exhibiting ever-increasing performance
across diverse tasks. Explaining the upward trend of their performance,
however, remains difficult as different DNN architectures of comparable depth
and width -- comm... | [
{
"version": "v1",
"created": "Tue, 17 Oct 2023 17:50:22 GMT"
},
{
"version": "v2",
"created": "Mon, 10 Jun 2024 09:29:21 GMT"
},
{
"version": "v3",
"created": "Mon, 1 Jul 2024 14:39:54 GMT"
},
{
"version": "v4",
"created": "Sun, 6 Apr 2025 16:31:38 GMT"
}
] | 2025-04-08T00:00:00 | [
[
"Bouniot",
"Quentin",
""
],
[
"Redko",
"Ievgen",
""
],
[
"Mallasto",
"Anton",
""
],
[
"Laclau",
"Charlotte",
""
],
[
"Struckmeier",
"Oliver",
""
],
[
"Arndt",
"Karol",
""
],
[
"Heinonen",
"Markus",
""
],
... | TITLE: From Alexnet to Transformers: Measuring the Non-linearity of Deep Neural
Networks with Affine Optimal Transport
ABSTRACT: In the last decade, we have witnessed the introduction of several novel deep
neural network (DNN) architectures exhibiting ever-increasing performance
across diverse tasks. Explaining t... |
2310.14778 | Jinzheng Zhao | Jinzheng Zhao, Yong Xu, Xinyuan Qian, Davide Berghi, Peipei Wu, Meng
Cui, Jianyuan Sun, Philip J.B. Jackson and Wenwu Wang | Audio-Visual Speaker Tracking: Progress, Challenges, and Future
Directions | null | null | null | null | cs.MM cs.SD eess.AS | http://creativecommons.org/licenses/by/4.0/ | Audio-visual speaker tracking has drawn increasing attention over the past
few years due to its academic values and wide applications. Audio and visual
modalities can provide complementary information for localization and tracking.
With audio and visual information, the Bayesian-based filter and deep
learning-based m... | [
{
"version": "v1",
"created": "Mon, 23 Oct 2023 10:29:33 GMT"
},
{
"version": "v2",
"created": "Sun, 17 Dec 2023 08:35:04 GMT"
},
{
"version": "v3",
"created": "Thu, 19 Dec 2024 11:49:06 GMT"
},
{
"version": "v4",
"created": "Sun, 6 Apr 2025 03:02:18 GMT"
}
] | 2025-04-08T00:00:00 | [
[
"Zhao",
"Jinzheng",
""
],
[
"Xu",
"Yong",
""
],
[
"Qian",
"Xinyuan",
""
],
[
"Berghi",
"Davide",
""
],
[
"Wu",
"Peipei",
""
],
[
"Cui",
"Meng",
""
],
[
"Sun",
"Jianyuan",
""
],
[
"Jackson",
"Phi... | TITLE: Audio-Visual Speaker Tracking: Progress, Challenges, and Future
Directions
ABSTRACT: Audio-visual speaker tracking has drawn increasing attention over the past
few years due to its academic values and wide applications. Audio and visual
modalities can provide complementary information for localization and ... |
2310.18542 | Shibal Ibrahim | Shibal Ibrahim and Kayhan Behdin and Rahul Mazumder | End-to-end Feature Selection Approach for Learning Skinny Trees | Published in AISTATS 2024 | International Conference on Artificial Intelligence and Statistics
(AISTATS) 2024 | null | null | cs.LG | http://creativecommons.org/licenses/by/4.0/ | We propose a new optimization-based approach for feature selection in tree
ensembles, an important problem in statistics and machine learning. Popular
tree ensemble toolkits e.g., Gradient Boosted Trees and Random Forests support
feature selection post-training based on feature importance scores, while very
popular, ... | [
{
"version": "v1",
"created": "Sat, 28 Oct 2023 00:15:10 GMT"
},
{
"version": "v2",
"created": "Tue, 3 Sep 2024 07:34:54 GMT"
},
{
"version": "v3",
"created": "Sun, 6 Apr 2025 03:10:53 GMT"
}
] | 2025-04-08T00:00:00 | [
[
"Ibrahim",
"Shibal",
""
],
[
"Behdin",
"Kayhan",
""
],
[
"Mazumder",
"Rahul",
""
]
] | TITLE: End-to-end Feature Selection Approach for Learning Skinny Trees
ABSTRACT: We propose a new optimization-based approach for feature selection in tree
ensembles, an important problem in statistics and machine learning. Popular
tree ensemble toolkits e.g., Gradient Boosted Trees and Random Forests support
featu... |
2310.18651 | Ali Javidani | Ali Javidani, Mohammad Amin Sadeghi, Babak Nadjar Araabi | Patch-Wise Self-Supervised Visual Representation Learning: A
Fine-Grained Approach | 15 pages | null | 10.1007/s11760-025-04020-y | null | cs.CV | http://creativecommons.org/licenses/by/4.0/ | Self-supervised visual representation learning traditionally focuses on
image-level instance discrimination. Our study introduces an innovative,
fine-grained dimension by integrating patch-level discrimination into these
methodologies. This integration allows for the simultaneous analysis of local
and global visual f... | [
{
"version": "v1",
"created": "Sat, 28 Oct 2023 09:35:30 GMT"
},
{
"version": "v2",
"created": "Mon, 6 Nov 2023 07:52:31 GMT"
},
{
"version": "v3",
"created": "Tue, 7 Nov 2023 07:02:59 GMT"
},
{
"version": "v4",
"created": "Sat, 16 Dec 2023 10:50:45 GMT"
},
{
"ver... | 2025-04-08T00:00:00 | [
[
"Javidani",
"Ali",
""
],
[
"Sadeghi",
"Mohammad Amin",
""
],
[
"Araabi",
"Babak Nadjar",
""
]
] | TITLE: Patch-Wise Self-Supervised Visual Representation Learning: A
Fine-Grained Approach
ABSTRACT: Self-supervised visual representation learning traditionally focuses on
image-level instance discrimination. Our study introduces an innovative,
fine-grained dimension by integrating patch-level discrimination into... |
2311.00635 | Andrea Giuseppe Di Francesco | Andrea Giuseppe Di Francesco, Giuliano Giampietro, Indro Spinelli and
Danilo Comminiello | GATSY: Graph Attention Network for Music Artist Similarity | Accepted at IJCNN 2025 | null | null | null | cs.IR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The artist similarity quest has become a crucial subject in social and
scientific contexts, driven by the desire to enhance music discovery according
to user preferences. Modern research solutions facilitate music discovery
according to user tastes. However, defining similarity among artists remains
challenging due t... | [
{
"version": "v1",
"created": "Wed, 1 Nov 2023 16:36:19 GMT"
},
{
"version": "v2",
"created": "Sat, 5 Apr 2025 18:14:41 GMT"
}
] | 2025-04-08T00:00:00 | [
[
"Di Francesco",
"Andrea Giuseppe",
""
],
[
"Giampietro",
"Giuliano",
""
],
[
"Spinelli",
"Indro",
""
],
[
"Comminiello",
"Danilo",
""
]
] | TITLE: GATSY: Graph Attention Network for Music Artist Similarity
ABSTRACT: The artist similarity quest has become a crucial subject in social and
scientific contexts, driven by the desire to enhance music discovery according
to user preferences. Modern research solutions facilitate music discovery
according to use... |
2311.08176 | Jingru Fu | Jingru Fu, Daniel Ferreira, \"Orjan Smedby, Rodrigo Moreno | A deformation-based morphometry framework for disentangling Alzheimer's
disease from normal aging using learned normal aging templates | 21 pages, 8 figures | null | 10.1038/s41598-025-96234-w | null | cs.CV | http://creativecommons.org/licenses/by/4.0/ | Alzheimer's Disease and normal aging are both characterized by brain atrophy.
The question of whether AD-related brain atrophy represents accelerated aging
or a neurodegeneration process distinct from that in normal aging remains
unresolved. Moreover, precisely disentangling AD-related brain atrophy from
normal aging... | [
{
"version": "v1",
"created": "Tue, 14 Nov 2023 14:04:35 GMT"
}
] | 2025-04-08T00:00:00 | [
[
"Fu",
"Jingru",
""
],
[
"Ferreira",
"Daniel",
""
],
[
"Smedby",
"Örjan",
""
],
[
"Moreno",
"Rodrigo",
""
]
] | TITLE: A deformation-based morphometry framework for disentangling Alzheimer's
disease from normal aging using learned normal aging templates
ABSTRACT: Alzheimer's Disease and normal aging are both characterized by brain atrophy.
The question of whether AD-related brain atrophy represents accelerated aging
or a n... |
2312.00502 | Aristotelis Ballas | Aristotelis Ballas, Vasileios Papapanagiotou and Christos Diou | Which Augmentation Should I Use? An Empirical Investigation of
Augmentations for Self-Supervised Phonocardiogram Representation Learning | Accepted in IEEE ACCESS: https://doi.org/10.1109/ACCESS.2024.3519297 | null | 10.1109/ACCESS.2024.3519297 | null | cs.LG cs.SD eess.AS q-bio.QM | http://creativecommons.org/licenses/by/4.0/ | Despite recent advancements in deep learning, its application in real-world
medical settings, such as phonocardiogram (PCG) classification, remains
limited. A significant barrier is the lack of high-quality annotated datasets,
which hampers the development of robust, generalizable models that can perform
well on newl... | [
{
"version": "v1",
"created": "Fri, 1 Dec 2023 11:06:00 GMT"
},
{
"version": "v2",
"created": "Mon, 18 Mar 2024 10:32:01 GMT"
},
{
"version": "v3",
"created": "Fri, 5 Apr 2024 11:19:12 GMT"
},
{
"version": "v4",
"created": "Wed, 11 Dec 2024 09:53:49 GMT"
},
{
"ver... | 2025-04-08T00:00:00 | [
[
"Ballas",
"Aristotelis",
""
],
[
"Papapanagiotou",
"Vasileios",
""
],
[
"Diou",
"Christos",
""
]
] | TITLE: Which Augmentation Should I Use? An Empirical Investigation of
Augmentations for Self-Supervised Phonocardiogram Representation Learning
ABSTRACT: Despite recent advancements in deep learning, its application in real-world
medical settings, such as phonocardiogram (PCG) classification, remains
limited. A s... |
2312.08034 | Mushfiqur Rahman | Mushfiqur Rahman, Runze Liu, Chau-Wai Wong, Huaiyu Dai | Individualized Deepfake Detection Exploiting Traces Due to Double
Neural-Network Operations | null | null | null | null | eess.IV cs.CR cs.CV cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In today's digital landscape, journalists urgently require tools to verify
the authenticity of facial images and videos depicting specific public figures
before incorporating them into news stories. Existing deepfake detectors are
not optimized for this detection task when an image is associated with a
specific and i... | [
{
"version": "v1",
"created": "Wed, 13 Dec 2023 10:21:00 GMT"
},
{
"version": "v2",
"created": "Fri, 4 Apr 2025 21:05:01 GMT"
}
] | 2025-04-08T00:00:00 | [
[
"Rahman",
"Mushfiqur",
""
],
[
"Liu",
"Runze",
""
],
[
"Wong",
"Chau-Wai",
""
],
[
"Dai",
"Huaiyu",
""
]
] | TITLE: Individualized Deepfake Detection Exploiting Traces Due to Double
Neural-Network Operations
ABSTRACT: In today's digital landscape, journalists urgently require tools to verify
the authenticity of facial images and videos depicting specific public figures
before incorporating them into news stories. Existi... |
2312.11952 | Collin Leiber | Collin Leiber and Dominik Mautz and Claudia Plant and Christian B\"ohm | Automatic Parameter Selection for Non-Redundant Clustering | null | Proceedings of the 2022 SIAM International Conference on Data
Mining (SDM) (pp. 226-234). Society for Industrial and Applied Mathematics | 10.1137/1.9781611977172.26 | null | cs.LG cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | High-dimensional datasets often contain multiple meaningful clusterings in
different subspaces. For example, objects can be clustered either by color,
weight, or size, revealing different interpretations of the given dataset. A
variety of approaches are able to identify such non-redundant clusterings.
However, most o... | [
{
"version": "v1",
"created": "Tue, 19 Dec 2023 08:53:00 GMT"
},
{
"version": "v2",
"created": "Mon, 7 Apr 2025 07:13:36 GMT"
}
] | 2025-04-08T00:00:00 | [
[
"Leiber",
"Collin",
""
],
[
"Mautz",
"Dominik",
""
],
[
"Plant",
"Claudia",
""
],
[
"Böhm",
"Christian",
""
]
] | TITLE: Automatic Parameter Selection for Non-Redundant Clustering
ABSTRACT: High-dimensional datasets often contain multiple meaningful clusterings in
different subspaces. For example, objects can be clustered either by color,
weight, or size, revealing different interpretations of the given dataset. A
variety of a... |
2401.07702 | Christopher Davis | Christopher Davis, Andrew Caines, {\O}istein Andersen, Shiva
Taslimipoor, Helen Yannakoudakis, Zheng Yuan, Christopher Bryant, Marek Rei,
Paula Buttery | Prompting open-source and commercial language models for grammatical
error correction of English learner text | 8 pages with appendices; accepted to ACL Findings 2024 | null | 10.18653/v1/2024.findings-acl.711 | null | cs.CL | http://creativecommons.org/licenses/by/4.0/ | Thanks to recent advances in generative AI, we are able to prompt large
language models (LLMs) to produce texts which are fluent and grammatical. In
addition, it has been shown that we can elicit attempts at grammatical error
correction (GEC) from LLMs when prompted with ungrammatical input sentences. We
evaluate how... | [
{
"version": "v1",
"created": "Mon, 15 Jan 2024 14:19:47 GMT"
},
{
"version": "v2",
"created": "Sun, 6 Apr 2025 11:25:39 GMT"
}
] | 2025-04-08T00:00:00 | [
[
"Davis",
"Christopher",
""
],
[
"Caines",
"Andrew",
""
],
[
"Andersen",
"Øistein",
""
],
[
"Taslimipoor",
"Shiva",
""
],
[
"Yannakoudakis",
"Helen",
""
],
[
"Yuan",
"Zheng",
""
],
[
"Bryant",
"Christopher",
... | TITLE: Prompting open-source and commercial language models for grammatical
error correction of English learner text
ABSTRACT: Thanks to recent advances in generative AI, we are able to prompt large
language models (LLMs) to produce texts which are fluent and grammatical. In
addition, it has been shown that we ca... |
2401.09234 | Alfredo Go\~ni Sarriguren | Alfredo Go\~ni Sarriguren | SARRIGUREN: a polynomial-time complete algorithm for random $k$-SAT with
relatively dense clauses | 24 pages, 2 figures, 8 tables, algorithms, results and data in
https://goo.su/zV3Pt6E | null | null | null | cs.DS cs.CC | http://creativecommons.org/licenses/by-nc-sa/4.0/ | SARRIGUREN, a new complete algorithm for SAT based on counting clauses (which
is valid also for Unique-SAT and #SAT) is described, analyzed and tested.
Although existing complete algorithms for SAT perform slower with clauses with
many literals, that is an advantage for SARRIGUREN, because the more literals
are in th... | [
{
"version": "v1",
"created": "Wed, 17 Jan 2024 14:23:55 GMT"
},
{
"version": "v2",
"created": "Mon, 7 Apr 2025 08:42:46 GMT"
}
] | 2025-04-08T00:00:00 | [
[
"Sarriguren",
"Alfredo Goñi",
""
]
] | TITLE: SARRIGUREN: a polynomial-time complete algorithm for random $k$-SAT with
relatively dense clauses
ABSTRACT: SARRIGUREN, a new complete algorithm for SAT based on counting clauses (which
is valid also for Unique-SAT and #SAT) is described, analyzed and tested.
Although existing complete algorithms for SAT p... |
2402.02085 | Long Ma | Long Ma, Zhiyuan Yan, Qinglang Guo, Yong Liao, Haiyang Yu, Pengyuan
Zhou | Detecting AI-Generated Video via Frame Consistency | null | null | null | null | cs.CV cs.AI | http://creativecommons.org/licenses/by/4.0/ | The escalating quality of video generated by advanced video generation
methods results in new security challenges, while there have been few relevant
research efforts: 1) There is no open-source dataset for generated video
detection, 2) No generated video detection method has been proposed so far. To
this end, we pro... | [
{
"version": "v1",
"created": "Sat, 3 Feb 2024 08:52:06 GMT"
},
{
"version": "v2",
"created": "Tue, 6 Feb 2024 02:51:00 GMT"
},
{
"version": "v3",
"created": "Mon, 3 Jun 2024 11:00:25 GMT"
},
{
"version": "v4",
"created": "Wed, 26 Jun 2024 03:32:50 GMT"
},
{
"vers... | 2025-04-08T00:00:00 | [
[
"Ma",
"Long",
""
],
[
"Yan",
"Zhiyuan",
""
],
[
"Guo",
"Qinglang",
""
],
[
"Liao",
"Yong",
""
],
[
"Yu",
"Haiyang",
""
],
[
"Zhou",
"Pengyuan",
""
]
] | TITLE: Detecting AI-Generated Video via Frame Consistency
ABSTRACT: The escalating quality of video generated by advanced video generation
methods results in new security challenges, while there have been few relevant
research efforts: 1) There is no open-source dataset for generated video
detection, 2) No generate... |
2402.05675 | Tong Chen | Tong Chen, Raghavendra Selvan | Is Adversarial Training with Compressed Datasets Effective? | 22 pages, 10 figures, 3 tables, accepted at Scandinavian Conference
on Image Analysis 2025 (SCIA 2025) | null | null | null | cs.LG | http://creativecommons.org/licenses/by/4.0/ | Dataset Condensation (DC) refers to the recent class of dataset compression
methods that generate a smaller, synthetic, dataset from a larger dataset. This
synthetic dataset aims to retain the essential information of the original
dataset, enabling models trained on it to achieve performance levels comparable
to thos... | [
{
"version": "v1",
"created": "Thu, 8 Feb 2024 13:53:11 GMT"
},
{
"version": "v2",
"created": "Mon, 7 Apr 2025 17:31:31 GMT"
}
] | 2025-04-08T00:00:00 | [
[
"Chen",
"Tong",
""
],
[
"Selvan",
"Raghavendra",
""
]
] | TITLE: Is Adversarial Training with Compressed Datasets Effective?
ABSTRACT: Dataset Condensation (DC) refers to the recent class of dataset compression
methods that generate a smaller, synthetic, dataset from a larger dataset. This
synthetic dataset aims to retain the essential information of the original
dataset,... |
2402.09081 | Dan Garber | Dan Garber, Atara Kaplan | Low-Rank Extragradient Methods for Scalable Semidefinite Optimization | This version corrects an error in the previous version, as well as in
the short version published in \textit{Operations Research Letters}
\cite{garber2025low}: while in those versions we reported $\mathcal{O}(1/T)$
rates for the \textbf{best iterate}, in this corrected version these rates
hold only w.r.t. the \... | null | null | null | math.OC cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We consider several classes of highly important semidefinite optimization
problems that involve both a convex objective function (smooth or nonsmooth)
and additional linear or nonlinear smooth and convex constraints, which are
ubiquitous in statistics, machine learning, combinatorial optimization, and
other domains. ... | [
{
"version": "v1",
"created": "Wed, 14 Feb 2024 10:48:00 GMT"
},
{
"version": "v2",
"created": "Mon, 7 Apr 2025 09:36:31 GMT"
}
] | 2025-04-08T00:00:00 | [
[
"Garber",
"Dan",
""
],
[
"Kaplan",
"Atara",
""
]
] | TITLE: Low-Rank Extragradient Methods for Scalable Semidefinite Optimization
ABSTRACT: We consider several classes of highly important semidefinite optimization
problems that involve both a convex objective function (smooth or nonsmooth)
and additional linear or nonlinear smooth and convex constraints, which are
ub... |
2402.14802 | Andrea Giuseppe Di Francesco | Andrea Giuseppe Di Francesco, Francesco Caso, Maria Sofia Bucarelli
and Fabrizio Silvestri | Link Prediction with Physics-Inspired Graph Neural Networks | Accepted at IJCNN 2025 | null | null | null | cs.LG cs.IR cs.SI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The message-passing mechanism underlying Graph Neural Networks (GNNs) is not
naturally suited for heterophilic datasets, where adjacent nodes often have
different labels. Most solutions to this problem remain confined to the task of
node classification. In this article, we focus on the valuable task of link
predictio... | [
{
"version": "v1",
"created": "Thu, 22 Feb 2024 18:56:31 GMT"
},
{
"version": "v2",
"created": "Sat, 5 Apr 2025 18:19:08 GMT"
}
] | 2025-04-08T00:00:00 | [
[
"Di Francesco",
"Andrea Giuseppe",
""
],
[
"Caso",
"Francesco",
""
],
[
"Bucarelli",
"Maria Sofia",
""
],
[
"Silvestri",
"Fabrizio",
""
]
] | TITLE: Link Prediction with Physics-Inspired Graph Neural Networks
ABSTRACT: The message-passing mechanism underlying Graph Neural Networks (GNNs) is not
naturally suited for heterophilic datasets, where adjacent nodes often have
different labels. Most solutions to this problem remain confined to the task of
node c... |
2403.08462 | Andrea Nini | Andrea Nini, Oren Halvani, Lukas Graner, Valerio Gherardi, Shunichi
Ishihara | Grammar as a Behavioral Biometric: Using Cognitively Motivated Grammar
Models for Authorship Verification | null | null | null | null | cs.CL cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Authorship Verification (AV) is a key area of research in digital text
forensics, which addresses the fundamental question of whether two texts were
written by the same person. Numerous computational approaches have been
proposed over the last two decades in an attempt to address this challenge.
However, existing AV ... | [
{
"version": "v1",
"created": "Wed, 13 Mar 2024 12:25:47 GMT"
},
{
"version": "v2",
"created": "Mon, 7 Apr 2025 11:12:57 GMT"
}
] | 2025-04-08T00:00:00 | [
[
"Nini",
"Andrea",
""
],
[
"Halvani",
"Oren",
""
],
[
"Graner",
"Lukas",
""
],
[
"Gherardi",
"Valerio",
""
],
[
"Ishihara",
"Shunichi",
""
]
] | TITLE: Grammar as a Behavioral Biometric: Using Cognitively Motivated Grammar
Models for Authorship Verification
ABSTRACT: Authorship Verification (AV) is a key area of research in digital text
forensics, which addresses the fundamental question of whether two texts were
written by the same person. Numerous compu... |
2403.10045 | Eric Xue | Eric Xue, Yijiang Li, Haoyang Liu, Peiran Wang, Yifan Shen, Haohan
Wang | Towards Adversarially Robust Dataset Distillation by Curvature
Regularization | AAAI 2025 | null | null | null | cs.LG cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Dataset distillation (DD) allows datasets to be distilled to fractions of
their original size while preserving the rich distributional information, so
that models trained on the distilled datasets can achieve a comparable accuracy
while saving significant computational loads. Recent research in this area has
been foc... | [
{
"version": "v1",
"created": "Fri, 15 Mar 2024 06:31:03 GMT"
},
{
"version": "v2",
"created": "Thu, 19 Dec 2024 21:39:24 GMT"
},
{
"version": "v3",
"created": "Mon, 31 Mar 2025 21:23:30 GMT"
},
{
"version": "v4",
"created": "Fri, 4 Apr 2025 20:27:58 GMT"
}
] | 2025-04-08T00:00:00 | [
[
"Xue",
"Eric",
""
],
[
"Li",
"Yijiang",
""
],
[
"Liu",
"Haoyang",
""
],
[
"Wang",
"Peiran",
""
],
[
"Shen",
"Yifan",
""
],
[
"Wang",
"Haohan",
""
]
] | TITLE: Towards Adversarially Robust Dataset Distillation by Curvature
Regularization
ABSTRACT: Dataset distillation (DD) allows datasets to be distilled to fractions of
their original size while preserving the rich distributional information, so
that models trained on the distilled datasets can achieve a comparab... |
2403.12529 | Brian Godwin Lim | Brian Godwin Lim, Galvin Brice Sy Lim, Renzo Roel Tan, Kazushi Ikeda | Contextualized Messages Boost Graph Representations | Published in Transactions on Machine Learning Research | null | null | null | cs.LG | http://creativecommons.org/licenses/by/4.0/ | Graph neural networks (GNNs) have gained significant attention in recent
years for their ability to process data that may be represented as graphs. This
has prompted several studies to explore their representational capability based
on the graph isomorphism task. Notably, these works inherently assume a
countable nod... | [
{
"version": "v1",
"created": "Tue, 19 Mar 2024 08:05:49 GMT"
},
{
"version": "v2",
"created": "Wed, 22 May 2024 09:02:33 GMT"
},
{
"version": "v3",
"created": "Mon, 30 Sep 2024 12:56:50 GMT"
},
{
"version": "v4",
"created": "Mon, 7 Apr 2025 11:27:48 GMT"
}
] | 2025-04-08T00:00:00 | [
[
"Lim",
"Brian Godwin",
""
],
[
"Lim",
"Galvin Brice Sy",
""
],
[
"Tan",
"Renzo Roel",
""
],
[
"Ikeda",
"Kazushi",
""
]
] | TITLE: Contextualized Messages Boost Graph Representations
ABSTRACT: Graph neural networks (GNNs) have gained significant attention in recent
years for their ability to process data that may be represented as graphs. This
has prompted several studies to explore their representational capability based
on the graph i... |
2403.15304 | Yahya Badran | Yahya Badran, Christine Preisach | Addressing Label Leakage in Knowledge Tracing Models | null | Proceedings of the 17th International Conference on Computer
Supported Education (CSEDU) - Volume 2, 2025, pp. 85-95 | 10.5220/0013275200003932 | null | cs.CY cs.AI cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Knowledge Tracing (KT) is concerned with predicting students' future
performance on learning items in intelligent tutoring systems. Learning items
are tagged with skill labels called knowledge concepts (KCs). Many KT models
expand the sequence of item-student interactions into KC-student interactions
by replacing lea... | [
{
"version": "v1",
"created": "Fri, 22 Mar 2024 15:54:30 GMT"
},
{
"version": "v2",
"created": "Thu, 11 Apr 2024 16:39:54 GMT"
},
{
"version": "v3",
"created": "Mon, 7 Apr 2025 15:00:58 GMT"
}
] | 2025-04-08T00:00:00 | [
[
"Badran",
"Yahya",
""
],
[
"Preisach",
"Christine",
""
]
] | TITLE: Addressing Label Leakage in Knowledge Tracing Models
ABSTRACT: Knowledge Tracing (KT) is concerned with predicting students' future
performance on learning items in intelligent tutoring systems. Learning items
are tagged with skill labels called knowledge concepts (KCs). Many KT models
expand the sequence of... |
2404.05014 | Jinfa Huang | Shenghai Yuan, Jinfa Huang, Yujun Shi, Yongqi Xu, Ruijie Zhu, Bin Lin,
Xinhua Cheng, Li Yuan, Jiebo Luo | MagicTime: Time-lapse Video Generation Models as Metamorphic Simulators | TPAMI 2025 | null | null | null | cs.CV | http://creativecommons.org/licenses/by/4.0/ | Recent advances in Text-to-Video generation (T2V) have achieved remarkable
success in synthesizing high-quality general videos from textual descriptions.
A largely overlooked problem in T2V is that existing models have not adequately
encoded physical knowledge of the real world, thus generated videos tend to
have lim... | [
{
"version": "v1",
"created": "Sun, 7 Apr 2024 16:49:07 GMT"
},
{
"version": "v2",
"created": "Sun, 6 Apr 2025 03:43:41 GMT"
}
] | 2025-04-08T00:00:00 | [
[
"Yuan",
"Shenghai",
""
],
[
"Huang",
"Jinfa",
""
],
[
"Shi",
"Yujun",
""
],
[
"Xu",
"Yongqi",
""
],
[
"Zhu",
"Ruijie",
""
],
[
"Lin",
"Bin",
""
],
[
"Cheng",
"Xinhua",
""
],
[
"Yuan",
"Li",
... | TITLE: MagicTime: Time-lapse Video Generation Models as Metamorphic Simulators
ABSTRACT: Recent advances in Text-to-Video generation (T2V) have achieved remarkable
success in synthesizing high-quality general videos from textual descriptions.
A largely overlooked problem in T2V is that existing models have not adeq... |
2404.09654 | Junran Wu | Jiaqi Zhu, Shaofeng Cai, Fang Deng, Beng Chin Ooi, Junran Wu | Do LLMs Understand Visual Anomalies? Uncovering LLM's Capabilities in
Zero-shot Anomaly Detection | Accepted by MM'24 (Oral) | null | null | null | cs.CV cs.MM | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Large vision-language models (LVLMs) are markedly proficient in deriving
visual representations guided by natural language. Recent explorations have
utilized LVLMs to tackle zero-shot visual anomaly detection (VAD) challenges by
pairing images with textual descriptions indicative of normal and abnormal
conditions, re... | [
{
"version": "v1",
"created": "Mon, 15 Apr 2024 10:42:22 GMT"
},
{
"version": "v2",
"created": "Tue, 10 Sep 2024 11:58:23 GMT"
},
{
"version": "v3",
"created": "Mon, 7 Apr 2025 05:18:12 GMT"
}
] | 2025-04-08T00:00:00 | [
[
"Zhu",
"Jiaqi",
""
],
[
"Cai",
"Shaofeng",
""
],
[
"Deng",
"Fang",
""
],
[
"Ooi",
"Beng Chin",
""
],
[
"Wu",
"Junran",
""
]
] | TITLE: Do LLMs Understand Visual Anomalies? Uncovering LLM's Capabilities in
Zero-shot Anomaly Detection
ABSTRACT: Large vision-language models (LVLMs) are markedly proficient in deriving
visual representations guided by natural language. Recent explorations have
utilized LVLMs to tackle zero-shot visual anomaly ... |
2404.13659 | Tong Wang | Tong Wang, Guanzhou Chen, Xiaodong Zhang, Chenxi Liu, Xiaoliang Tan,
Jiaqi Wang, Chanjuan He, Wenlin Zhou | LMFNet: An Efficient Multimodal Fusion Approach for Semantic
Segmentation in High-Resolution Remote Sensing | null | null | 10.1016/j.patcog.2025.111579 | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Despite the rapid evolution of semantic segmentation for land cover
classification in high-resolution remote sensing imagery, integrating multiple
data modalities such as Digital Surface Model (DSM), RGB, and Near-infrared
(NIR) remains a challenge. Current methods often process only two types of
data, missing out on... | [
{
"version": "v1",
"created": "Sun, 21 Apr 2024 13:29:42 GMT"
}
] | 2025-04-08T00:00:00 | [
[
"Wang",
"Tong",
""
],
[
"Chen",
"Guanzhou",
""
],
[
"Zhang",
"Xiaodong",
""
],
[
"Liu",
"Chenxi",
""
],
[
"Tan",
"Xiaoliang",
""
],
[
"Wang",
"Jiaqi",
""
],
[
"He",
"Chanjuan",
""
],
[
"Zhou",
"... | TITLE: LMFNet: An Efficient Multimodal Fusion Approach for Semantic
Segmentation in High-Resolution Remote Sensing
ABSTRACT: Despite the rapid evolution of semantic segmentation for land cover
classification in high-resolution remote sensing imagery, integrating multiple
data modalities such as Digital Surface Mo... |
2404.15451 | Hongyi Cai | Hongyi Cai, Mohammad Mahdinur Rahman, Wenzhen Dong and Jingyu Wu | CFPFormer: Feature-pyramid like Transformer Decoder for Segmentation and
Detection | null | null | null | null | cs.CV | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Feature pyramids have been widely adopted in convolutional neural networks
and transformers for tasks in medical image segmentation. However, existing
models generally focus on the Encoder-side Transformer for feature extraction.
We further explore the potential in improving the feature decoder with a
well-designed a... | [
{
"version": "v1",
"created": "Tue, 23 Apr 2024 18:46:07 GMT"
},
{
"version": "v2",
"created": "Sat, 5 Apr 2025 23:18:49 GMT"
}
] | 2025-04-08T00:00:00 | [
[
"Cai",
"Hongyi",
""
],
[
"Rahman",
"Mohammad Mahdinur",
""
],
[
"Dong",
"Wenzhen",
""
],
[
"Wu",
"Jingyu",
""
]
] | TITLE: CFPFormer: Feature-pyramid like Transformer Decoder for Segmentation and
Detection
ABSTRACT: Feature pyramids have been widely adopted in convolutional neural networks
and transformers for tasks in medical image segmentation. However, existing
models generally focus on the Encoder-side Transformer for feat... |
2405.00543 | Kiet Nguyen | Quy Hoang Nguyen, Minh-Van Truong Nguyen, Kiet Van Nguyen | New Benchmark Dataset and Fine-Grained Cross-Modal Fusion Framework for
Vietnamese Multimodal Aspect-Category Sentiment Analysis | null | Multimedia Systems 31, 4 (2025) | 10.1007/s00530-024-01558-8 | null | cs.CL cs.AI | http://creativecommons.org/licenses/by-nc-sa/4.0/ | The emergence of multimodal data on social media platforms presents new
opportunities to better understand user sentiments toward a given aspect.
However, existing multimodal datasets for Aspect-Category Sentiment Analysis
(ACSA) often focus on textual annotations, neglecting fine-grained information
in images. Conse... | [
{
"version": "v1",
"created": "Wed, 1 May 2024 14:29:03 GMT"
}
] | 2025-04-08T00:00:00 | [
[
"Nguyen",
"Quy Hoang",
""
],
[
"Nguyen",
"Minh-Van Truong",
""
],
[
"Van Nguyen",
"Kiet",
""
]
] | TITLE: New Benchmark Dataset and Fine-Grained Cross-Modal Fusion Framework for
Vietnamese Multimodal Aspect-Category Sentiment Analysis
ABSTRACT: The emergence of multimodal data on social media platforms presents new
opportunities to better understand user sentiments toward a given aspect.
However, existing mult... |
2405.04804 | Yin Li | Yin Li, Rajalakshmi Nandakumar | WixUp: A General Data Augmentation Framework for Wireless Perception in
Tracking of Humans | SenSys pre-published version | null | 10.1145/3715014.3722084 | null | cs.NI | http://creativecommons.org/licenses/by/4.0/ | Recent advancements in wireless perception technologies, including mmWave,
WiFi, and acoustics, have expanded their application in human motion tracking
and health monitoring. They are promising alternatives to traditional
camera-based perception systems, thanks to their efficacy under diverse
conditions or occlusion... | [
{
"version": "v1",
"created": "Wed, 8 May 2024 04:26:32 GMT"
},
{
"version": "v2",
"created": "Sat, 5 Apr 2025 20:25:46 GMT"
}
] | 2025-04-08T00:00:00 | [
[
"Li",
"Yin",
""
],
[
"Nandakumar",
"Rajalakshmi",
""
]
] | TITLE: WixUp: A General Data Augmentation Framework for Wireless Perception in
Tracking of Humans
ABSTRACT: Recent advancements in wireless perception technologies, including mmWave,
WiFi, and acoustics, have expanded their application in human motion tracking
and health monitoring. They are promising alternative... |
2405.07765 | Mubashara Akhtar | Mubashara Akhtar and Chenxi Pang and Andreea Marzoca and Yasemin Altun
and Julian Martin Eisenschlos | TANQ: An open domain dataset of table answered questions | 12 pages, accepted at TACL | null | null | null | cs.CL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Language models, potentially augmented with tool usage such as retrieval are
becoming the go-to means of answering questions. Understanding and answering
questions in real-world settings often requires retrieving information from
different sources, processing and aggregating data to extract insights, and
presenting c... | [
{
"version": "v1",
"created": "Mon, 13 May 2024 14:07:20 GMT"
},
{
"version": "v2",
"created": "Wed, 15 Jan 2025 07:29:20 GMT"
},
{
"version": "v3",
"created": "Sat, 5 Apr 2025 10:44:55 GMT"
}
] | 2025-04-08T00:00:00 | [
[
"Akhtar",
"Mubashara",
""
],
[
"Pang",
"Chenxi",
""
],
[
"Marzoca",
"Andreea",
""
],
[
"Altun",
"Yasemin",
""
],
[
"Eisenschlos",
"Julian Martin",
""
]
] | TITLE: TANQ: An open domain dataset of table answered questions
ABSTRACT: Language models, potentially augmented with tool usage such as retrieval are
becoming the go-to means of answering questions. Understanding and answering
questions in real-world settings often requires retrieving information from
different so... |
2405.07920 | Ferdinand Schlatt | Ferdinand Schlatt, Maik Fr\"obe, Harrisen Scells, Shengyao Zhuang,
Bevan Koopman, Guido Zuccon, Benno Stein, Martin Potthast, Matthias Hagen | Rank-DistiLLM: Closing the Effectiveness Gap Between Cross-Encoders and
LLMs for Passage Re-Ranking | Accepted at ECIR'25 | null | 10.1007/978-3-031-88714-7_31 | null | cs.IR | http://creativecommons.org/licenses/by/4.0/ | Cross-encoders distilled from large language models (LLMs) are often more
effective re-rankers than cross-encoders fine-tuned on manually labeled data.
However, distilled models do not match the effectiveness of their teacher LLMs.
We hypothesize that this effectiveness gap is due to the fact that previous
work has n... | [
{
"version": "v1",
"created": "Mon, 13 May 2024 16:51:53 GMT"
},
{
"version": "v2",
"created": "Sun, 16 Jun 2024 12:43:02 GMT"
},
{
"version": "v3",
"created": "Sat, 22 Mar 2025 09:53:21 GMT"
},
{
"version": "v4",
"created": "Sat, 5 Apr 2025 10:01:30 GMT"
}
] | 2025-04-08T00:00:00 | [
[
"Schlatt",
"Ferdinand",
""
],
[
"Fröbe",
"Maik",
""
],
[
"Scells",
"Harrisen",
""
],
[
"Zhuang",
"Shengyao",
""
],
[
"Koopman",
"Bevan",
""
],
[
"Zuccon",
"Guido",
""
],
[
"Stein",
"Benno",
""
],
[
... | TITLE: Rank-DistiLLM: Closing the Effectiveness Gap Between Cross-Encoders and
LLMs for Passage Re-Ranking
ABSTRACT: Cross-encoders distilled from large language models (LLMs) are often more
effective re-rankers than cross-encoders fine-tuned on manually labeled data.
However, distilled models do not match the ef... |
2405.08487 | Mian Zou | Mian Zou, Baosheng Yu, Yibing Zhan, Siwei Lyu, and Kede Ma | Semantic Contextualization of Face Forgery: A New Definition, Dataset,
and Detection Method | null | null | null | null | cs.CV cs.CR | http://creativecommons.org/licenses/by/4.0/ | In recent years, deep learning has greatly streamlined the process of
manipulating photographic face images. Aware of the potential dangers,
researchers have developed various tools to spot these counterfeits. Yet, none
asks the fundamental question: What digital manipulations make a real
photographic face image fake... | [
{
"version": "v1",
"created": "Tue, 14 May 2024 10:24:19 GMT"
},
{
"version": "v2",
"created": "Sat, 29 Mar 2025 07:00:42 GMT"
},
{
"version": "v3",
"created": "Sat, 5 Apr 2025 09:19:17 GMT"
}
] | 2025-04-08T00:00:00 | [
[
"Zou",
"Mian",
""
],
[
"Yu",
"Baosheng",
""
],
[
"Zhan",
"Yibing",
""
],
[
"Lyu",
"Siwei",
""
],
[
"Ma",
"Kede",
""
]
] | TITLE: Semantic Contextualization of Face Forgery: A New Definition, Dataset,
and Detection Method
ABSTRACT: In recent years, deep learning has greatly streamlined the process of
manipulating photographic face images. Aware of the potential dangers,
researchers have developed various tools to spot these counterfe... |
2405.17238 | Ziyang Li | Ziyang Li, Saikat Dutta, Mayur Naik | IRIS: LLM-Assisted Static Analysis for Detecting Security
Vulnerabilities | null | null | null | null | cs.CR cs.PL cs.SE | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Software is prone to security vulnerabilities. Program analysis tools to
detect them have limited effectiveness in practice due to their reliance on
human labeled specifications. Large language models (or LLMs) have shown
impressive code generation capabilities but they cannot do complex reasoning
over code to detect... | [
{
"version": "v1",
"created": "Mon, 27 May 2024 14:53:35 GMT"
},
{
"version": "v2",
"created": "Mon, 11 Nov 2024 21:05:43 GMT"
},
{
"version": "v3",
"created": "Sun, 6 Apr 2025 23:46:59 GMT"
}
] | 2025-04-08T00:00:00 | [
[
"Li",
"Ziyang",
""
],
[
"Dutta",
"Saikat",
""
],
[
"Naik",
"Mayur",
""
]
] | TITLE: IRIS: LLM-Assisted Static Analysis for Detecting Security
Vulnerabilities
ABSTRACT: Software is prone to security vulnerabilities. Program analysis tools to
detect them have limited effectiveness in practice due to their reliance on
human labeled specifications. Large language models (or LLMs) have shown
i... |
2405.18902 | Andrea Pugnana | Filippo Palomba and Andrea Pugnana and Jos\'e Manuel Alvarez and
Salvatore Ruggieri | A Causal Framework for Evaluating Deferring Systems | Accepted at AISTATS 2025 | null | null | null | cs.LG cs.AI stat.ML | http://creativecommons.org/licenses/by/4.0/ | Deferring systems extend supervised Machine Learning (ML) models with the
possibility to defer predictions to human experts. However, evaluating the
impact of a deferring strategy on system accuracy is still an overlooked area.
This paper fills this gap by evaluating deferring systems through a causal
lens. We link t... | [
{
"version": "v1",
"created": "Wed, 29 May 2024 09:03:44 GMT"
},
{
"version": "v2",
"created": "Mon, 7 Apr 2025 08:54:30 GMT"
}
] | 2025-04-08T00:00:00 | [
[
"Palomba",
"Filippo",
""
],
[
"Pugnana",
"Andrea",
""
],
[
"Alvarez",
"José Manuel",
""
],
[
"Ruggieri",
"Salvatore",
""
]
] | TITLE: A Causal Framework for Evaluating Deferring Systems
ABSTRACT: Deferring systems extend supervised Machine Learning (ML) models with the
possibility to defer predictions to human experts. However, evaluating the
impact of a deferring strategy on system accuracy is still an overlooked area.
This paper fills th... |
2406.02541 | Inkyu Shin | Inkyu Shin, Qihang Yu, Xiaohui Shen, In So Kweon, Kuk-Jin Yoon,
Liang-Chieh Chen | Enhancing Temporal Consistency in Video Editing by Reconstructing Videos
with 3D Gaussian Splatting | Accepted to TMLR 2025. Project page at
https://video-3dgs-project.github.io/ | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recent advancements in zero-shot video diffusion models have shown promise
for text-driven video editing, but challenges remain in achieving high temporal
consistency. To address this, we introduce Video-3DGS, a 3D Gaussian Splatting
(3DGS)-based video refiner designed to enhance temporal consistency in
zero-shot vid... | [
{
"version": "v1",
"created": "Tue, 4 Jun 2024 17:57:37 GMT"
},
{
"version": "v2",
"created": "Wed, 5 Jun 2024 05:00:39 GMT"
},
{
"version": "v3",
"created": "Thu, 6 Jun 2024 01:40:56 GMT"
},
{
"version": "v4",
"created": "Fri, 4 Apr 2025 18:48:54 GMT"
}
] | 2025-04-08T00:00:00 | [
[
"Shin",
"Inkyu",
""
],
[
"Yu",
"Qihang",
""
],
[
"Shen",
"Xiaohui",
""
],
[
"Kweon",
"In So",
""
],
[
"Yoon",
"Kuk-Jin",
""
],
[
"Chen",
"Liang-Chieh",
""
]
] | TITLE: Enhancing Temporal Consistency in Video Editing by Reconstructing Videos
with 3D Gaussian Splatting
ABSTRACT: Recent advancements in zero-shot video diffusion models have shown promise
for text-driven video editing, but challenges remain in achieving high temporal
consistency. To address this, we introduce... |
2406.04928 | Torben Peters | Ghjulia Sialelli, Torben Peters, Jan D. Wegner, Konrad Schindler | AGBD: A Global-scale Biomass Dataset | null | null | null | null | cs.CV cs.LG eess.IV | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Accurate estimates of Above Ground Biomass (AGB) are essential in addressing
two of humanity's biggest challenges: climate change and biodiversity loss.
Existing datasets for AGB estimation from satellite imagery are limited. Either
they focus on specific, local regions at high resolution, or they offer global
covera... | [
{
"version": "v1",
"created": "Fri, 7 Jun 2024 13:34:17 GMT"
},
{
"version": "v2",
"created": "Mon, 9 Dec 2024 11:08:35 GMT"
},
{
"version": "v3",
"created": "Mon, 7 Apr 2025 11:19:12 GMT"
}
] | 2025-04-08T00:00:00 | [
[
"Sialelli",
"Ghjulia",
""
],
[
"Peters",
"Torben",
""
],
[
"Wegner",
"Jan D.",
""
],
[
"Schindler",
"Konrad",
""
]
] | TITLE: AGBD: A Global-scale Biomass Dataset
ABSTRACT: Accurate estimates of Above Ground Biomass (AGB) are essential in addressing
two of humanity's biggest challenges: climate change and biodiversity loss.
Existing datasets for AGB estimation from satellite imagery are limited. Either
they focus on specific, local... |
2406.09067 | Tarun Khajuria | Tarun Khajuria, Braian Olmiro Dias, Marharyta Domnich, Jaan Aru | Interpreting the structure of multi-object representations in vision
encoders | null | null | null | null | cs.CV cs.CL q-bio.NC | http://creativecommons.org/licenses/by/4.0/ | In this work, we interpret the representations of multi-object scenes in
vision encoders through the lens of structured representations. Structured
representations allow modeling of individual objects distinctly and their
flexible use based on the task context for both scene-level and object-specific
tasks. These cap... | [
{
"version": "v1",
"created": "Thu, 13 Jun 2024 12:54:20 GMT"
},
{
"version": "v2",
"created": "Tue, 18 Jun 2024 12:27:36 GMT"
},
{
"version": "v3",
"created": "Sun, 6 Apr 2025 13:44:02 GMT"
}
] | 2025-04-08T00:00:00 | [
[
"Khajuria",
"Tarun",
""
],
[
"Dias",
"Braian Olmiro",
""
],
[
"Domnich",
"Marharyta",
""
],
[
"Aru",
"Jaan",
""
]
] | TITLE: Interpreting the structure of multi-object representations in vision
encoders
ABSTRACT: In this work, we interpret the representations of multi-object scenes in
vision encoders through the lens of structured representations. Structured
representations allow modeling of individual objects distinctly and the... |
2406.09564 | Ziyan Wang | Ziyan Wang, Xiaoming Huo, Hao Wang | Towards Domain Adaptive Neural Contextual Bandits | Accepted at ICLR 2025 | null | null | null | cs.LG cs.AI cs.CE cs.CV stat.ML | http://creativecommons.org/licenses/by/4.0/ | Contextual bandit algorithms are essential for solving real-world decision
making problems. In practice, collecting a contextual bandit's feedback from
different domains may involve different costs. For example, measuring drug
reaction from mice (as a source domain) and humans (as a target domain).
Unfortunately, ada... | [
{
"version": "v1",
"created": "Thu, 13 Jun 2024 20:12:46 GMT"
},
{
"version": "v2",
"created": "Tue, 22 Oct 2024 02:14:24 GMT"
},
{
"version": "v3",
"created": "Sun, 6 Apr 2025 18:23:33 GMT"
}
] | 2025-04-08T00:00:00 | [
[
"Wang",
"Ziyan",
""
],
[
"Huo",
"Xiaoming",
""
],
[
"Wang",
"Hao",
""
]
] | TITLE: Towards Domain Adaptive Neural Contextual Bandits
ABSTRACT: Contextual bandit algorithms are essential for solving real-world decision
making problems. In practice, collecting a contextual bandit's feedback from
different domains may involve different costs. For example, measuring drug
reaction from mice (as... |
2406.19774 | Yixing Li | Yixing Li, Yuxian Gu, Li Dong, Dequan Wang, Yu Cheng, Furu Wei | Direct Preference Knowledge Distillation for Large Language Models | null | null | null | null | cs.CL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In the field of large language models (LLMs), Knowledge Distillation (KD) is
a critical technique for transferring capabilities from teacher models to
student models. However, existing KD methods face limitations and challenges in
distillation of LLMs, including efficiency and insufficient measurement
capabilities of... | [
{
"version": "v1",
"created": "Fri, 28 Jun 2024 09:23:40 GMT"
},
{
"version": "v2",
"created": "Mon, 7 Apr 2025 06:11:54 GMT"
}
] | 2025-04-08T00:00:00 | [
[
"Li",
"Yixing",
""
],
[
"Gu",
"Yuxian",
""
],
[
"Dong",
"Li",
""
],
[
"Wang",
"Dequan",
""
],
[
"Cheng",
"Yu",
""
],
[
"Wei",
"Furu",
""
]
] | TITLE: Direct Preference Knowledge Distillation for Large Language Models
ABSTRACT: In the field of large language models (LLMs), Knowledge Distillation (KD) is
a critical technique for transferring capabilities from teacher models to
student models. However, existing KD methods face limitations and challenges in
d... |
2407.00342 | Kibeom Nam | Kibeom Nam | KPC-cF: Aspect-Based Sentiment Analysis via Implicit-Feature Alignment
with Corpus Filtering | Work in Progress, DMLR@ICML 2024 | null | null | null | cs.CL cs.AI | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Investigations into Aspect-Based Sentiment Analysis (ABSA) for Korean
industrial reviews are notably lacking in the existing literature. Our research
proposes an intuitive and effective framework for ABSA in low-resource
languages such as Korean. It optimizes prediction labels by integrating
translated benchmark and ... | [
{
"version": "v1",
"created": "Sat, 29 Jun 2024 07:01:51 GMT"
},
{
"version": "v2",
"created": "Thu, 11 Jul 2024 17:08:36 GMT"
},
{
"version": "v3",
"created": "Sat, 20 Jul 2024 09:32:01 GMT"
},
{
"version": "v4",
"created": "Fri, 15 Nov 2024 17:59:10 GMT"
},
{
"v... | 2025-04-08T00:00:00 | [
[
"Nam",
"Kibeom",
""
]
] | TITLE: KPC-cF: Aspect-Based Sentiment Analysis via Implicit-Feature Alignment
with Corpus Filtering
ABSTRACT: Investigations into Aspect-Based Sentiment Analysis (ABSA) for Korean
industrial reviews are notably lacking in the existing literature. Our research
proposes an intuitive and effective framework for ABSA... |
2407.00923 | Oleg Vasilyev | Oleg Vasilyev, Randy Sawaya, John Bohannon | Preserving Multilingual Quality While Tuning Query Encoder on English
Only | Accepted to NAACL 2025 | null | null | null | cs.CL | http://creativecommons.org/licenses/by/4.0/ | A query encoder of a dual passage retrieval system can be tuned for specific
types of queries or domains, while the precomputed and stored documents
representations are kept intact. Switching from one query encoder to another
when needed is easily feasible, unlike overhauling the embeddings of a whole
knowledge base.... | [
{
"version": "v1",
"created": "Mon, 1 Jul 2024 03:03:18 GMT"
},
{
"version": "v2",
"created": "Fri, 9 Aug 2024 06:02:12 GMT"
},
{
"version": "v3",
"created": "Sat, 14 Dec 2024 01:23:33 GMT"
},
{
"version": "v4",
"created": "Sat, 5 Apr 2025 23:03:41 GMT"
}
] | 2025-04-08T00:00:00 | [
[
"Vasilyev",
"Oleg",
""
],
[
"Sawaya",
"Randy",
""
],
[
"Bohannon",
"John",
""
]
] | TITLE: Preserving Multilingual Quality While Tuning Query Encoder on English
Only
ABSTRACT: A query encoder of a dual passage retrieval system can be tuned for specific
types of queries or domains, while the precomputed and stored documents
representations are kept intact. Switching from one query encoder to anot... |
2407.05952 | Nikhil Abhyankar | Nikhil Abhyankar, Vivek Gupta, Dan Roth, Chandan K. Reddy | H-STAR: LLM-driven Hybrid SQL-Text Adaptive Reasoning on Tables | NAACL 2025 Main Conference | null | null | null | cs.DB cs.AI cs.CL cs.LG | http://creativecommons.org/licenses/by-sa/4.0/ | Tabular reasoning involves interpreting natural language queries about
tabular data, which presents a unique challenge of combining language
understanding with structured data analysis. Existing methods employ either
textual reasoning, which excels in semantic interpretation but struggles with
mathematical operations... | [
{
"version": "v1",
"created": "Sat, 29 Jun 2024 21:24:19 GMT"
},
{
"version": "v2",
"created": "Wed, 30 Oct 2024 23:44:31 GMT"
},
{
"version": "v3",
"created": "Mon, 7 Apr 2025 00:44:34 GMT"
}
] | 2025-04-08T00:00:00 | [
[
"Abhyankar",
"Nikhil",
""
],
[
"Gupta",
"Vivek",
""
],
[
"Roth",
"Dan",
""
],
[
"Reddy",
"Chandan K.",
""
]
] | TITLE: H-STAR: LLM-driven Hybrid SQL-Text Adaptive Reasoning on Tables
ABSTRACT: Tabular reasoning involves interpreting natural language queries about
tabular data, which presents a unique challenge of combining language
understanding with structured data analysis. Existing methods employ either
textual reasoning,... |
2407.13349 | HongHao Li | Honghao Li, Yiwen Zhang, Yi Zhang, Hanwei Li, Lei Sang, and Jieming
Zhu | FCN: Fusing Exponential and Linear Cross Network for Click-Through Rate
Prediction | null | null | null | null | cs.IR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | As an important modeling paradigm in click-through rate (CTR) prediction, the
Deep & Cross Network (DCN) and its derivative models have gained widespread
recognition primarily due to their success in a trade-off between computational
cost and performance. This paradigm employs a cross network to explicitly model
feat... | [
{
"version": "v1",
"created": "Thu, 18 Jul 2024 09:49:13 GMT"
},
{
"version": "v2",
"created": "Fri, 19 Jul 2024 03:23:01 GMT"
},
{
"version": "v3",
"created": "Mon, 29 Jul 2024 16:30:42 GMT"
},
{
"version": "v4",
"created": "Wed, 31 Jul 2024 15:59:46 GMT"
},
{
"v... | 2025-04-08T00:00:00 | [
[
"Li",
"Honghao",
""
],
[
"Zhang",
"Yiwen",
""
],
[
"Zhang",
"Yi",
""
],
[
"Li",
"Hanwei",
""
],
[
"Sang",
"Lei",
""
],
[
"Zhu",
"Jieming",
""
]
] | TITLE: FCN: Fusing Exponential and Linear Cross Network for Click-Through Rate
Prediction
ABSTRACT: As an important modeling paradigm in click-through rate (CTR) prediction, the
Deep & Cross Network (DCN) and its derivative models have gained widespread
recognition primarily due to their success in a trade-off be... |
2407.19992 | Hao Shu | Hao Shu | Enhancing Edge Detection by Texture Handling Architecture and Noiseless
Training Data | 28 pages | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Image edge detection (ED) is a fundamental task in computer vision. While
convolution-based models have significantly advanced ED performance, achieving
high precision under strict error tolerance constraints remains challenging.
Furthermore, the reliance on noisy, human-annotated training data limits model
performan... | [
{
"version": "v1",
"created": "Mon, 29 Jul 2024 13:24:55 GMT"
},
{
"version": "v2",
"created": "Tue, 1 Oct 2024 12:22:31 GMT"
},
{
"version": "v3",
"created": "Wed, 2 Oct 2024 10:24:45 GMT"
},
{
"version": "v4",
"created": "Sat, 5 Apr 2025 02:17:03 GMT"
}
] | 2025-04-08T00:00:00 | [
[
"Shu",
"Hao",
""
]
] | TITLE: Enhancing Edge Detection by Texture Handling Architecture and Noiseless
Training Data
ABSTRACT: Image edge detection (ED) is a fundamental task in computer vision. While
convolution-based models have significantly advanced ED performance, achieving
high precision under strict error tolerance constraints re... |
2408.07107 | Wenxuan Yang | Wenxuan Yang, Hanyu Zhang, Weimin Tan, Yuqi Sun, Bo Yan | A Self-Supervised Paradigm for Data-Efficient Medical Foundation Model
Pre-training: V-information Optimization Framework | null | null | null | null | cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Self-supervised pre-training medical foundation models on large-scale
datasets demonstrate exceptional performance. Recent research challenges this
common paradigm by introducing data-effective learning approaches,
demonstrating that merely increasing pre-training data volume does not
necessarily improve model perfor... | [
{
"version": "v1",
"created": "Tue, 13 Aug 2024 10:28:54 GMT"
},
{
"version": "v2",
"created": "Fri, 16 Aug 2024 12:19:44 GMT"
},
{
"version": "v3",
"created": "Sat, 23 Nov 2024 08:24:19 GMT"
},
{
"version": "v4",
"created": "Sun, 6 Apr 2025 02:50:25 GMT"
}
] | 2025-04-08T00:00:00 | [
[
"Yang",
"Wenxuan",
""
],
[
"Zhang",
"Hanyu",
""
],
[
"Tan",
"Weimin",
""
],
[
"Sun",
"Yuqi",
""
],
[
"Yan",
"Bo",
""
]
] | TITLE: A Self-Supervised Paradigm for Data-Efficient Medical Foundation Model
Pre-training: V-information Optimization Framework
ABSTRACT: Self-supervised pre-training medical foundation models on large-scale
datasets demonstrate exceptional performance. Recent research challenges this
common paradigm by introduc... |
2408.07108 | Marina Zajnulina | Marina Zajnulina | Shannon Entropy Helps Optimize the Performance of a
Frequency-Multiplexed Extreme Learning Machine | null | null | null | null | physics.optics | http://creativecommons.org/licenses/by/4.0/ | Knowing the dynamics of neuromorphic photonic schemes would allow their
optimization for controlled data-processing capability at possibly minimized
energy consumption levels. In nonlinear substrates such as optical fibers or
semiconductors, these dynamics can widely vary depending on each optical input
encoded with ... | [
{
"version": "v1",
"created": "Tue, 13 Aug 2024 11:03:54 GMT"
},
{
"version": "v2",
"created": "Sun, 6 Apr 2025 22:21:54 GMT"
}
] | 2025-04-08T00:00:00 | [
[
"Zajnulina",
"Marina",
""
]
] | TITLE: Shannon Entropy Helps Optimize the Performance of a
Frequency-Multiplexed Extreme Learning Machine
ABSTRACT: Knowing the dynamics of neuromorphic photonic schemes would allow their
optimization for controlled data-processing capability at possibly minimized
energy consumption levels. In nonlinear substrate... |
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