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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2503.03462 | Ahmed Njifenjou | Ahmed Njifenjou, Virgile Sucal, Bassam Jabaian, Fabrice Lef\`evre | Open-Source Large Language Models as Multilingual Crowdworkers:
Synthesizing Open-Domain Dialogues in Several Languages With No Examples in
Targets and No Machine Translation | null | null | null | null | cs.CL cs.AI cs.HC cs.LG | http://creativecommons.org/licenses/by/4.0/ | The prevailing paradigm in the domain of Open-Domain Dialogue agents
predominantly focuses on the English language, encompassing both models and
datasets. Furthermore, the financial and temporal investments required for
crowdsourcing such datasets for finetuning are substantial, particularly when
multiple languages a... | [
{
"version": "v1",
"created": "Wed, 5 Mar 2025 12:52:14 GMT"
}
] | 2025-03-06T00:00:00 | [
[
"Njifenjou",
"Ahmed",
""
],
[
"Sucal",
"Virgile",
""
],
[
"Jabaian",
"Bassam",
""
],
[
"Lefèvre",
"Fabrice",
""
]
] | TITLE: Open-Source Large Language Models as Multilingual Crowdworkers:
Synthesizing Open-Domain Dialogues in Several Languages With No Examples in
Targets and No Machine Translation
ABSTRACT: The prevailing paradigm in the domain of Open-Domain Dialogue agents
predominantly focuses on the English language, enco... |
2503.03476 | Xiaoyi Wei | Jiaxin Tu, Xiaoyi Wei, Yueqi Zhang, Taixian Hou, Xiaofei Gao, Zhiyan
Dong, Peng Zhai, and Lihua Zhang | Continuous Control of Diverse Skills in Quadruped Robots Without
Complete Expert Datasets | Accepted by ICRA 2025 | null | null | null | cs.RO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Learning diverse skills for quadruped robots presents significant challenges,
such as mastering complex transitions between different skills and handling
tasks of varying difficulty. Existing imitation learning methods, while
successful, rely on expensive datasets to reproduce expert behaviors. Inspired
by introspect... | [
{
"version": "v1",
"created": "Wed, 5 Mar 2025 13:12:49 GMT"
}
] | 2025-03-06T00:00:00 | [
[
"Tu",
"Jiaxin",
""
],
[
"Wei",
"Xiaoyi",
""
],
[
"Zhang",
"Yueqi",
""
],
[
"Hou",
"Taixian",
""
],
[
"Gao",
"Xiaofei",
""
],
[
"Dong",
"Zhiyan",
""
],
[
"Zhai",
"Peng",
""
],
[
"Zhang",
"Lihua",... | TITLE: Continuous Control of Diverse Skills in Quadruped Robots Without
Complete Expert Datasets
ABSTRACT: Learning diverse skills for quadruped robots presents significant challenges,
such as mastering complex transitions between different skills and handling
tasks of varying difficulty. Existing imitation learn... |
2503.03485 | Soumya Ghosh | Alexis Chevalier, Soumya Ghosh, Urvi Awasthi, James Watkins, Julia
Bieniewska, Nichita Mitrea, Olga Kotova, Kirill Shkura, Andrew Noble, Michael
Steinbaugh, Julien Delile, Christoph Meier, Leonid Zhukov, Iya Khalil,
Srayanta Mukherjee, Judith Mueller | TEDDY: A Family Of Foundation Models For Understanding Single Cell
Biology | null | null | null | null | cs.LG q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Understanding the biological mechanism of disease is critical for medicine,
and in particular drug discovery. AI-powered analysis of genome-scale
biological data hold great potential in this regard. The increasing
availability of single-cell RNA sequencing data has enabled the development of
large foundation models f... | [
{
"version": "v1",
"created": "Wed, 5 Mar 2025 13:24:57 GMT"
}
] | 2025-03-06T00:00:00 | [
[
"Chevalier",
"Alexis",
""
],
[
"Ghosh",
"Soumya",
""
],
[
"Awasthi",
"Urvi",
""
],
[
"Watkins",
"James",
""
],
[
"Bieniewska",
"Julia",
""
],
[
"Mitrea",
"Nichita",
""
],
[
"Kotova",
"Olga",
""
],
[
... | TITLE: TEDDY: A Family Of Foundation Models For Understanding Single Cell
Biology
ABSTRACT: Understanding the biological mechanism of disease is critical for medicine,
and in particular drug discovery. AI-powered analysis of genome-scale
biological data hold great potential in this regard. The increasing
availabi... |
2503.03486 | Maresa Schr\"oder | Maresa Schr\"oder, Valentyn Melnychuk, Stefan Feuerriegel | Differentially Private Learners for Heterogeneous Treatment Effects | Published at ICLR 2025 | null | null | null | cs.LG cs.CR | http://creativecommons.org/licenses/by/4.0/ | Patient data is widely used to estimate heterogeneous treatment effects and
thus understand the effectiveness and safety of drugs. Yet, patient data
includes highly sensitive information that must be kept private. In this work,
we aim to estimate the conditional average treatment effect (CATE) from
observational data... | [
{
"version": "v1",
"created": "Wed, 5 Mar 2025 13:24:58 GMT"
}
] | 2025-03-06T00:00:00 | [
[
"Schröder",
"Maresa",
""
],
[
"Melnychuk",
"Valentyn",
""
],
[
"Feuerriegel",
"Stefan",
""
]
] | TITLE: Differentially Private Learners for Heterogeneous Treatment Effects
ABSTRACT: Patient data is widely used to estimate heterogeneous treatment effects and
thus understand the effectiveness and safety of drugs. Yet, patient data
includes highly sensitive information that must be kept private. In this work,
we ... |
2503.03499 | Wonjun Kang | Wonjun Kang, Kevin Galim, Yuchen Zeng, Minjae Lee, Hyung Il Koo, Nam
Ik Cho | State-offset Tuning: State-based Parameter-Efficient Fine-Tuning for
State Space Models | Code is available at https://github.com/furiosa-ai/ssm-state-tuning | null | null | null | cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | State Space Models (SSMs) have emerged as efficient alternatives to
Transformers, mitigating their quadratic computational cost. However, the
application of Parameter-Efficient Fine-Tuning (PEFT) methods to SSMs remains
largely unexplored. In particular, prompt-based methods like Prompt Tuning and
Prefix-Tuning, whic... | [
{
"version": "v1",
"created": "Wed, 5 Mar 2025 13:44:42 GMT"
}
] | 2025-03-06T00:00:00 | [
[
"Kang",
"Wonjun",
""
],
[
"Galim",
"Kevin",
""
],
[
"Zeng",
"Yuchen",
""
],
[
"Lee",
"Minjae",
""
],
[
"Koo",
"Hyung Il",
""
],
[
"Cho",
"Nam Ik",
""
]
] | TITLE: State-offset Tuning: State-based Parameter-Efficient Fine-Tuning for
State Space Models
ABSTRACT: State Space Models (SSMs) have emerged as efficient alternatives to
Transformers, mitigating their quadratic computational cost. However, the
application of Parameter-Efficient Fine-Tuning (PEFT) methods to SS... |
2503.03500 | Karuna K Chandra | Arvindh Arun, Karuna K Chandra, Akshit Sinha, Balakumar Velayutham,
Jashn Arora, Manish Jain, Ponnurangam Kumaraguru | Topo Goes Political: TDA-Based Controversy Detection in Imbalanced
Reddit Political Data | null | null | 10.1145/3701716.3717535 | null | cs.SI | http://creativecommons.org/licenses/by/4.0/ | The detection of controversial content in political discussions on the
Internet is a critical challenge in maintaining healthy digital discourse.
Unlike much of the existing literature that relies on synthetically balanced
data, our work preserves the natural distribution of controversial and
non-controversial posts.... | [
{
"version": "v1",
"created": "Wed, 5 Mar 2025 13:46:39 GMT"
}
] | 2025-03-06T00:00:00 | [
[
"Arun",
"Arvindh",
""
],
[
"Chandra",
"Karuna K",
""
],
[
"Sinha",
"Akshit",
""
],
[
"Velayutham",
"Balakumar",
""
],
[
"Arora",
"Jashn",
""
],
[
"Jain",
"Manish",
""
],
[
"Kumaraguru",
"Ponnurangam",
""
... | TITLE: Topo Goes Political: TDA-Based Controversy Detection in Imbalanced
Reddit Political Data
ABSTRACT: The detection of controversial content in political discussions on the
Internet is a critical challenge in maintaining healthy digital discourse.
Unlike much of the existing literature that relies on syntheti... |
2503.03501 | Gavriel Habib | Gavriel Habib, Noa Barzilay, Or Shimshi, Rami Ben-Ari, Nir Darshan | CarGait: Cross-Attention based Re-ranking for Gait recognition | null | null | null | null | cs.CV | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Gait recognition is a computer vision task that identifies individuals based
on their walking patterns. Gait recognition performance is commonly evaluated
by ranking a gallery of candidates and measuring the accuracy at the top
Rank-$K$. Existing models are typically single-staged, i.e. searching for the
probe's near... | [
{
"version": "v1",
"created": "Wed, 5 Mar 2025 13:47:02 GMT"
}
] | 2025-03-06T00:00:00 | [
[
"Habib",
"Gavriel",
""
],
[
"Barzilay",
"Noa",
""
],
[
"Shimshi",
"Or",
""
],
[
"Ben-Ari",
"Rami",
""
],
[
"Darshan",
"Nir",
""
]
] | TITLE: CarGait: Cross-Attention based Re-ranking for Gait recognition
ABSTRACT: Gait recognition is a computer vision task that identifies individuals based
on their walking patterns. Gait recognition performance is commonly evaluated
by ranking a gallery of candidates and measuring the accuracy at the top
Rank-$K$... |
2503.03512 | Ali Erkan | Ali Erkan and Tunga G\"ung\"or | An Aspect Extraction Framework using Different Embedding Types, Learning
Models, and Dependency Structure | Aspect-based Sentiment Analysis, Aspect Extraction, Natural Language
Processing, Machine Learning, Deep Neural Networks, Turkish | null | null | null | cs.CL cs.LG | http://creativecommons.org/licenses/by/4.0/ | Aspect-based sentiment analysis has gained significant attention in recent
years due to its ability to provide fine-grained insights for sentiment
expressions related to specific features of entities. An important component of
aspect-based sentiment analysis is aspect extraction, which involves
identifying and extrac... | [
{
"version": "v1",
"created": "Wed, 5 Mar 2025 13:57:48 GMT"
}
] | 2025-03-06T00:00:00 | [
[
"Erkan",
"Ali",
""
],
[
"Güngör",
"Tunga",
""
]
] | TITLE: An Aspect Extraction Framework using Different Embedding Types, Learning
Models, and Dependency Structure
ABSTRACT: Aspect-based sentiment analysis has gained significant attention in recent
years due to its ability to provide fine-grained insights for sentiment
expressions related to specific features of ... |
2503.03523 | Jun Yan | Maryam Al Shami, Jun Yan, Emmanuel Thepie Fapi | O-RAN xApps Conflict Management using Graph Convolutional Networks | 9 pages, 10 figures | null | null | null | cs.NI cs.LG | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Open Radio Access Network (O-RAN) adopts a flexible, open, and virtualized
structure with standardized interfaces, reducing dependency on a single
supplier. Conflict management in O-RAN refers to the process of identifying and
resolving conflicts between network applications. xApps are applications
deployed at the RA... | [
{
"version": "v1",
"created": "Wed, 5 Mar 2025 14:07:29 GMT"
}
] | 2025-03-06T00:00:00 | [
[
"Shami",
"Maryam Al",
""
],
[
"Yan",
"Jun",
""
],
[
"Fapi",
"Emmanuel Thepie",
""
]
] | TITLE: O-RAN xApps Conflict Management using Graph Convolutional Networks
ABSTRACT: Open Radio Access Network (O-RAN) adopts a flexible, open, and virtualized
structure with standardized interfaces, reducing dependency on a single
supplier. Conflict management in O-RAN refers to the process of identifying and
resol... |
2503.03529 | David Johnson | David S. Johnson | Higher Stakes, Healthier Trust? An Application-Grounded Approach to
Assessing Healthy Trust in High-Stakes Human-AI Collaboration | 11 pages, 5 figures; submitted to IJCAI 2025 | null | null | null | cs.HC | http://creativecommons.org/licenses/by/4.0/ | Human-AI collaboration is increasingly promoted to improve high-stakes
decision-making, yet its benefits have not been fully realized.
Application-grounded evaluations are needed to better evaluate methods for
improving collaboration but often require domain experts, making studies costly
and limiting their generaliz... | [
{
"version": "v1",
"created": "Wed, 5 Mar 2025 14:11:19 GMT"
}
] | 2025-03-06T00:00:00 | [
[
"Johnson",
"David S.",
""
]
] | TITLE: Higher Stakes, Healthier Trust? An Application-Grounded Approach to
Assessing Healthy Trust in High-Stakes Human-AI Collaboration
ABSTRACT: Human-AI collaboration is increasingly promoted to improve high-stakes
decision-making, yet its benefits have not been fully realized.
Application-grounded evaluations... |
2503.03535 | Po-Chien Luan | Po-Chien Luan, Yang Gao, Celine Demonsant, Alexandre Alahi | Unified Human Localization and Trajectory Prediction with Monocular
Vision | ICRA 2025 | null | null | null | cs.CV cs.RO | http://creativecommons.org/licenses/by/4.0/ | Conventional human trajectory prediction models rely on clean curated data,
requiring specialized equipment or manual labeling, which is often impractical
for robotic applications. The existing predictors tend to overfit to clean
observation affecting their robustness when used with noisy inputs. In this
work, we pro... | [
{
"version": "v1",
"created": "Wed, 5 Mar 2025 14:18:39 GMT"
}
] | 2025-03-06T00:00:00 | [
[
"Luan",
"Po-Chien",
""
],
[
"Gao",
"Yang",
""
],
[
"Demonsant",
"Celine",
""
],
[
"Alahi",
"Alexandre",
""
]
] | TITLE: Unified Human Localization and Trajectory Prediction with Monocular
Vision
ABSTRACT: Conventional human trajectory prediction models rely on clean curated data,
requiring specialized equipment or manual labeling, which is often impractical
for robotic applications. The existing predictors tend to overfit t... |
2503.03543 | Dragos Costea | Dragos Costea, Alina Marcu, Marius Leordeanu | A self-supervised cyclic neural-analytic approach for novel view
synthesis and 3D reconstruction | Published in BMVC 2024, 10 pages, 4 figures | British Machine Vision Conference (BMVC), 2024 | null | null | cs.CV | http://creativecommons.org/licenses/by/4.0/ | Generating novel views from recorded videos is crucial for enabling
autonomous UAV navigation. Recent advancements in neural rendering have
facilitated the rapid development of methods capable of rendering new
trajectories. However, these methods often fail to generalize well to regions
far from the training data wit... | [
{
"version": "v1",
"created": "Wed, 5 Mar 2025 14:28:01 GMT"
}
] | 2025-03-06T00:00:00 | [
[
"Costea",
"Dragos",
""
],
[
"Marcu",
"Alina",
""
],
[
"Leordeanu",
"Marius",
""
]
] | TITLE: A self-supervised cyclic neural-analytic approach for novel view
synthesis and 3D reconstruction
ABSTRACT: Generating novel views from recorded videos is crucial for enabling
autonomous UAV navigation. Recent advancements in neural rendering have
facilitated the rapid development of methods capable of rend... |
2503.03548 | Milin Patel | Milin Patel, Rolf Jung | Simulation-Based Performance Evaluation of 3D Object Detection Methods
with Deep Learning for a LiDAR Point Cloud Dataset in a SOTIF-related Use
Case | null | Proceedings of the 10th International Conference on Vehicle
Technology and Intelligent Transport Systems VEHITS - Volume 1, 415-426, 2024
, Angers, France | 10.5220/0012707300003702 | null | cs.CV cs.LG cs.SY eess.SY | http://creativecommons.org/licenses/by/4.0/ | Safety of the Intended Functionality (SOTIF) addresses sensor performance
limitations and deep learning-based object detection insufficiencies to ensure
the intended functionality of Automated Driving Systems (ADS). This paper
presents a methodology examining the adaptability and performance evaluation of
the 3D obje... | [
{
"version": "v1",
"created": "Wed, 5 Mar 2025 14:32:32 GMT"
}
] | 2025-03-06T00:00:00 | [
[
"Patel",
"Milin",
""
],
[
"Jung",
"Rolf",
""
]
] | TITLE: Simulation-Based Performance Evaluation of 3D Object Detection Methods
with Deep Learning for a LiDAR Point Cloud Dataset in a SOTIF-related Use
Case
ABSTRACT: Safety of the Intended Functionality (SOTIF) addresses sensor performance
limitations and deep learning-based object detection insufficiencies to... |
2503.03556 | Xiaomeng Zhu | Xiaomeng Zhu, Yuyang Li, Leiyao Cui, Pengfei Li, Huan-ang Gao, Yixin
Zhu, Hao Zhao | Afford-X: Generalizable and Slim Affordance Reasoning for Task-oriented
Manipulation | null | null | null | null | cs.CV cs.RO | http://creativecommons.org/licenses/by/4.0/ | Object affordance reasoning, the ability to infer object functionalities
based on physical properties, is fundamental for task-oriented planning and
activities in both humans and Artificial Intelligence (AI). This capability,
required for planning and executing daily activities in a task-oriented manner,
relies on co... | [
{
"version": "v1",
"created": "Wed, 5 Mar 2025 14:44:53 GMT"
}
] | 2025-03-06T00:00:00 | [
[
"Zhu",
"Xiaomeng",
""
],
[
"Li",
"Yuyang",
""
],
[
"Cui",
"Leiyao",
""
],
[
"Li",
"Pengfei",
""
],
[
"Gao",
"Huan-ang",
""
],
[
"Zhu",
"Yixin",
""
],
[
"Zhao",
"Hao",
""
]
] | TITLE: Afford-X: Generalizable and Slim Affordance Reasoning for Task-oriented
Manipulation
ABSTRACT: Object affordance reasoning, the ability to infer object functionalities
based on physical properties, is fundamental for task-oriented planning and
activities in both humans and Artificial Intelligence (AI). Thi... |
2503.03573 | Jonas Dube | Jonas Dube, Julius K\"uhn, Chen Wang, Sonal Mistry, Guido Klemz, Alice
Galdi, Thorsten Kamps | Triple Evaporation of Bialkali Antimonide Photocathodes and
Photoemission Characterization at the PhoTEx Experiment | The following article has been submitted to Journal of Applied
Physics | null | null | null | physics.acc-ph | http://creativecommons.org/licenses/by-nc-nd/4.0/ | The development of high-performance photocathodes is essential for generating
high-brightness electron beams required by existing and future accelerators.
This work introduces a state-of-the-art triple evaporation growth system
designed for bialkali antimonide photocathodes. By enabling the simultaneous
deposition of... | [
{
"version": "v1",
"created": "Wed, 5 Mar 2025 15:01:42 GMT"
}
] | 2025-03-06T00:00:00 | [
[
"Dube",
"Jonas",
""
],
[
"Kühn",
"Julius",
""
],
[
"Wang",
"Chen",
""
],
[
"Mistry",
"Sonal",
""
],
[
"Klemz",
"Guido",
""
],
[
"Galdi",
"Alice",
""
],
[
"Kamps",
"Thorsten",
""
]
] | TITLE: Triple Evaporation of Bialkali Antimonide Photocathodes and
Photoemission Characterization at the PhoTEx Experiment
ABSTRACT: The development of high-performance photocathodes is essential for generating
high-brightness electron beams required by existing and future accelerators.
This work introduces a sta... |
2503.03607 | Keqi Chen | Keqi Chen, Zekai Sun, Yuhua Wen, Huijun Lian, Yingming Gao, Ya Li | Psy-Insight: Explainable Multi-turn Bilingual Dataset for Mental Health
Counseling | null | null | null | null | cs.CL | http://creativecommons.org/licenses/by-sa/4.0/ | The in-context learning capabilities of large language models (LLMs) show
great potential in mental health support. However, the lack of counseling
datasets, particularly in Chinese corpora, restricts their application in this
field. To address this, we constructed Psy-Insight, the first mental
health-oriented explai... | [
{
"version": "v1",
"created": "Wed, 5 Mar 2025 15:44:21 GMT"
}
] | 2025-03-06T00:00:00 | [
[
"Chen",
"Keqi",
""
],
[
"Sun",
"Zekai",
""
],
[
"Wen",
"Yuhua",
""
],
[
"Lian",
"Huijun",
""
],
[
"Gao",
"Yingming",
""
],
[
"Li",
"Ya",
""
]
] | TITLE: Psy-Insight: Explainable Multi-turn Bilingual Dataset for Mental Health
Counseling
ABSTRACT: The in-context learning capabilities of large language models (LLMs) show
great potential in mental health support. However, the lack of counseling
datasets, particularly in Chinese corpora, restricts their applica... |
2503.03609 | Lingli Cao | Lingli Cao and He Zhang and Shanshan Li and Danyang Li and Yanjing
Yang and Chenxing Zhong and Xin Zhou and Yue Xie | Enhancing the Accuracy and Comprehensibility in Architectural Tactics
Detection via Small Model-Augmented Prompt Engineering | null | null | null | null | cs.SE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Architectural tactics (ATs), as the concrete implementation of architectural
decisions in code, address non-functional requirements of software systems. Due
to the implicit nature of architectural knowledge in code implementation,
developers may risk inadvertently altering or removing these tactics during
code modifi... | [
{
"version": "v1",
"created": "Wed, 5 Mar 2025 15:47:22 GMT"
}
] | 2025-03-06T00:00:00 | [
[
"Cao",
"Lingli",
""
],
[
"Zhang",
"He",
""
],
[
"Li",
"Shanshan",
""
],
[
"Li",
"Danyang",
""
],
[
"Yang",
"Yanjing",
""
],
[
"Zhong",
"Chenxing",
""
],
[
"Zhou",
"Xin",
""
],
[
"Xie",
"Yue",
... | TITLE: Enhancing the Accuracy and Comprehensibility in Architectural Tactics
Detection via Small Model-Augmented Prompt Engineering
ABSTRACT: Architectural tactics (ATs), as the concrete implementation of architectural
decisions in code, address non-functional requirements of software systems. Due
to the implicit... |
2503.03613 | Songlong Xing | Songlong Xing, Zhengyu Zhao, Nicu Sebe | CLIP is Strong Enough to Fight Back: Test-time Counterattacks towards
Zero-shot Adversarial Robustness of CLIP | Accepted to CVPR 2025 | null | null | null | cs.CV | http://creativecommons.org/licenses/by/4.0/ | Despite its prevalent use in image-text matching tasks in a zero-shot manner,
CLIP has been shown to be highly vulnerable to adversarial perturbations added
onto images. Recent studies propose to finetune the vision encoder of CLIP with
adversarial samples generated on the fly, and show improved robustness against
ad... | [
{
"version": "v1",
"created": "Wed, 5 Mar 2025 15:51:59 GMT"
}
] | 2025-03-06T00:00:00 | [
[
"Xing",
"Songlong",
""
],
[
"Zhao",
"Zhengyu",
""
],
[
"Sebe",
"Nicu",
""
]
] | TITLE: CLIP is Strong Enough to Fight Back: Test-time Counterattacks towards
Zero-shot Adversarial Robustness of CLIP
ABSTRACT: Despite its prevalent use in image-text matching tasks in a zero-shot manner,
CLIP has been shown to be highly vulnerable to adversarial perturbations added
onto images. Recent studies p... |
2503.03622 | Arun Ganesh | Arun Ganesh, Ryan McKenna, Brendan McMahan, Adam Smith, Fan Wu | It's My Data Too: Private ML for Datasets with Multi-User Training
Examples | null | null | null | null | cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We initiate a study of algorithms for model training with user-level
differential privacy (DP), where each example may be attributed to multiple
users, which we call the multi-attribution model. We first provide a carefully
chosen definition of user-level DP under the multi-attribution model. Training
in the multi-at... | [
{
"version": "v1",
"created": "Wed, 5 Mar 2025 16:02:09 GMT"
}
] | 2025-03-06T00:00:00 | [
[
"Ganesh",
"Arun",
""
],
[
"McKenna",
"Ryan",
""
],
[
"McMahan",
"Brendan",
""
],
[
"Smith",
"Adam",
""
],
[
"Wu",
"Fan",
""
]
] | TITLE: It's My Data Too: Private ML for Datasets with Multi-User Training
Examples
ABSTRACT: We initiate a study of algorithms for model training with user-level
differential privacy (DP), where each example may be attributed to multiple
users, which we call the multi-attribution model. We first provide a careful... |
2503.03625 | Anastasia Georgiou | Anastasia Georgiou, Daniel Jungen, Luise Kaven, Verena Hunstig,
Constantine Frangakis, Ioannis Kevrekidis and Alexander Mitsos | Deterministic Global Optimization of the Acquisition Function in
Bayesian Optimization: To Do or Not To Do? | 32 pages, 7 figures, 7 tables | null | null | null | math.OC cs.LG | http://creativecommons.org/licenses/by/4.0/ | Bayesian Optimization (BO) with Gaussian Processes relies on optimizing an
acquisition function to determine sampling. We investigate the advantages and
disadvantages of using a deterministic global solver (MAiNGO) compared to
conventional local and stochastic global solvers (L-BFGS-B and multi-start,
respectively) f... | [
{
"version": "v1",
"created": "Wed, 5 Mar 2025 16:05:26 GMT"
}
] | 2025-03-06T00:00:00 | [
[
"Georgiou",
"Anastasia",
""
],
[
"Jungen",
"Daniel",
""
],
[
"Kaven",
"Luise",
""
],
[
"Hunstig",
"Verena",
""
],
[
"Frangakis",
"Constantine",
""
],
[
"Kevrekidis",
"Ioannis",
""
],
[
"Mitsos",
"Alexander",
... | TITLE: Deterministic Global Optimization of the Acquisition Function in
Bayesian Optimization: To Do or Not To Do?
ABSTRACT: Bayesian Optimization (BO) with Gaussian Processes relies on optimizing an
acquisition function to determine sampling. We investigate the advantages and
disadvantages of using a determinist... |
2503.03637 | WooJin Jung | Woo-Jin Jung, Dong-Hee Paek, and Seung-Hyun Kong | 4D Radar Ground Truth Augmentation with LiDAR-to-4D Radar Data Synthesis | 24 pages | null | null | null | cs.CV eess.IV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Ground truth augmentation (GT-Aug) is a common method for LiDAR-based object
detection, as it enhances object density by leveraging ground truth bounding
boxes (GT bboxes). However, directly applying GT-Aug to 4D Radar tensor data
overlooks important measurements outside the GT bboxes-such as
sidelobes-leading to syn... | [
{
"version": "v1",
"created": "Wed, 5 Mar 2025 16:16:46 GMT"
}
] | 2025-03-06T00:00:00 | [
[
"Jung",
"Woo-Jin",
""
],
[
"Paek",
"Dong-Hee",
""
],
[
"Kong",
"Seung-Hyun",
""
]
] | TITLE: 4D Radar Ground Truth Augmentation with LiDAR-to-4D Radar Data Synthesis
ABSTRACT: Ground truth augmentation (GT-Aug) is a common method for LiDAR-based object
detection, as it enhances object density by leveraging ground truth bounding
boxes (GT bboxes). However, directly applying GT-Aug to 4D Radar tensor ... |
2503.03640 | Yuezhe Tian | Yuezhe Tian, Kangchen Yao, Xiaoyang Yu | An Adaptive Underwater Image Enhancement Framework via Multi-Domain
Fusion and Color Compensation | null | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Underwater optical imaging is severely degraded by light absorption,
scattering, and color distortion, hindering visibility and accurate image
analysis. This paper presents an adaptive enhancement framework integrating
illumination compensation, multi-domain filtering, and dynamic color
correction. A hybrid illuminat... | [
{
"version": "v1",
"created": "Wed, 5 Mar 2025 16:19:56 GMT"
}
] | 2025-03-06T00:00:00 | [
[
"Tian",
"Yuezhe",
""
],
[
"Yao",
"Kangchen",
""
],
[
"Yu",
"Xiaoyang",
""
]
] | TITLE: An Adaptive Underwater Image Enhancement Framework via Multi-Domain
Fusion and Color Compensation
ABSTRACT: Underwater optical imaging is severely degraded by light absorption,
scattering, and color distortion, hindering visibility and accurate image
analysis. This paper presents an adaptive enhancement fr... |
2503.03652 | Re'em Harel | Re'em Harel and Niv Gilboa and Yuval Pinter | Token-Level Privacy in Large Language Models | null | null | null | null | cs.CL cs.CR | http://creativecommons.org/licenses/by/4.0/ | The use of language models as remote services requires transmitting private
information to external providers, raising significant privacy concerns. This
process not only risks exposing sensitive data to untrusted service providers
but also leaves it vulnerable to interception by eavesdroppers. Existing
privacy-prese... | [
{
"version": "v1",
"created": "Wed, 5 Mar 2025 16:27:25 GMT"
}
] | 2025-03-06T00:00:00 | [
[
"Harel",
"Re'em",
""
],
[
"Gilboa",
"Niv",
""
],
[
"Pinter",
"Yuval",
""
]
] | TITLE: Token-Level Privacy in Large Language Models
ABSTRACT: The use of language models as remote services requires transmitting private
information to external providers, raising significant privacy concerns. This
process not only risks exposing sensitive data to untrusted service providers
but also leaves it vul... |
2503.03654 | Jessica Hoffmann | Jessica Hoffmann, Christiane Ahlheim, Zac Yu, Aria Walfrand, Jarvis
Jin, Marie Tano, Ahmad Beirami, Erin van Liemt, Nithum Thain, Hakim Sidahmed
and Lucas Dixon | Improving Neutral Point of View Text Generation through
Parameter-Efficient Reinforcement Learning and a Small-Scale High-Quality
Dataset | null | null | null | null | cs.CL cs.AI cs.LG | http://creativecommons.org/licenses/by/4.0/ | This paper describes the construction of a dataset and the evaluation of
training methods to improve generative large language models' (LLMs) ability to
answer queries on sensitive topics with a Neutral Point of View (NPOV), i.e.,
to provide significantly more informative, diverse and impartial answers. The
dataset, ... | [
{
"version": "v1",
"created": "Wed, 5 Mar 2025 16:32:47 GMT"
}
] | 2025-03-06T00:00:00 | [
[
"Hoffmann",
"Jessica",
""
],
[
"Ahlheim",
"Christiane",
""
],
[
"Yu",
"Zac",
""
],
[
"Walfrand",
"Aria",
""
],
[
"Jin",
"Jarvis",
""
],
[
"Tano",
"Marie",
""
],
[
"Beirami",
"Ahmad",
""
],
[
"van Li... | TITLE: Improving Neutral Point of View Text Generation through
Parameter-Efficient Reinforcement Learning and a Small-Scale High-Quality
Dataset
ABSTRACT: This paper describes the construction of a dataset and the evaluation of
training methods to improve generative large language models' (LLMs) ability to
answ... |
2503.03655 | Thomas P\"ollabauer | Thomas P\"ollabauer, Michael Gasser, Tristan Wirth, Sarah Berkei,
Volker Knauthe, Arjan Kuijper | Improving 6D Object Pose Estimation of metallic Household and Industry
Objects | null | null | null | null | cs.CV cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | 6D object pose estimation suffers from reduced accuracy when applied to
metallic objects. We set out to improve the state-of-the-art by addressing
challenges such as reflections and specular highlights in industrial
applications. Our novel BOP-compatible dataset, featuring a diverse set of
metallic objects (cans, hou... | [
{
"version": "v1",
"created": "Wed, 5 Mar 2025 16:35:15 GMT"
}
] | 2025-03-06T00:00:00 | [
[
"Pöllabauer",
"Thomas",
""
],
[
"Gasser",
"Michael",
""
],
[
"Wirth",
"Tristan",
""
],
[
"Berkei",
"Sarah",
""
],
[
"Knauthe",
"Volker",
""
],
[
"Kuijper",
"Arjan",
""
]
] | TITLE: Improving 6D Object Pose Estimation of metallic Household and Industry
Objects
ABSTRACT: 6D object pose estimation suffers from reduced accuracy when applied to
metallic objects. We set out to improve the state-of-the-art by addressing
challenges such as reflections and specular highlights in industrial
ap... |
2503.03684 | Alina Basharat | Alina Basharat, Yijun Bian, Ping Xu and Zhi Tian | Towards Trustworthy Federated Learning | null | null | null | null | cs.LG cs.CR cs.DC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper develops a comprehensive framework to address three critical
trustworthy challenges in federated learning (FL): robustness against Byzantine
attacks, fairness, and privacy preservation. To improve the system's defense
against Byzantine attacks that send malicious information to bias the system's
performanc... | [
{
"version": "v1",
"created": "Wed, 5 Mar 2025 17:25:20 GMT"
}
] | 2025-03-06T00:00:00 | [
[
"Basharat",
"Alina",
""
],
[
"Bian",
"Yijun",
""
],
[
"Xu",
"Ping",
""
],
[
"Tian",
"Zhi",
""
]
] | TITLE: Towards Trustworthy Federated Learning
ABSTRACT: This paper develops a comprehensive framework to address three critical
trustworthy challenges in federated learning (FL): robustness against Byzantine
attacks, fairness, and privacy preservation. To improve the system's defense
against Byzantine attacks that ... |
2503.03686 | Rui Ye | Rui Ye, Shuo Tang, Rui Ge, Yaxin Du, Zhenfei Yin, Siheng Chen, Jing
Shao | MAS-GPT: Training LLMs to Build LLM-based Multi-Agent Systems | 26 pages, 7 figures | null | null | null | cs.CL cs.MA | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | LLM-based multi-agent systems (MAS) have shown significant potential in
tackling diverse tasks. However, to design effective MAS, existing approaches
heavily rely on manual configurations or multiple calls of advanced LLMs,
resulting in inadaptability and high inference costs. In this paper, we
simplify the process o... | [
{
"version": "v1",
"created": "Wed, 5 Mar 2025 17:27:59 GMT"
}
] | 2025-03-06T00:00:00 | [
[
"Ye",
"Rui",
""
],
[
"Tang",
"Shuo",
""
],
[
"Ge",
"Rui",
""
],
[
"Du",
"Yaxin",
""
],
[
"Yin",
"Zhenfei",
""
],
[
"Chen",
"Siheng",
""
],
[
"Shao",
"Jing",
""
]
] | TITLE: MAS-GPT: Training LLMs to Build LLM-based Multi-Agent Systems
ABSTRACT: LLM-based multi-agent systems (MAS) have shown significant potential in
tackling diverse tasks. However, to design effective MAS, existing approaches
heavily rely on manual configurations or multiple calls of advanced LLMs,
resulting in ... |
2503.03689 | Zhao Yang | Zhao Yang, Zezhong Qian, Xiaofan Li, Weixiang Xu, Gongpeng Zhao,
Ruohong Yu, Lingsi Zhu and Longjun Liu | DualDiff+: Dual-Branch Diffusion for High-Fidelity Video Generation with
Reward Guidance | null | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Accurate and high-fidelity driving scene reconstruction demands the effective
utilization of comprehensive scene information as conditional inputs. Existing
methods predominantly rely on 3D bounding boxes and BEV road maps for
foreground and background control, which fail to capture the full complexity of
driving sce... | [
{
"version": "v1",
"created": "Wed, 5 Mar 2025 17:31:45 GMT"
}
] | 2025-03-06T00:00:00 | [
[
"Yang",
"Zhao",
""
],
[
"Qian",
"Zezhong",
""
],
[
"Li",
"Xiaofan",
""
],
[
"Xu",
"Weixiang",
""
],
[
"Zhao",
"Gongpeng",
""
],
[
"Yu",
"Ruohong",
""
],
[
"Zhu",
"Lingsi",
""
],
[
"Liu",
"Longju... | TITLE: DualDiff+: Dual-Branch Diffusion for High-Fidelity Video Generation with
Reward Guidance
ABSTRACT: Accurate and high-fidelity driving scene reconstruction demands the effective
utilization of comprehensive scene information as conditional inputs. Existing
methods predominantly rely on 3D bounding boxes and... |
2503.03693 | Ungsik Kim | Ungsik Kim | ILLC: Iterative Layer-by-Layer Compression for Enhancing Structural
Faithfulness in SpArX | 8 pages, 2 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In the field of Explainable Artificial Intelligence (XAI), argumentative XAI
approaches have been proposed to represent the internal reasoning process of
deep neural networks in a more transparent way by interpreting hidden nodes as
arguements. However, as the number of layers increases, existing compression
methods ... | [
{
"version": "v1",
"created": "Wed, 5 Mar 2025 17:43:49 GMT"
}
] | 2025-03-06T00:00:00 | [
[
"Kim",
"Ungsik",
""
]
] | TITLE: ILLC: Iterative Layer-by-Layer Compression for Enhancing Structural
Faithfulness in SpArX
ABSTRACT: In the field of Explainable Artificial Intelligence (XAI), argumentative XAI
approaches have been proposed to represent the internal reasoning process of
deep neural networks in a more transparent way by int... |
2503.03702 | Jiyue Jiang | Jiyue Jiang, Alfred Kar Yin Truong, Yanyu Chen, Qinghang Bao, Sheng
Wang, Pengan Chen, Jiuming Wang, Lingpeng Kong, Yu Li, Chuan Wu | Developing and Utilizing a Large-Scale Cantonese Dataset for
Multi-Tasking in Large Language Models | null | null | null | null | cs.CL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | High-quality data resources play a crucial role in learning large language
models (LLMs), particularly for low-resource languages like Cantonese. Despite
having more than 85 million native speakers, Cantonese is still considered a
low-resource language in the field of natural language processing (NLP) due to
factors ... | [
{
"version": "v1",
"created": "Wed, 5 Mar 2025 17:53:07 GMT"
}
] | 2025-03-06T00:00:00 | [
[
"Jiang",
"Jiyue",
""
],
[
"Truong",
"Alfred Kar Yin",
""
],
[
"Chen",
"Yanyu",
""
],
[
"Bao",
"Qinghang",
""
],
[
"Wang",
"Sheng",
""
],
[
"Chen",
"Pengan",
""
],
[
"Wang",
"Jiuming",
""
],
[
"Kong"... | TITLE: Developing and Utilizing a Large-Scale Cantonese Dataset for
Multi-Tasking in Large Language Models
ABSTRACT: High-quality data resources play a crucial role in learning large language
models (LLMs), particularly for low-resource languages like Cantonese. Despite
having more than 85 million native speakers... |
2503.03706 | Ruben Doste | Ruben Doste, Julia Camps, Zhinuo Jenny Wang, Lucas Arantes Berg, Maxx
Holmes, Hannah Smith, Marcel Beetz, Lei Li, Abhirup Banerjee, Vicente Grau,
Blanca Rodriguez | An Automated Computational Pipeline for Generating Large-Scale Cohorts
of Patient-Specific Ventricular Models in Electromechanical In Silico Trials | null | null | null | null | cs.CE | http://creativecommons.org/licenses/by/4.0/ | In recent years, human in silico trials have gained significant traction as a
powerful approach to evaluate the effects of drugs, clinical interventions, and
medical devices. In silico trials not only minimise patient risks but also
reduce reliance on animal testing. However, the implementation of in silico
trials pr... | [
{
"version": "v1",
"created": "Wed, 5 Mar 2025 17:56:49 GMT"
}
] | 2025-03-06T00:00:00 | [
[
"Doste",
"Ruben",
""
],
[
"Camps",
"Julia",
""
],
[
"Wang",
"Zhinuo Jenny",
""
],
[
"Berg",
"Lucas Arantes",
""
],
[
"Holmes",
"Maxx",
""
],
[
"Smith",
"Hannah",
""
],
[
"Beetz",
"Marcel",
""
],
[
"... | TITLE: An Automated Computational Pipeline for Generating Large-Scale Cohorts
of Patient-Specific Ventricular Models in Electromechanical In Silico Trials
ABSTRACT: In recent years, human in silico trials have gained significant traction as a
powerful approach to evaluate the effects of drugs, clinical interventi... |
2503.03707 | Annie Chen | Annie S. Chen, Alec M. Lessing, Yuejiang Liu, Chelsea Finn | Curating Demonstrations using Online Experience | null | null | null | null | cs.RO cs.AI cs.LG | http://creativecommons.org/licenses/by/4.0/ | Many robot demonstration datasets contain heterogeneous demonstrations of
varying quality. This heterogeneity may benefit policy pre-training, but can
hinder robot performance when used with a final imitation learning objective.
In particular, some strategies in the data may be less reliable than others or
may be und... | [
{
"version": "v1",
"created": "Wed, 5 Mar 2025 17:58:16 GMT"
}
] | 2025-03-06T00:00:00 | [
[
"Chen",
"Annie S.",
""
],
[
"Lessing",
"Alec M.",
""
],
[
"Liu",
"Yuejiang",
""
],
[
"Finn",
"Chelsea",
""
]
] | TITLE: Curating Demonstrations using Online Experience
ABSTRACT: Many robot demonstration datasets contain heterogeneous demonstrations of
varying quality. This heterogeneity may benefit policy pre-training, but can
hinder robot performance when used with a final imitation learning objective.
In particular, some st... |
2503.03726 | Jun Yang | Jun Yang, Wenjie Xue, Sahar Ghavidel, Steven L. Waslander | Active 6D Pose Estimation for Textureless Objects using Multi-View RGB
Frames | null | null | null | null | cs.CV cs.RO | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Estimating the 6D pose of textureless objects from RBG images is an important
problem in robotics. Due to appearance ambiguities, rotational symmetries, and
severe occlusions, single-view based 6D pose estimators are still unable to
handle a wide range of objects, motivating research towards multi-view pose
estimatio... | [
{
"version": "v1",
"created": "Wed, 5 Mar 2025 18:28:32 GMT"
}
] | 2025-03-06T00:00:00 | [
[
"Yang",
"Jun",
""
],
[
"Xue",
"Wenjie",
""
],
[
"Ghavidel",
"Sahar",
""
],
[
"Waslander",
"Steven L.",
""
]
] | TITLE: Active 6D Pose Estimation for Textureless Objects using Multi-View RGB
Frames
ABSTRACT: Estimating the 6D pose of textureless objects from RBG images is an important
problem in robotics. Due to appearance ambiguities, rotational symmetries, and
severe occlusions, single-view based 6D pose estimators are st... |
2503.03729 | Sneh Pillai | Sneh Pillai | Graph-Augmented LSTM for Forecasting Sparse Anomalies in
Graph-Structured Time Series | 12 pages | null | null | null | cs.LG | http://creativecommons.org/licenses/by/4.0/ | Detecting anomalies in time series data is a critical task across many
domains. The challenge intensifies when anomalies are sparse and the data are
multivariate with relational dependencies across sensors or nodes. Traditional
univariate anomaly detectors struggle to capture such cross-node dependencies,
particularl... | [
{
"version": "v1",
"created": "Wed, 5 Mar 2025 18:37:52 GMT"
}
] | 2025-03-06T00:00:00 | [
[
"Pillai",
"Sneh",
""
]
] | TITLE: Graph-Augmented LSTM for Forecasting Sparse Anomalies in
Graph-Structured Time Series
ABSTRACT: Detecting anomalies in time series data is a critical task across many
domains. The challenge intensifies when anomalies are sparse and the data are
multivariate with relational dependencies across sensors or no... |
2503.03733 | Amal Shaheen Dr. | Amal Shaheena, Nairouz Mrabahb, Riadh Ksantinia, Abdulla Alqaddoumia | Rethinking Deep Clustering Paradigms: Self-Supervision Is All You Need | null | Volume 181, January 2025, 106773 | null | null | cs.CV cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The recent advances in deep clustering have been made possible by significant
progress in self-supervised and pseudo-supervised learning. However, the
trade-off between self-supervision and pseudo-supervision can give rise to
three primary issues. The joint training causes Feature Randomness and Feature
Drift, wherea... | [
{
"version": "v1",
"created": "Wed, 5 Mar 2025 18:44:35 GMT"
}
] | 2025-03-06T00:00:00 | [
[
"Shaheena",
"Amal",
""
],
[
"Mrabahb",
"Nairouz",
""
],
[
"Ksantinia",
"Riadh",
""
],
[
"Alqaddoumia",
"Abdulla",
""
]
] | TITLE: Rethinking Deep Clustering Paradigms: Self-Supervision Is All You Need
ABSTRACT: The recent advances in deep clustering have been made possible by significant
progress in self-supervised and pseudo-supervised learning. However, the
trade-off between self-supervision and pseudo-supervision can give rise to
th... |
2503.03743 | Yuqi Zhou | Yuqi Zhou, Shuai Wang, Sunhao Dai, Qinglin Jia, Zhaocheng Du, Zhenhua
Dong and Jun Xu | CHOP: Mobile Operating Assistant with Constrained High-frequency
Optimized Subtask Planning | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | The advancement of visual language models (VLMs) has enhanced mobile device
operations, allowing simulated human-like actions to address user requirements.
Current VLM-based mobile operating assistants can be structured into three
levels: task, subtask, and action. The subtask level, linking high-level goals
with low... | [
{
"version": "v1",
"created": "Wed, 5 Mar 2025 18:56:16 GMT"
}
] | 2025-03-06T00:00:00 | [
[
"Zhou",
"Yuqi",
""
],
[
"Wang",
"Shuai",
""
],
[
"Dai",
"Sunhao",
""
],
[
"Jia",
"Qinglin",
""
],
[
"Du",
"Zhaocheng",
""
],
[
"Dong",
"Zhenhua",
""
],
[
"Xu",
"Jun",
""
]
] | TITLE: CHOP: Mobile Operating Assistant with Constrained High-frequency
Optimized Subtask Planning
ABSTRACT: The advancement of visual language models (VLMs) has enhanced mobile device
operations, allowing simulated human-like actions to address user requirements.
Current VLM-based mobile operating assistants can... |
2011.13986 | Johannes Schneider | Johannes Schneider and Michalis Vlachos | Reflective-Net: Learning from Explanations | null | Data Mining and Knowledge Discovery, 1-22, 2023 | 10.1007/s10618-023-00920-0 | null | cs.LG cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We examine whether data generated by explanation techniques, which promote a
process of self-reflection, can improve classifier performance. Our work is
based on the idea that humans have the ability to make quick, intuitive
decisions as well as to reflect on their own thinking and learn from
explanations. To the bes... | [
{
"version": "v1",
"created": "Fri, 27 Nov 2020 20:40:45 GMT"
},
{
"version": "v2",
"created": "Sat, 18 Feb 2023 22:11:22 GMT"
},
{
"version": "v3",
"created": "Tue, 4 Mar 2025 06:42:03 GMT"
}
] | 2025-03-05T00:00:00 | [
[
"Schneider",
"Johannes",
""
],
[
"Vlachos",
"Michalis",
""
]
] | TITLE: Reflective-Net: Learning from Explanations
ABSTRACT: We examine whether data generated by explanation techniques, which promote a
process of self-reflection, can improve classifier performance. Our work is
based on the idea that humans have the ability to make quick, intuitive
decisions as well as to reflect... |
2205.07593 | Shreyas Pai | M\'elanie Cambus and Fabian Kuhn and Etna Lindy and Shreyas Pai and
Jara Uitto | A $(3+\varepsilon)$-Approximate Correlation Clustering Algorithm in
Dynamic Streams | 19 pages. This is the TheoretiCS journal version | TheoretiCS, Volume 4 (February 28, 2025) theoretics:13092 | 10.46298/theoretics.25.6 | null | cs.DS cs.DC | http://creativecommons.org/licenses/by/4.0/ | Grouping together similar elements in datasets is a common task in data
mining and machine learning. In this paper, we study streaming algorithms for
correlation clustering, where each pair of elements is labeled either similar
or dissimilar. The task is to partition the elements and the objective is to
minimize disa... | [
{
"version": "v1",
"created": "Mon, 16 May 2022 11:51:48 GMT"
},
{
"version": "v2",
"created": "Tue, 24 May 2022 13:26:59 GMT"
},
{
"version": "v3",
"created": "Mon, 24 Oct 2022 13:25:07 GMT"
},
{
"version": "v4",
"created": "Tue, 4 Apr 2023 17:50:57 GMT"
},
{
"ve... | 2025-03-05T00:00:00 | [
[
"Cambus",
"Mélanie",
""
],
[
"Kuhn",
"Fabian",
""
],
[
"Lindy",
"Etna",
""
],
[
"Pai",
"Shreyas",
""
],
[
"Uitto",
"Jara",
""
]
] | TITLE: A $(3+\varepsilon)$-Approximate Correlation Clustering Algorithm in
Dynamic Streams
ABSTRACT: Grouping together similar elements in datasets is a common task in data
mining and machine learning. In this paper, we study streaming algorithms for
correlation clustering, where each pair of elements is labeled ... |
2211.10630 | Manxi Lin | Manxi Lin, Aasa Feragen, Kamil Mikolaj, Zahra Bashir, Martin
Gr{\o}nneb{\ae}k Tolsgaard, Anders Nymark Christensen | Explainable fetal ultrasound quality assessment with progressive concept
bottleneck models | null | null | null | null | cs.CV cs.AI | http://creativecommons.org/publicdomain/zero/1.0/ | The quality of fetal ultrasound screening scans directly influences the
precision of biometric measurements. However, acquiring high-quality scans is
labor-intensive and highly relies on the operator's skills. Considering the low
contrastiveness and imaging artifacts that widely exist in ultrasound, even a
dedicated ... | [
{
"version": "v1",
"created": "Sat, 19 Nov 2022 09:31:19 GMT"
},
{
"version": "v2",
"created": "Tue, 4 Mar 2025 02:39:27 GMT"
}
] | 2025-03-05T00:00:00 | [
[
"Lin",
"Manxi",
""
],
[
"Feragen",
"Aasa",
""
],
[
"Mikolaj",
"Kamil",
""
],
[
"Bashir",
"Zahra",
""
],
[
"Tolsgaard",
"Martin Grønnebæk",
""
],
[
"Christensen",
"Anders Nymark",
""
]
] | TITLE: Explainable fetal ultrasound quality assessment with progressive concept
bottleneck models
ABSTRACT: The quality of fetal ultrasound screening scans directly influences the
precision of biometric measurements. However, acquiring high-quality scans is
labor-intensive and highly relies on the operator's skil... |
2303.11858 | Yunjie He | Yunjie He, Mojtaba Nayyeri, Bo Xiong, Yuqicheng Zhu, Evgeny Kharlamov,
Steffen Staab | Modeling Relational Patterns for Logical Query Answering over Knowledge
Graphs | The results reported in this paper are included in our accepted paper
arXiv:2407.09212 at ECAI 2024 | null | null | null | cs.DB cs.AI cs.LG | http://creativecommons.org/licenses/by/4.0/ | Answering first-order logical (FOL) queries over knowledge graphs (KG)
remains a challenging task mainly due to KG incompleteness. Query embedding
approaches this problem by computing the low-dimensional vector representations
of entities, relations, and logical queries. KGs exhibit relational patterns
such as symmet... | [
{
"version": "v1",
"created": "Tue, 21 Mar 2023 13:59:15 GMT"
},
{
"version": "v2",
"created": "Wed, 17 Jul 2024 13:57:25 GMT"
},
{
"version": "v3",
"created": "Tue, 4 Mar 2025 15:03:02 GMT"
}
] | 2025-03-05T00:00:00 | [
[
"He",
"Yunjie",
""
],
[
"Nayyeri",
"Mojtaba",
""
],
[
"Xiong",
"Bo",
""
],
[
"Zhu",
"Yuqicheng",
""
],
[
"Kharlamov",
"Evgeny",
""
],
[
"Staab",
"Steffen",
""
]
] | TITLE: Modeling Relational Patterns for Logical Query Answering over Knowledge
Graphs
ABSTRACT: Answering first-order logical (FOL) queries over knowledge graphs (KG)
remains a challenging task mainly due to KG incompleteness. Query embedding
approaches this problem by computing the low-dimensional vector represe... |
2304.02488 | Fan Yang | Fan Yang | SCB-dataset: A Dataset for Detecting Student Classroom Behavior | null | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Using deep learning methods to detect the classroom behaviors of both
students and teachers is an effective way to automatically analyze classroom
performance and enhance teaching effectiveness. Then, there is still a scarcity
of publicly available high-quality datasets on student-teacher behaviors. Based
on the SCB-... | [
{
"version": "v1",
"created": "Wed, 5 Apr 2023 15:02:30 GMT"
},
{
"version": "v2",
"created": "Fri, 26 Jul 2024 13:31:21 GMT"
},
{
"version": "v3",
"created": "Thu, 28 Nov 2024 04:19:15 GMT"
},
{
"version": "v4",
"created": "Thu, 19 Dec 2024 13:00:35 GMT"
},
{
"ve... | 2025-03-05T00:00:00 | [
[
"Yang",
"Fan",
""
]
] | TITLE: SCB-dataset: A Dataset for Detecting Student Classroom Behavior
ABSTRACT: Using deep learning methods to detect the classroom behaviors of both
students and teachers is an effective way to automatically analyze classroom
performance and enhance teaching effectiveness. Then, there is still a scarcity
of publi... |
2307.07036 | Deressa Wodajo | Deressa Wodajo Deressa, Hannes Mareen, Peter Lambert, Solomon Atnafu,
Zahid Akhtar, Glenn Van Wallendael | GenConViT: Deepfake Video Detection Using Generative Convolutional
Vision Transformer | 11 pages, 4 figures | null | null | null | cs.CV | http://creativecommons.org/licenses/by/4.0/ | Deepfakes have raised significant concerns due to their potential to spread
false information and compromise digital media integrity. Current deepfake
detection models often struggle to generalize across a diverse range of
deepfake generation techniques and video content. In this work, we propose a
Generative Convolu... | [
{
"version": "v1",
"created": "Thu, 13 Jul 2023 19:27:40 GMT"
},
{
"version": "v2",
"created": "Tue, 4 Mar 2025 10:43:51 GMT"
}
] | 2025-03-05T00:00:00 | [
[
"Deressa",
"Deressa Wodajo",
""
],
[
"Mareen",
"Hannes",
""
],
[
"Lambert",
"Peter",
""
],
[
"Atnafu",
"Solomon",
""
],
[
"Akhtar",
"Zahid",
""
],
[
"Van Wallendael",
"Glenn",
""
]
] | TITLE: GenConViT: Deepfake Video Detection Using Generative Convolutional
Vision Transformer
ABSTRACT: Deepfakes have raised significant concerns due to their potential to spread
false information and compromise digital media integrity. Current deepfake
detection models often struggle to generalize across a diver... |
2308.10373 | Hejia Geng | Hejia Geng, Peng Li | HoSNN: Adversarially-Robust Homeostatic Spiking Neural Networks with
Adaptive Firing Thresholds | Accepted by TMLR | null | null | null | cs.NE cs.CR cs.CV cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | While spiking neural networks (SNNs) offer a promising neurally-inspired
model of computation, they are vulnerable to adversarial attacks. We present
the first study that draws inspiration from neural homeostasis to design a
threshold-adapting leaky integrate-and-fire (TA-LIF) neuron model and utilize
TA-LIF neurons ... | [
{
"version": "v1",
"created": "Sun, 20 Aug 2023 21:47:54 GMT"
},
{
"version": "v2",
"created": "Sun, 22 Oct 2023 19:48:02 GMT"
},
{
"version": "v3",
"created": "Fri, 31 May 2024 23:45:57 GMT"
},
{
"version": "v4",
"created": "Tue, 4 Mar 2025 01:24:52 GMT"
}
] | 2025-03-05T00:00:00 | [
[
"Geng",
"Hejia",
""
],
[
"Li",
"Peng",
""
]
] | TITLE: HoSNN: Adversarially-Robust Homeostatic Spiking Neural Networks with
Adaptive Firing Thresholds
ABSTRACT: While spiking neural networks (SNNs) offer a promising neurally-inspired
model of computation, they are vulnerable to adversarial attacks. We present
the first study that draws inspiration from neural ... |
2309.02244 | Amelia Jim\'enez-S\'anchez | Veronika Cheplygina, Cathrine Damgaard, Trine Naja Eriksen, Dovile
Juodelyte, Amelia Jim\'enez-S\'anchez | Augmenting Chest X-ray Datasets with Non-Expert Annotations | null | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The advancement of machine learning algorithms in medical image analysis
requires the expansion of training datasets. A popular and cost-effective
approach is automated annotation extraction from free-text medical reports,
primarily due to the high costs associated with expert clinicians annotating
medical images, su... | [
{
"version": "v1",
"created": "Tue, 5 Sep 2023 13:52:43 GMT"
},
{
"version": "v2",
"created": "Tue, 4 Mar 2025 13:04:45 GMT"
}
] | 2025-03-05T00:00:00 | [
[
"Cheplygina",
"Veronika",
""
],
[
"Damgaard",
"Cathrine",
""
],
[
"Eriksen",
"Trine Naja",
""
],
[
"Juodelyte",
"Dovile",
""
],
[
"Jiménez-Sánchez",
"Amelia",
""
]
] | TITLE: Augmenting Chest X-ray Datasets with Non-Expert Annotations
ABSTRACT: The advancement of machine learning algorithms in medical image analysis
requires the expansion of training datasets. A popular and cost-effective
approach is automated annotation extraction from free-text medical reports,
primarily due to... |
2310.07584 | Laurenz Ruzicka | Laurenz Ruzicka and Bernhard Strobl and Bernhard Kohn and Clemens
Heitzinger | Centrality of the Fingerprint Core Location | null | In Proceedings of the 17th International Joint Conference on
Biomedical Engineering Systems and Technologie, 2024 | 10.5220/0012309300003657 | olume 1, pages 713-720 | cs.CV | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Fingerprints have long been recognized as a unique and reliable means of
personal identification. Central to the analysis and enhancement of
fingerprints is the concept of the fingerprint core. Although the location of
the core is used in many applications, to the best of our knowledge, this study
is the first to inv... | [
{
"version": "v1",
"created": "Wed, 11 Oct 2023 15:20:44 GMT"
}
] | 2025-03-05T00:00:00 | [
[
"Ruzicka",
"Laurenz",
""
],
[
"Strobl",
"Bernhard",
""
],
[
"Kohn",
"Bernhard",
""
],
[
"Heitzinger",
"Clemens",
""
]
] | TITLE: Centrality of the Fingerprint Core Location
ABSTRACT: Fingerprints have long been recognized as a unique and reliable means of
personal identification. Central to the analysis and enhancement of
fingerprints is the concept of the fingerprint core. Although the location of
the core is used in many application... |
2310.10315 | Alberto Marchisio | Kamila Zaman and Alberto Marchisio and Muhammad Abdullah Hanif and
Muhammad Shafique | A Survey on Quantum Machine Learning: Current Trends, Challenges,
Opportunities, and the Road Ahead | null | null | null | null | quant-ph cs.LG | http://creativecommons.org/licenses/by/4.0/ | Quantum Computing (QC) claims to improve the efficiency of solving complex
problems, compared to classical computing. When QC is integrated with Machine
Learning (ML), it creates a Quantum Machine Learning (QML) system. This paper
aims to provide a thorough understanding of the foundational concepts of QC and
its not... | [
{
"version": "v1",
"created": "Mon, 16 Oct 2023 11:52:54 GMT"
},
{
"version": "v2",
"created": "Sat, 27 Jul 2024 08:08:45 GMT"
},
{
"version": "v3",
"created": "Tue, 4 Mar 2025 07:25:39 GMT"
}
] | 2025-03-05T00:00:00 | [
[
"Zaman",
"Kamila",
""
],
[
"Marchisio",
"Alberto",
""
],
[
"Hanif",
"Muhammad Abdullah",
""
],
[
"Shafique",
"Muhammad",
""
]
] | TITLE: A Survey on Quantum Machine Learning: Current Trends, Challenges,
Opportunities, and the Road Ahead
ABSTRACT: Quantum Computing (QC) claims to improve the efficiency of solving complex
problems, compared to classical computing. When QC is integrated with Machine
Learning (ML), it creates a Quantum Machine ... |
2311.13121 | Yang Li | Yang Li, Qi'ao Zhao, Chen Lin, Zhenjie Zhang, Xiaomin Zhu, Jinsong Su | GENET: Unleashing the Power of Side Information for Recommendation via
Hypergraph Pre-training | null | null | null | null | cs.IR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recommendation with side information has drawn significant research interest
due to its potential to mitigate user feedback sparsity. However, existing
models struggle with generalization across diverse domains and types of side
information. In particular, three challenges have not been addressed, and they
are (1) th... | [
{
"version": "v1",
"created": "Wed, 22 Nov 2023 02:49:14 GMT"
},
{
"version": "v2",
"created": "Tue, 4 Mar 2025 12:17:16 GMT"
}
] | 2025-03-05T00:00:00 | [
[
"Li",
"Yang",
""
],
[
"Zhao",
"Qi'ao",
""
],
[
"Lin",
"Chen",
""
],
[
"Zhang",
"Zhenjie",
""
],
[
"Zhu",
"Xiaomin",
""
],
[
"Su",
"Jinsong",
""
]
] | TITLE: GENET: Unleashing the Power of Side Information for Recommendation via
Hypergraph Pre-training
ABSTRACT: Recommendation with side information has drawn significant research interest
due to its potential to mitigate user feedback sparsity. However, existing
models struggle with generalization across diverse... |
2401.02702 | Lin Liu | Ziying Song, Guoxin Zhang, Jun Xie, Lin Liu, Caiyan Jia, Shaoqing Xu,
Zhepeng Wang | VoxelNextFusion: A Simple, Unified and Effective Voxel Fusion Framework
for Multi-Modal 3D Object Detection | null | IEEE Transactions on Geoscience and Remote Sensing, vol. 61, 2023,
pp. 1-12 | 10.1109/TGRS.2023.3331893 | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | LiDAR-camera fusion can enhance the performance of 3D object detection by
utilizing complementary information between depth-aware LiDAR points and
semantically rich images. Existing voxel-based methods face significant
challenges when fusing sparse voxel features with dense image features in a
one-to-one manner, resu... | [
{
"version": "v1",
"created": "Fri, 5 Jan 2024 08:10:49 GMT"
},
{
"version": "v2",
"created": "Tue, 4 Mar 2025 03:16:54 GMT"
}
] | 2025-03-05T00:00:00 | [
[
"Song",
"Ziying",
""
],
[
"Zhang",
"Guoxin",
""
],
[
"Xie",
"Jun",
""
],
[
"Liu",
"Lin",
""
],
[
"Jia",
"Caiyan",
""
],
[
"Xu",
"Shaoqing",
""
],
[
"Wang",
"Zhepeng",
""
]
] | TITLE: VoxelNextFusion: A Simple, Unified and Effective Voxel Fusion Framework
for Multi-Modal 3D Object Detection
ABSTRACT: LiDAR-camera fusion can enhance the performance of 3D object detection by
utilizing complementary information between depth-aware LiDAR points and
semantically rich images. Existing voxel-b... |
2401.04720 | Benedikt Roth | Benedikt Roth, Valentin Koch, Sophia J. Wagner, Julia A. Schnabel,
Carsten Marr, Tingying Peng | Low-resource finetuning of foundation models beats state-of-the-art in
histopathology | null | 2024 IEEE International Symposium on Biomedical Imaging (ISBI) | 10.1109/ISBI56570.2024.10635695 | null | cs.CV | http://creativecommons.org/licenses/by/4.0/ | To handle the large scale of whole slide images in computational pathology,
most approaches first tessellate the images into smaller patches, extract
features from these patches, and finally aggregate the feature vectors with
weakly-supervised learning. The performance of this workflow strongly depends
on the quality... | [
{
"version": "v1",
"created": "Tue, 9 Jan 2024 18:46:59 GMT"
}
] | 2025-03-05T00:00:00 | [
[
"Roth",
"Benedikt",
""
],
[
"Koch",
"Valentin",
""
],
[
"Wagner",
"Sophia J.",
""
],
[
"Schnabel",
"Julia A.",
""
],
[
"Marr",
"Carsten",
""
],
[
"Peng",
"Tingying",
""
]
] | TITLE: Low-resource finetuning of foundation models beats state-of-the-art in
histopathology
ABSTRACT: To handle the large scale of whole slide images in computational pathology,
most approaches first tessellate the images into smaller patches, extract
features from these patches, and finally aggregate the featur... |
2402.11480 | Kun Ma | Kun Ma, Cong Xu, Zeyuan Chen, Wei Zhang | Pattern-wise Transparent Sequential Recommendation | This paper has been accepted by IEEE TKDE | null | null | null | cs.IR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A transparent decision-making process is essential for developing reliable
and trustworthy recommender systems. For sequential recommendation, it means
that the model can identify key items that account for its recommendation
results. However, achieving both interpretability and recommendation
performance simultaneou... | [
{
"version": "v1",
"created": "Sun, 18 Feb 2024 07:06:17 GMT"
},
{
"version": "v2",
"created": "Thu, 29 Feb 2024 13:03:36 GMT"
},
{
"version": "v3",
"created": "Sat, 9 Mar 2024 09:37:53 GMT"
},
{
"version": "v4",
"created": "Sun, 18 Aug 2024 15:36:17 GMT"
},
{
"ve... | 2025-03-05T00:00:00 | [
[
"Ma",
"Kun",
""
],
[
"Xu",
"Cong",
""
],
[
"Chen",
"Zeyuan",
""
],
[
"Zhang",
"Wei",
""
]
] | TITLE: Pattern-wise Transparent Sequential Recommendation
ABSTRACT: A transparent decision-making process is essential for developing reliable
and trustworthy recommender systems. For sequential recommendation, it means
that the model can identify key items that account for its recommendation
results. However, achi... |
2403.15422 | Xiaozhou Ye | Xiaozhou Ye, Kouichi Sakurai, Nirmal Nair, Kevin I-Kai Wang | Machine Learning Techniques for Sensor-based Human Activity Recognition
with Data Heterogeneity -- A Review | null | Sensors, 2024, 24(24), 7975 | 10.3390/s24247975 | null | eess.SP cs.AI cs.HC cs.LG | http://creativecommons.org/licenses/by/4.0/ | Sensor-based Human Activity Recognition (HAR) is crucial in ubiquitous
computing, analysing behaviours through multi-dimensional observations. Despite
research progress, HAR confronts challenges, particularly in data distribution
assumptions. Most studies often assume uniform data distributions across
datasets, contr... | [
{
"version": "v1",
"created": "Tue, 12 Mar 2024 22:22:14 GMT"
}
] | 2025-03-05T00:00:00 | [
[
"Ye",
"Xiaozhou",
""
],
[
"Sakurai",
"Kouichi",
""
],
[
"Nair",
"Nirmal",
""
],
[
"Wang",
"Kevin I-Kai",
""
]
] | TITLE: Machine Learning Techniques for Sensor-based Human Activity Recognition
with Data Heterogeneity -- A Review
ABSTRACT: Sensor-based Human Activity Recognition (HAR) is crucial in ubiquitous
computing, analysing behaviours through multi-dimensional observations. Despite
research progress, HAR confronts chall... |
2403.15423 | Xiaozhou Ye | Xiaozhou Ye, Kevin I-Kai Wang | Cross-user activity recognition via temporal relation optimal transport | null | International Conference on Mobile and Ubiquitous Systems:
Computing, Networking, and Services, pp. 355-374. Cham: Springer Nature
Switzerland, 2023 | 10.1007/978-3-031-63989-0_18 | null | eess.SP cs.AI cs.CV cs.HC cs.LG | http://creativecommons.org/licenses/by/4.0/ | Current research on human activity recognition (HAR) mainly assumes that
training and testing data are drawn from the same distribution to achieve a
generalised model, which means all the data are considered to be independent
and identically distributed $\displaystyle (i.i.d.) $. In many real-world
applications, this... | [
{
"version": "v1",
"created": "Tue, 12 Mar 2024 22:33:56 GMT"
}
] | 2025-03-05T00:00:00 | [
[
"Ye",
"Xiaozhou",
""
],
[
"Wang",
"Kevin I-Kai",
""
]
] | TITLE: Cross-user activity recognition via temporal relation optimal transport
ABSTRACT: Current research on human activity recognition (HAR) mainly assumes that
training and testing data are drawn from the same distribution to achieve a
generalised model, which means all the data are considered to be independent
a... |
2403.17958 | Xiaozhou Ye | Xiaozhou Ye, Kevin I-Kai Wang | Deep Generative Domain Adaptation with Temporal Attention for Cross-User
Activity Recognition | null | Pattern Recognition, Volume 156, December 2024, 110811 | 10.1016/j.patcog.2024.110811 | null | cs.LG cs.AI cs.CV cs.HC | http://creativecommons.org/licenses/by/4.0/ | In Human Activity Recognition (HAR), a predominant assumption is that the
data utilized for training and evaluation purposes are drawn from the same
distribution. It is also assumed that all data samples are independent and
identically distributed ($\displaystyle i.i.d.$). Contrarily, practical
implementations often ... | [
{
"version": "v1",
"created": "Tue, 12 Mar 2024 22:45:05 GMT"
}
] | 2025-03-05T00:00:00 | [
[
"Ye",
"Xiaozhou",
""
],
[
"Wang",
"Kevin I-Kai",
""
]
] | TITLE: Deep Generative Domain Adaptation with Temporal Attention for Cross-User
Activity Recognition
ABSTRACT: In Human Activity Recognition (HAR), a predominant assumption is that the
data utilized for training and evaluation purposes are drawn from the same
distribution. It is also assumed that all data samples... |
2403.18281 | Changkun Liu | Changkun Liu, Jianhao Jiao, Huajian Huang, Zhengyang Ma, Dimitrios
Kanoulas, Tristan Braud | AIR-HLoc: Adaptive Retrieved Images Selection for Efficient Visual
Localisation | Accepted to the 2025 IEEE International Conference on Robotics and
Automation (ICRA) | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | State-of-the-art hierarchical localisation pipelines (HLoc) employ image
retrieval (IR) to establish 2D-3D correspondences by selecting the top-$k$ most
similar images from a reference database. While increasing $k$ improves
localisation robustness, it also linearly increases computational cost and
runtime, creating ... | [
{
"version": "v1",
"created": "Wed, 27 Mar 2024 06:17:21 GMT"
},
{
"version": "v2",
"created": "Tue, 17 Sep 2024 03:09:15 GMT"
},
{
"version": "v3",
"created": "Tue, 4 Mar 2025 04:31:55 GMT"
}
] | 2025-03-05T00:00:00 | [
[
"Liu",
"Changkun",
""
],
[
"Jiao",
"Jianhao",
""
],
[
"Huang",
"Huajian",
""
],
[
"Ma",
"Zhengyang",
""
],
[
"Kanoulas",
"Dimitrios",
""
],
[
"Braud",
"Tristan",
""
]
] | TITLE: AIR-HLoc: Adaptive Retrieved Images Selection for Efficient Visual
Localisation
ABSTRACT: State-of-the-art hierarchical localisation pipelines (HLoc) employ image
retrieval (IR) to establish 2D-3D correspondences by selecting the top-$k$ most
similar images from a reference database. While increasing $k$ i... |
2404.08254 | Zeyu Yang | Zeyu Yang, Han Yu, Peikun Guo, Khadija Zanna, Xiaoxue Yang, Akane Sano | Balanced Mixed-Type Tabular Data Synthesis with Diffusion Models | OpenReview: https://openreview.net/forum?id=dvRysCqmYQ | Transactions on Machine Learning Research, ISSN 2835-8856 (2025) | null | null | cs.LG | http://creativecommons.org/licenses/by/4.0/ | Diffusion models have emerged as a robust framework for various generative
tasks, including tabular data synthesis. However, current tabular diffusion
models tend to inherit bias in the training dataset and generate biased
synthetic data, which may influence discriminatory actions. In this research,
we introduce a no... | [
{
"version": "v1",
"created": "Fri, 12 Apr 2024 06:08:43 GMT"
},
{
"version": "v2",
"created": "Wed, 6 Nov 2024 03:23:14 GMT"
},
{
"version": "v3",
"created": "Tue, 4 Mar 2025 07:39:04 GMT"
}
] | 2025-03-05T00:00:00 | [
[
"Yang",
"Zeyu",
""
],
[
"Yu",
"Han",
""
],
[
"Guo",
"Peikun",
""
],
[
"Zanna",
"Khadija",
""
],
[
"Yang",
"Xiaoxue",
""
],
[
"Sano",
"Akane",
""
]
] | TITLE: Balanced Mixed-Type Tabular Data Synthesis with Diffusion Models
ABSTRACT: Diffusion models have emerged as a robust framework for various generative
tasks, including tabular data synthesis. However, current tabular diffusion
models tend to inherit bias in the training dataset and generate biased
synthetic d... |
2404.09299 | Dror Markus | Dror K. Markus, Effi Levi, Tamir Sheafer, and Shaul R. Shenhav | Reap the Wild Wind: Detecting Media Storms in Large-Scale News Corpora | This paper was accepted and published in Findings of EMNLP 2024. The
final version is available at:
https://aclanthology.org/2024.findings-emnlp.275/ | null | null | null | cs.CL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Media Storms, dramatic outbursts of attention to a story, are central
components of media dynamics and the attention landscape. Despite their
significance, there has been little systematic and empirical research on this
concept due to issues of measurement and operationalization. We introduce an
iterative human-in-th... | [
{
"version": "v1",
"created": "Sun, 14 Apr 2024 16:47:38 GMT"
},
{
"version": "v2",
"created": "Tue, 4 Mar 2025 13:10:27 GMT"
}
] | 2025-03-05T00:00:00 | [
[
"Markus",
"Dror K.",
""
],
[
"Levi",
"Effi",
""
],
[
"Sheafer",
"Tamir",
""
],
[
"Shenhav",
"Shaul R.",
""
]
] | TITLE: Reap the Wild Wind: Detecting Media Storms in Large-Scale News Corpora
ABSTRACT: Media Storms, dramatic outbursts of attention to a story, are central
components of media dynamics and the attention landscape. Despite their
significance, there has been little systematic and empirical research on this
concept ... |
2404.15274 | Matt Cheung | Matt Y Cheung, Tucker J Netherton, Laurence E Court, Ashok
Veeraraghavan, Guha Balakrishnan | Metric-Guided Conformal Bounds for Probabilistic Image Reconstruction | 11 pages, 4 figures, 1 table, 2 algorithms. Code available at
https://github.com/matthewyccheung/conformal-metric. Previously titled
"Metric-guided Image Reconstruction Bounds via Conformal Prediction" | null | null | null | cs.LG cs.CV eess.IV physics.med-ph | http://creativecommons.org/licenses/by/4.0/ | Modern deep learning reconstruction algorithms generate impressively
realistic scans from sparse inputs, but can often produce significant
inaccuracies. This makes it difficult to provide statistically guaranteed
claims about the true state of a subject from scans reconstructed by these
algorithms. In this study, we ... | [
{
"version": "v1",
"created": "Tue, 23 Apr 2024 17:59:12 GMT"
},
{
"version": "v2",
"created": "Tue, 2 Jul 2024 03:31:16 GMT"
},
{
"version": "v3",
"created": "Tue, 4 Mar 2025 04:07:12 GMT"
}
] | 2025-03-05T00:00:00 | [
[
"Cheung",
"Matt Y",
""
],
[
"Netherton",
"Tucker J",
""
],
[
"Court",
"Laurence E",
""
],
[
"Veeraraghavan",
"Ashok",
""
],
[
"Balakrishnan",
"Guha",
""
]
] | TITLE: Metric-Guided Conformal Bounds for Probabilistic Image Reconstruction
ABSTRACT: Modern deep learning reconstruction algorithms generate impressively
realistic scans from sparse inputs, but can often produce significant
inaccuracies. This makes it difficult to provide statistically guaranteed
claims about the... |
2405.00200 | Amanda Bertsch | Amanda Bertsch, Maor Ivgi, Emily Xiao, Uri Alon, Jonathan Berant,
Matthew R. Gormley, Graham Neubig | In-Context Learning with Long-Context Models: An In-Depth Exploration | 32 pages; NAACL 2025 camera-ready | null | null | null | cs.CL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | As model context lengths continue to increase, the number of demonstrations
that can be provided in-context approaches the size of entire training
datasets. We study the behavior of in-context learning (ICL) at this extreme
scale on multiple datasets and models. We show that, for many datasets with
large label spaces... | [
{
"version": "v1",
"created": "Tue, 30 Apr 2024 21:06:52 GMT"
},
{
"version": "v2",
"created": "Mon, 3 Mar 2025 19:53:28 GMT"
}
] | 2025-03-05T00:00:00 | [
[
"Bertsch",
"Amanda",
""
],
[
"Ivgi",
"Maor",
""
],
[
"Xiao",
"Emily",
""
],
[
"Alon",
"Uri",
""
],
[
"Berant",
"Jonathan",
""
],
[
"Gormley",
"Matthew R.",
""
],
[
"Neubig",
"Graham",
""
]
] | TITLE: In-Context Learning with Long-Context Models: An In-Depth Exploration
ABSTRACT: As model context lengths continue to increase, the number of demonstrations
that can be provided in-context approaches the size of entire training
datasets. We study the behavior of in-context learning (ICL) at this extreme
scale... |
2405.03714 | Devaansh Gupta | Siddhant Kharbanda, Devaansh Gupta, Gururaj K, Pankaj Malhotra, Amit
Singh, Cho-Jui Hsieh, Rohit Babbar | UniDEC : Unified Dual Encoder and Classifier Training for Extreme
Multi-Label Classification | null | In Proceedings of the ACM Web Conference 2025 (WWW 2025) | null | null | cs.LG cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Extreme Multi-label Classification (XMC) involves predicting a subset of
relevant labels from an extremely large label space, given an input query and
labels with textual features. Models developed for this problem have
conventionally made use of dual encoder (DE) to embed the queries and label
texts and one-vs-all (... | [
{
"version": "v1",
"created": "Sat, 4 May 2024 17:27:51 GMT"
},
{
"version": "v2",
"created": "Mon, 3 Mar 2025 19:29:02 GMT"
}
] | 2025-03-05T00:00:00 | [
[
"Kharbanda",
"Siddhant",
""
],
[
"Gupta",
"Devaansh",
""
],
[
"K",
"Gururaj",
""
],
[
"Malhotra",
"Pankaj",
""
],
[
"Singh",
"Amit",
""
],
[
"Hsieh",
"Cho-Jui",
""
],
[
"Babbar",
"Rohit",
""
]
] | TITLE: UniDEC : Unified Dual Encoder and Classifier Training for Extreme
Multi-Label Classification
ABSTRACT: Extreme Multi-label Classification (XMC) involves predicting a subset of
relevant labels from an extremely large label space, given an input query and
labels with textual features. Models developed for th... |
2405.04309 | Jiawei Shi | Jiawei Shi, Hui Deng, Yuchao Dai | Non-rigid Structure-from-Motion: Temporally-smooth Procrustean Alignment
and Spatially-variant Deformation Modeling | Accepted by CVPR 2024; The new version adds additional experiments
and corrects typos | null | null | null | cs.CV cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Even though Non-rigid Structure-from-Motion (NRSfM) has been extensively
studied and great progress has been made, there are still key challenges that
hinder their broad real-world applications: 1) the inherent motion/rotation
ambiguity requires either explicit camera motion recovery with extra constraint
or complex ... | [
{
"version": "v1",
"created": "Tue, 7 May 2024 13:33:50 GMT"
},
{
"version": "v2",
"created": "Mon, 24 Jun 2024 01:30:48 GMT"
},
{
"version": "v3",
"created": "Tue, 4 Mar 2025 08:37:43 GMT"
}
] | 2025-03-05T00:00:00 | [
[
"Shi",
"Jiawei",
""
],
[
"Deng",
"Hui",
""
],
[
"Dai",
"Yuchao",
""
]
] | TITLE: Non-rigid Structure-from-Motion: Temporally-smooth Procrustean Alignment
and Spatially-variant Deformation Modeling
ABSTRACT: Even though Non-rigid Structure-from-Motion (NRSfM) has been extensively
studied and great progress has been made, there are still key challenges that
hinder their broad real-world ... |
2405.05998 | Niki Kilbertus | Zhufeng Li and Sandeep S Cranganore and Nicholas Youngblut and Niki
Kilbertus | Whole Genome Transformer for Gene Interaction Effects in Microbiome
Habitat Specificity | published at AAAI 2025 | null | null | null | q-bio.GN cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Leveraging the vast genetic diversity within microbiomes offers unparalleled
insights into complex phenotypes, yet the task of accurately predicting and
understanding such traits from genomic data remains challenging. We propose a
framework taking advantage of existing large models for gene vectorization to
predict h... | [
{
"version": "v1",
"created": "Thu, 9 May 2024 09:34:51 GMT"
},
{
"version": "v2",
"created": "Tue, 28 May 2024 10:59:16 GMT"
},
{
"version": "v3",
"created": "Mon, 3 Mar 2025 21:31:23 GMT"
}
] | 2025-03-05T00:00:00 | [
[
"Li",
"Zhufeng",
""
],
[
"Cranganore",
"Sandeep S",
""
],
[
"Youngblut",
"Nicholas",
""
],
[
"Kilbertus",
"Niki",
""
]
] | TITLE: Whole Genome Transformer for Gene Interaction Effects in Microbiome
Habitat Specificity
ABSTRACT: Leveraging the vast genetic diversity within microbiomes offers unparalleled
insights into complex phenotypes, yet the task of accurately predicting and
understanding such traits from genomic data remains chal... |
2405.10822 | Samantha J. Fournier | Samantha J. Fournier, Pierfrancesco Urbani | Generative modeling through internal high-dimensional chaotic activity | null | null | null | null | cs.LG cond-mat.dis-nn | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Generative modeling aims at producing new datapoints whose statistical
properties resemble the ones in a training dataset. In recent years, there has
been a burst of machine learning techniques and settings that can achieve this
goal with remarkable performances. In most of these settings, one uses the
training datas... | [
{
"version": "v1",
"created": "Fri, 17 May 2024 14:43:30 GMT"
},
{
"version": "v2",
"created": "Tue, 4 Mar 2025 11:17:59 GMT"
}
] | 2025-03-05T00:00:00 | [
[
"Fournier",
"Samantha J.",
""
],
[
"Urbani",
"Pierfrancesco",
""
]
] | TITLE: Generative modeling through internal high-dimensional chaotic activity
ABSTRACT: Generative modeling aims at producing new datapoints whose statistical
properties resemble the ones in a training dataset. In recent years, there has
been a burst of machine learning techniques and settings that can achieve this... |
2405.13152 | Shiji Huang | Shiji Huang, Lei Ye, Min Chen, Wenhai Luo, Dihong Wang, Chenqi Xu,
Deyuan Liang | Interpretable Interaction Modeling for Trajectory Prediction via Agent
Selection and Physical Coefficient | code:https://github.com/kkk00714/ASPILin | null | null | null | cs.CV cs.AI | http://creativecommons.org/licenses/by/4.0/ | A thorough understanding of the interaction between the target agent and
surrounding agents is a prerequisite for accurate trajectory prediction.
Although many methods have been explored, they assign correlation coefficients
to surrounding agents in a purely learning-based manner. In this study, we
present ASPILin, w... | [
{
"version": "v1",
"created": "Tue, 21 May 2024 18:45:18 GMT"
},
{
"version": "v2",
"created": "Fri, 11 Oct 2024 19:40:39 GMT"
},
{
"version": "v3",
"created": "Wed, 23 Oct 2024 12:56:05 GMT"
},
{
"version": "v4",
"created": "Tue, 4 Mar 2025 13:07:09 GMT"
}
] | 2025-03-05T00:00:00 | [
[
"Huang",
"Shiji",
""
],
[
"Ye",
"Lei",
""
],
[
"Chen",
"Min",
""
],
[
"Luo",
"Wenhai",
""
],
[
"Wang",
"Dihong",
""
],
[
"Xu",
"Chenqi",
""
],
[
"Liang",
"Deyuan",
""
]
] | TITLE: Interpretable Interaction Modeling for Trajectory Prediction via Agent
Selection and Physical Coefficient
ABSTRACT: A thorough understanding of the interaction between the target agent and
surrounding agents is a prerequisite for accurate trajectory prediction.
Although many methods have been explored, the... |
2405.14093 | Yueen Ma | Yueen Ma, Zixing Song, Yuzheng Zhuang, Jianye Hao, Irwin King | A Survey on Vision-Language-Action Models for Embodied AI | Project page: https://github.com/yueen-ma/Awesome-VLA | null | null | null | cs.RO cs.CL cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Embodied AI is widely recognized as a key element of artificial general
intelligence because it involves controlling embodied agents to perform tasks
in the physical world. Building on the success of large language models and
vision-language models, a new category of multimodal models -- referred to as
vision-languag... | [
{
"version": "v1",
"created": "Thu, 23 May 2024 01:43:54 GMT"
},
{
"version": "v2",
"created": "Thu, 28 Nov 2024 09:18:10 GMT"
},
{
"version": "v3",
"created": "Mon, 3 Mar 2025 03:19:31 GMT"
},
{
"version": "v4",
"created": "Tue, 4 Mar 2025 08:24:20 GMT"
}
] | 2025-03-05T00:00:00 | [
[
"Ma",
"Yueen",
""
],
[
"Song",
"Zixing",
""
],
[
"Zhuang",
"Yuzheng",
""
],
[
"Hao",
"Jianye",
""
],
[
"King",
"Irwin",
""
]
] | TITLE: A Survey on Vision-Language-Action Models for Embodied AI
ABSTRACT: Embodied AI is widely recognized as a key element of artificial general
intelligence because it involves controlling embodied agents to perform tasks
in the physical world. Building on the success of large language models and
vision-language... |
2405.16792 | Eric Mugnier | Eric Mugnier, Emmanuel Anaya Gonzalez, Ranjit Jhala, Nadia
Polikarpova, Yuanyuan Zhou | Laurel: Unblocking Automated Verification with Large Language Models | 34 pages, accepted at OOPSLA 25 | null | null | null | cs.LO cs.AI | http://creativecommons.org/licenses/by/4.0/ | Program verifiers such as Dafny automate proofs by outsourcing them to an SMT
solver. This automation is not perfect, however, and the solver often requires
hints in the form of assertions, creating a burden for the proof engineer. In
this paper, we propose Laurel, a tool that alleviates this burden by
automatically ... | [
{
"version": "v1",
"created": "Mon, 27 May 2024 03:26:01 GMT"
},
{
"version": "v2",
"created": "Mon, 3 Mar 2025 22:24:37 GMT"
}
] | 2025-03-05T00:00:00 | [
[
"Mugnier",
"Eric",
""
],
[
"Gonzalez",
"Emmanuel Anaya",
""
],
[
"Jhala",
"Ranjit",
""
],
[
"Polikarpova",
"Nadia",
""
],
[
"Zhou",
"Yuanyuan",
""
]
] | TITLE: Laurel: Unblocking Automated Verification with Large Language Models
ABSTRACT: Program verifiers such as Dafny automate proofs by outsourcing them to an SMT
solver. This automation is not perfect, however, and the solver often requires
hints in the form of assertions, creating a burden for the proof engineer... |
2406.00783 | Li Lin | Li Lin, Santosh, Mingyang Wu, Xin Wang, Shu Hu | AI-Face: A Million-Scale Demographically Annotated AI-Generated Face
Dataset and Fairness Benchmark | This paper has been accepted by CVPR 2025 | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | AI-generated faces have enriched human life, such as entertainment,
education, and art. However, they also pose misuse risks. Therefore, detecting
AI-generated faces becomes crucial, yet current detectors show biased
performance across different demographic groups. Mitigating biases can be done
by designing algorithm... | [
{
"version": "v1",
"created": "Sun, 2 Jun 2024 15:51:33 GMT"
},
{
"version": "v2",
"created": "Tue, 4 Jun 2024 16:08:07 GMT"
},
{
"version": "v3",
"created": "Mon, 3 Mar 2025 22:38:01 GMT"
}
] | 2025-03-05T00:00:00 | [
[
"Lin",
"Li",
""
],
[
"Santosh",
"",
""
],
[
"Wu",
"Mingyang",
""
],
[
"Wang",
"Xin",
""
],
[
"Hu",
"Shu",
""
]
] | TITLE: AI-Face: A Million-Scale Demographically Annotated AI-Generated Face
Dataset and Fairness Benchmark
ABSTRACT: AI-generated faces have enriched human life, such as entertainment,
education, and art. However, they also pose misuse risks. Therefore, detecting
AI-generated faces becomes crucial, yet current de... |
2406.04412 | Jaehyung Kim | Dongyoung Kim, Kimin Lee, Jinwoo Shin, Jaehyung Kim | Spread Preference Annotation: Direct Preference Judgment for Efficient
LLM Alignment | ICLR 2025 Oral Presentation, 22 pages | null | null | null | cs.LG cs.AI cs.CL | http://creativecommons.org/licenses/by/4.0/ | Aligning large language models (LLMs) with human preferences becomes a key
component to obtaining state-of-the-art performance, but it yields a huge cost
to construct a large human-annotated preference dataset. To tackle this
problem, we propose a new framework, Spread Preference Annotation with direct
preference jud... | [
{
"version": "v1",
"created": "Thu, 6 Jun 2024 18:01:02 GMT"
},
{
"version": "v2",
"created": "Tue, 4 Mar 2025 00:04:24 GMT"
}
] | 2025-03-05T00:00:00 | [
[
"Kim",
"Dongyoung",
""
],
[
"Lee",
"Kimin",
""
],
[
"Shin",
"Jinwoo",
""
],
[
"Kim",
"Jaehyung",
""
]
] | TITLE: Spread Preference Annotation: Direct Preference Judgment for Efficient
LLM Alignment
ABSTRACT: Aligning large language models (LLMs) with human preferences becomes a key
component to obtaining state-of-the-art performance, but it yields a huge cost
to construct a large human-annotated preference dataset. T... |
2406.06419 | Ramses Sanchez | David Berghaus, Kostadin Cvejoski, Patrick Seifner, Cesar Ojeda,
Ramses J. Sanchez | Foundation Inference Models for Markov Jump Processes | null | null | null | null | cs.LG stat.ML | http://creativecommons.org/licenses/by/4.0/ | Markov jump processes are continuous-time stochastic processes which describe
dynamical systems evolving in discrete state spaces. These processes find wide
application in the natural sciences and machine learning, but their inference
is known to be far from trivial. In this work we introduce a methodology for
zero-s... | [
{
"version": "v1",
"created": "Mon, 10 Jun 2024 16:12:00 GMT"
},
{
"version": "v2",
"created": "Fri, 4 Oct 2024 08:16:30 GMT"
},
{
"version": "v3",
"created": "Mon, 3 Mar 2025 11:26:33 GMT"
}
] | 2025-03-05T00:00:00 | [
[
"Berghaus",
"David",
""
],
[
"Cvejoski",
"Kostadin",
""
],
[
"Seifner",
"Patrick",
""
],
[
"Ojeda",
"Cesar",
""
],
[
"Sanchez",
"Ramses J.",
""
]
] | TITLE: Foundation Inference Models for Markov Jump Processes
ABSTRACT: Markov jump processes are continuous-time stochastic processes which describe
dynamical systems evolving in discrete state spaces. These processes find wide
application in the natural sciences and machine learning, but their inference
is known t... |
2406.15044 | Adnan Ali | Adnan Ali, Jinlong Li, Huanhuan Chen, Ali Kashif Bashir | From Overfitting to Robustness: Quantity, Quality, and Variety Oriented
Negative Sample Selection in Graph Contrastive Learning | null | null | null | null | cs.LG cs.AI | http://creativecommons.org/licenses/by/4.0/ | Graph contrastive learning (GCL) aims to contrast positive-negative
counterparts to learn the node embeddings, whereas graph data augmentation
methods are employed to generate these positive-negative samples. The
variation, quantity, and quality of negative samples compared to positive
samples play crucial roles in l... | [
{
"version": "v1",
"created": "Fri, 21 Jun 2024 10:47:26 GMT"
}
] | 2025-03-05T00:00:00 | [
[
"Ali",
"Adnan",
""
],
[
"Li",
"Jinlong",
""
],
[
"Chen",
"Huanhuan",
""
],
[
"Bashir",
"Ali Kashif",
""
]
] | TITLE: From Overfitting to Robustness: Quantity, Quality, and Variety Oriented
Negative Sample Selection in Graph Contrastive Learning
ABSTRACT: Graph contrastive learning (GCL) aims to contrast positive-negative
counterparts to learn the node embeddings, whereas graph data augmentation
methods are employed to ge... |
2406.16135 | Chulin Xie | Lynn Chua, Badih Ghazi, Yangsibo Huang, Pritish Kamath, Ravi Kumar,
Pasin Manurangsi, Amer Sinha, Chulin Xie, Chiyuan Zhang | Crosslingual Capabilities and Knowledge Barriers in Multilingual Large
Language Models | null | null | null | null | cs.CL cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Large language models (LLMs) are typically multilingual due to pretraining on
diverse multilingual corpora. But can these models relate corresponding
concepts across languages, i.e., be crosslingual? This study evaluates
state-of-the-art LLMs on inherently crosslingual tasks. We observe that while
these models show p... | [
{
"version": "v1",
"created": "Sun, 23 Jun 2024 15:15:17 GMT"
},
{
"version": "v2",
"created": "Tue, 4 Mar 2025 07:00:10 GMT"
}
] | 2025-03-05T00:00:00 | [
[
"Chua",
"Lynn",
""
],
[
"Ghazi",
"Badih",
""
],
[
"Huang",
"Yangsibo",
""
],
[
"Kamath",
"Pritish",
""
],
[
"Kumar",
"Ravi",
""
],
[
"Manurangsi",
"Pasin",
""
],
[
"Sinha",
"Amer",
""
],
[
"Xie",
... | TITLE: Crosslingual Capabilities and Knowledge Barriers in Multilingual Large
Language Models
ABSTRACT: Large language models (LLMs) are typically multilingual due to pretraining on
diverse multilingual corpora. But can these models relate corresponding
concepts across languages, i.e., be crosslingual? This study... |
2406.16783 | Vikas Yadav | Rishabh Maheshwary and Vikas Yadav and Hoang Nguyen and Khyati Mahajan
and Sathwik Tejaswi Madhusudhan | M2Lingual: Enhancing Multilingual, Multi-Turn Instruction Alignment in
Large Language Models | 39 pages | null | null | null | cs.CL cs.AI cs.LG | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Instruction finetuning (IFT) is critical for aligning Large Language Models
(LLMs) to follow instructions. While many effective IFT datasets have been
introduced recently, they predominantly focus on high-resource languages like
English. To better align LLMs across a broad spectrum of languages and tasks,
we propose ... | [
{
"version": "v1",
"created": "Mon, 24 Jun 2024 16:45:13 GMT"
},
{
"version": "v2",
"created": "Fri, 28 Jun 2024 10:14:53 GMT"
},
{
"version": "v3",
"created": "Tue, 4 Mar 2025 07:56:00 GMT"
}
] | 2025-03-05T00:00:00 | [
[
"Maheshwary",
"Rishabh",
""
],
[
"Yadav",
"Vikas",
""
],
[
"Nguyen",
"Hoang",
""
],
[
"Mahajan",
"Khyati",
""
],
[
"Madhusudhan",
"Sathwik Tejaswi",
""
]
] | TITLE: M2Lingual: Enhancing Multilingual, Multi-Turn Instruction Alignment in
Large Language Models
ABSTRACT: Instruction finetuning (IFT) is critical for aligning Large Language Models
(LLMs) to follow instructions. While many effective IFT datasets have been
introduced recently, they predominantly focus on high... |
2407.01574 | Gabriel Ducrocq | Gabriel Ducrocq, Lukas Grunewald, Sebastian Westenhoff, Fredrik
Lindsten | cryoSPHERE: Single-particle heterogeneous reconstruction from cryo EM | null | International Conference on Learning Representations (ICLR), 2025 | null | null | q-bio.BM cs.LG | http://creativecommons.org/licenses/by/4.0/ | The three-dimensional structure of proteins plays a crucial role in
determining their function. Protein structure prediction methods, like
AlphaFold, offer rapid access to a protein structure. However, large protein
complexes cannot be reliably predicted, and proteins are dynamic, making it
important to resolve their... | [
{
"version": "v1",
"created": "Wed, 29 May 2024 15:12:19 GMT"
},
{
"version": "v2",
"created": "Tue, 4 Mar 2025 06:16:45 GMT"
}
] | 2025-03-05T00:00:00 | [
[
"Ducrocq",
"Gabriel",
""
],
[
"Grunewald",
"Lukas",
""
],
[
"Westenhoff",
"Sebastian",
""
],
[
"Lindsten",
"Fredrik",
""
]
] | TITLE: cryoSPHERE: Single-particle heterogeneous reconstruction from cryo EM
ABSTRACT: The three-dimensional structure of proteins plays a crucial role in
determining their function. Protein structure prediction methods, like
AlphaFold, offer rapid access to a protein structure. However, large protein
complexes can... |
2407.03153 | MingYu Lu | Chris Lin, Mingyu Lu, Chanwoo Kim, Su-In Lee | An Efficient Framework for Crediting Data Contributors of Diffusion
Models | null | null | null | null | cs.LG cs.CV | http://creativecommons.org/licenses/by/4.0/ | As diffusion models are deployed in real-world settings, and their
performance is driven by training data, appraising the contribution of data
contributors is crucial to creating incentives for sharing quality data and to
implementing policies for data compensation. Depending on the use case, model
performance corres... | [
{
"version": "v1",
"created": "Sun, 9 Jun 2024 17:42:09 GMT"
},
{
"version": "v2",
"created": "Wed, 22 Jan 2025 18:21:13 GMT"
},
{
"version": "v3",
"created": "Mon, 3 Mar 2025 19:46:45 GMT"
}
] | 2025-03-05T00:00:00 | [
[
"Lin",
"Chris",
""
],
[
"Lu",
"Mingyu",
""
],
[
"Kim",
"Chanwoo",
""
],
[
"Lee",
"Su-In",
""
]
] | TITLE: An Efficient Framework for Crediting Data Contributors of Diffusion
Models
ABSTRACT: As diffusion models are deployed in real-world settings, and their
performance is driven by training data, appraising the contribution of data
contributors is crucial to creating incentives for sharing quality data and to
... |
2407.03157 | Zhenyu He | Zhenyu He, Jun Zhang, Shengjie Luo, Jingjing Xu, Zhi Zhang, Di He | Let the Code LLM Edit Itself When You Edit the Code | ICLR 2025 Camera Ready | null | null | null | cs.CL cs.AI cs.LG cs.SE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this work, we investigate a typical scenario in code generation where a
developer edits existing code in real time and requests a code assistant, e.g.,
a large language model, to re-predict the next token or next line on the fly.
Naively, the LLM needs to re-encode the entire KV cache to provide an accurate
predic... | [
{
"version": "v1",
"created": "Wed, 3 Jul 2024 14:34:03 GMT"
},
{
"version": "v2",
"created": "Tue, 4 Mar 2025 13:01:07 GMT"
}
] | 2025-03-05T00:00:00 | [
[
"He",
"Zhenyu",
""
],
[
"Zhang",
"Jun",
""
],
[
"Luo",
"Shengjie",
""
],
[
"Xu",
"Jingjing",
""
],
[
"Zhang",
"Zhi",
""
],
[
"He",
"Di",
""
]
] | TITLE: Let the Code LLM Edit Itself When You Edit the Code
ABSTRACT: In this work, we investigate a typical scenario in code generation where a
developer edits existing code in real time and requests a code assistant, e.g.,
a large language model, to re-predict the next token or next line on the fly.
Naively, the L... |
2408.01262 | Kunlun Zhu | Kunlun Zhu, Yifan Luo, Dingling Xu, Yukun Yan, Zhenghao Liu, Shi Yu,
Ruobing Wang, Shuo Wang, Yishan Li, Nan Zhang, Xu Han, Zhiyuan Liu, Maosong
Sun | RAGEval: Scenario Specific RAG Evaluation Dataset Generation Framework | https://github.com/OpenBMB/RAGEval | null | null | null | cs.CL cs.IR | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Retrieval-Augmented Generation (RAG) is a powerful approach that enables
large language models (LLMs) to incorporate external knowledge. However,
evaluating the effectiveness of RAG systems in specialized scenarios remains
challenging due to the high costs of data construction and the lack of suitable
evaluation metr... | [
{
"version": "v1",
"created": "Fri, 2 Aug 2024 13:35:11 GMT"
},
{
"version": "v2",
"created": "Sun, 18 Aug 2024 15:48:02 GMT"
},
{
"version": "v3",
"created": "Tue, 27 Aug 2024 03:13:50 GMT"
},
{
"version": "v4",
"created": "Thu, 17 Oct 2024 02:20:47 GMT"
},
{
"ve... | 2025-03-05T00:00:00 | [
[
"Zhu",
"Kunlun",
""
],
[
"Luo",
"Yifan",
""
],
[
"Xu",
"Dingling",
""
],
[
"Yan",
"Yukun",
""
],
[
"Liu",
"Zhenghao",
""
],
[
"Yu",
"Shi",
""
],
[
"Wang",
"Ruobing",
""
],
[
"Wang",
"Shuo",
... | TITLE: RAGEval: Scenario Specific RAG Evaluation Dataset Generation Framework
ABSTRACT: Retrieval-Augmented Generation (RAG) is a powerful approach that enables
large language models (LLMs) to incorporate external knowledge. However,
evaluating the effectiveness of RAG systems in specialized scenarios remains
chall... |
2408.12136 | Weiqin Chen | Weiqin Chen, Sandipan Mishra and Santiago Paternain | Domain Adaptation for Offline Reinforcement Learning with Limited
Samples | null | null | null | null | cs.LG stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Offline reinforcement learning (RL) learns effective policies from a static
target dataset. The performance of state-of-the-art offline RL algorithms
notwithstanding, it relies on the quality and size of the target dataset and it
degrades if limited samples in the target dataset are available, which is often
the case... | [
{
"version": "v1",
"created": "Thu, 22 Aug 2024 05:38:48 GMT"
},
{
"version": "v2",
"created": "Tue, 5 Nov 2024 21:28:34 GMT"
},
{
"version": "v3",
"created": "Tue, 4 Mar 2025 05:21:05 GMT"
}
] | 2025-03-05T00:00:00 | [
[
"Chen",
"Weiqin",
""
],
[
"Mishra",
"Sandipan",
""
],
[
"Paternain",
"Santiago",
""
]
] | TITLE: Domain Adaptation for Offline Reinforcement Learning with Limited
Samples
ABSTRACT: Offline reinforcement learning (RL) learns effective policies from a static
target dataset. The performance of state-of-the-art offline RL algorithms
notwithstanding, it relies on the quality and size of the target dataset ... |
2408.14608 | Lazar Atanackovic | Lazar Atanackovic, Xi Zhang, Brandon Amos, Mathieu Blanchette, Leo J.
Lee, Yoshua Bengio, Alexander Tong, Kirill Neklyudov | Meta Flow Matching: Integrating Vector Fields on the Wasserstein
Manifold | Accepted to ICLR 2025 | null | null | null | cs.LG stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Numerous biological and physical processes can be modeled as systems of
interacting entities evolving continuously over time, e.g. the dynamics of
communicating cells or physical particles. Learning the dynamics of such
systems is essential for predicting the temporal evolution of populations
across novel samples and... | [
{
"version": "v1",
"created": "Mon, 26 Aug 2024 20:05:31 GMT"
},
{
"version": "v2",
"created": "Mon, 3 Mar 2025 23:31:16 GMT"
}
] | 2025-03-05T00:00:00 | [
[
"Atanackovic",
"Lazar",
""
],
[
"Zhang",
"Xi",
""
],
[
"Amos",
"Brandon",
""
],
[
"Blanchette",
"Mathieu",
""
],
[
"Lee",
"Leo J.",
""
],
[
"Bengio",
"Yoshua",
""
],
[
"Tong",
"Alexander",
""
],
[
"... | TITLE: Meta Flow Matching: Integrating Vector Fields on the Wasserstein
Manifold
ABSTRACT: Numerous biological and physical processes can be modeled as systems of
interacting entities evolving continuously over time, e.g. the dynamics of
communicating cells or physical particles. Learning the dynamics of such
sys... |
2408.14769 | Yixuan Huang | Yixuan Huang, Christopher Agia, Jimmy Wu, Tucker Hermans, Jeannette
Bohg | Points2Plans: From Point Clouds to Long-Horizon Plans with Composable
Relational Dynamics | Project page: https://sites.google.com/stanford.edu/points2plans. 23
pages, 11 figures. Accepted to the IEEE International Conference on Robotics
and Automation (ICRA) 2025 | null | null | null | cs.RO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present Points2Plans, a framework for composable planning with a
relational dynamics model that enables robots to solve long-horizon
manipulation tasks from partial-view point clouds. Given a language instruction
and a point cloud of the scene, our framework initiates a hierarchical planning
procedure, whereby a l... | [
{
"version": "v1",
"created": "Tue, 27 Aug 2024 04:10:22 GMT"
},
{
"version": "v2",
"created": "Tue, 4 Mar 2025 02:53:51 GMT"
}
] | 2025-03-05T00:00:00 | [
[
"Huang",
"Yixuan",
""
],
[
"Agia",
"Christopher",
""
],
[
"Wu",
"Jimmy",
""
],
[
"Hermans",
"Tucker",
""
],
[
"Bohg",
"Jeannette",
""
]
] | TITLE: Points2Plans: From Point Clouds to Long-Horizon Plans with Composable
Relational Dynamics
ABSTRACT: We present Points2Plans, a framework for composable planning with a
relational dynamics model that enables robots to solve long-horizon
manipulation tasks from partial-view point clouds. Given a language ins... |
2408.16498 | Zhengran Zeng | Liguo Chen, Qi Guo, Hongrui Jia, Zhengran Zeng, Xin Wang, Yijiang Xu,
Jian Wu, Yidong Wang, Qing Gao, Jindong Wang, Wei Ye, Shikun Zhang | A Survey on Evaluating Large Language Models in Code Generation Tasks | null | null | null | null | cs.SE | http://creativecommons.org/licenses/by/4.0/ | This paper provides a comprehensive review of the current methods and metrics
used to evaluate the performance of Large Language Models (LLMs) in code
generation tasks. With the rapid growth in demand for automated software
development, LLMs have demonstrated significant potential in the field of code
generation. The... | [
{
"version": "v1",
"created": "Thu, 29 Aug 2024 12:56:06 GMT"
},
{
"version": "v2",
"created": "Tue, 4 Mar 2025 09:13:23 GMT"
}
] | 2025-03-05T00:00:00 | [
[
"Chen",
"Liguo",
""
],
[
"Guo",
"Qi",
""
],
[
"Jia",
"Hongrui",
""
],
[
"Zeng",
"Zhengran",
""
],
[
"Wang",
"Xin",
""
],
[
"Xu",
"Yijiang",
""
],
[
"Wu",
"Jian",
""
],
[
"Wang",
"Yidong",
""... | TITLE: A Survey on Evaluating Large Language Models in Code Generation Tasks
ABSTRACT: This paper provides a comprehensive review of the current methods and metrics
used to evaluate the performance of Large Language Models (LLMs) in code
generation tasks. With the rapid growth in demand for automated software
devel... |
2409.01115 | Gildas Morvan | Mouhamadou Mansour Lo, Gildas Morvan, Mathieu Rossi, Fabrice Morganti,
David Mercier | Time series classification with random convolution kernels: pooling
operators and input representations matter | v1: initial version, incorrect evaluation. v2: Method improved,
evaluation corrected, title simplified | null | null | null | cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This article presents a new approach based on MiniRocket, called SelF-Rocket,
for fast time series classification (TSC). Unlike existing approaches based on
random convolution kernels, it dynamically selects the best couple of input
representations and pooling operator during the training process. SelF-Rocket
achieve... | [
{
"version": "v1",
"created": "Mon, 2 Sep 2024 09:42:17 GMT"
},
{
"version": "v2",
"created": "Tue, 4 Mar 2025 07:52:43 GMT"
}
] | 2025-03-05T00:00:00 | [
[
"Lo",
"Mouhamadou Mansour",
""
],
[
"Morvan",
"Gildas",
""
],
[
"Rossi",
"Mathieu",
""
],
[
"Morganti",
"Fabrice",
""
],
[
"Mercier",
"David",
""
]
] | TITLE: Time series classification with random convolution kernels: pooling
operators and input representations matter
ABSTRACT: This article presents a new approach based on MiniRocket, called SelF-Rocket,
for fast time series classification (TSC). Unlike existing approaches based on
random convolution kernels, i... |
2409.01348 | Guanglei Zhou | Guanglei Zhou, Bhargav Korrapati, Gaurav Rajavendra Reddy, Chen-Chia
Chang, Jingyu Pan, Jiang Hu, Yiran Chen and Dipto G. Thakurta | PatternPaint: Practical Layout Pattern Generation Using Diffusion-Based
Inpainting | null | null | null | null | cs.CV cs.CE cs.LG | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Generating diverse VLSI layout patterns is essential for various downstream
tasks in design for manufacturing, as design rules continually evolve during
the development of new technology nodes. However, existing training-based
methods for layout pattern generation rely on large datasets. In practical
scenarios, espec... | [
{
"version": "v1",
"created": "Mon, 2 Sep 2024 16:02:26 GMT"
},
{
"version": "v2",
"created": "Fri, 25 Oct 2024 23:24:03 GMT"
},
{
"version": "v3",
"created": "Tue, 4 Mar 2025 05:29:33 GMT"
}
] | 2025-03-05T00:00:00 | [
[
"Zhou",
"Guanglei",
""
],
[
"Korrapati",
"Bhargav",
""
],
[
"Reddy",
"Gaurav Rajavendra",
""
],
[
"Chang",
"Chen-Chia",
""
],
[
"Pan",
"Jingyu",
""
],
[
"Hu",
"Jiang",
""
],
[
"Chen",
"Yiran",
""
],
[
... | TITLE: PatternPaint: Practical Layout Pattern Generation Using Diffusion-Based
Inpainting
ABSTRACT: Generating diverse VLSI layout patterns is essential for various downstream
tasks in design for manufacturing, as design rules continually evolve during
the development of new technology nodes. However, existing tr... |
2409.04429 | Yecheng Wu | Yecheng Wu, Zhuoyang Zhang, Junyu Chen, Haotian Tang, Dacheng Li,
Yunhao Fang, Ligeng Zhu, Enze Xie, Hongxu Yin, Li Yi, Song Han, Yao Lu | VILA-U: a Unified Foundation Model Integrating Visual Understanding and
Generation | Code: https://github.com/mit-han-lab/vila-u. The first two authors
contributed equally to this work | null | null | null | cs.CV cs.LG | http://creativecommons.org/licenses/by/4.0/ | VILA-U is a Unified foundation model that integrates Video, Image, Language
understanding and generation. Traditional visual language models (VLMs) use
separate modules for understanding and generating visual content, which can
lead to misalignment and increased complexity. In contrast, VILA-U employs a
single autore... | [
{
"version": "v1",
"created": "Fri, 6 Sep 2024 17:49:56 GMT"
},
{
"version": "v2",
"created": "Wed, 23 Oct 2024 16:42:06 GMT"
},
{
"version": "v3",
"created": "Tue, 4 Mar 2025 16:31:57 GMT"
}
] | 2025-03-05T00:00:00 | [
[
"Wu",
"Yecheng",
""
],
[
"Zhang",
"Zhuoyang",
""
],
[
"Chen",
"Junyu",
""
],
[
"Tang",
"Haotian",
""
],
[
"Li",
"Dacheng",
""
],
[
"Fang",
"Yunhao",
""
],
[
"Zhu",
"Ligeng",
""
],
[
"Xie",
"Enze... | TITLE: VILA-U: a Unified Foundation Model Integrating Visual Understanding and
Generation
ABSTRACT: VILA-U is a Unified foundation model that integrates Video, Image, Language
understanding and generation. Traditional visual language models (VLMs) use
separate modules for understanding and generating visual conte... |
2409.06948 | Anbo Tao | Anbo Tao, Yarong Luo, Chunxi Xia, Chi Guo and Xingxing Li | Equivariant Filter for Tightly Coupled LiDAR-Inertial Odometry | Accepted by ICRA 2025 | null | null | null | cs.RO cs.SY eess.SY | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Pose estimation is a crucial problem in simultaneous localization and mapping
(SLAM). However, developing a robust and consistent state estimator remains a
significant challenge, as the traditional extended Kalman filter (EKF)
struggles to handle the model nonlinearity, especially for inertial measurement
unit (IMU) ... | [
{
"version": "v1",
"created": "Wed, 11 Sep 2024 02:00:54 GMT"
},
{
"version": "v2",
"created": "Tue, 4 Mar 2025 10:38:01 GMT"
}
] | 2025-03-05T00:00:00 | [
[
"Tao",
"Anbo",
""
],
[
"Luo",
"Yarong",
""
],
[
"Xia",
"Chunxi",
""
],
[
"Guo",
"Chi",
""
],
[
"Li",
"Xingxing",
""
]
] | TITLE: Equivariant Filter for Tightly Coupled LiDAR-Inertial Odometry
ABSTRACT: Pose estimation is a crucial problem in simultaneous localization and mapping
(SLAM). However, developing a robust and consistent state estimator remains a
significant challenge, as the traditional extended Kalman filter (EKF)
struggles... |
2409.10095 | Huy-Dung Nguyen | Huy-Dung Nguyen, Anass Bairouk, Mirjana Maras, Wei Xiao, Tsun-Hsuan
Wang, Patrick Chareyre, Ramin Hasani, Marc Blanchon, Daniela Rus | Human Insights Driven Latent Space for Different Driving Perspectives: A
Unified Encoder for Efficient Multi-Task Inference | null | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Autonomous driving systems require a comprehensive understanding of the
environment, achieved by extracting visual features essential for perception,
planning, and control. However, models trained solely on single-task objectives
or generic datasets often lack the contextual information needed for robust
performance ... | [
{
"version": "v1",
"created": "Mon, 16 Sep 2024 08:54:03 GMT"
},
{
"version": "v2",
"created": "Tue, 4 Mar 2025 09:35:01 GMT"
}
] | 2025-03-05T00:00:00 | [
[
"Nguyen",
"Huy-Dung",
""
],
[
"Bairouk",
"Anass",
""
],
[
"Maras",
"Mirjana",
""
],
[
"Xiao",
"Wei",
""
],
[
"Wang",
"Tsun-Hsuan",
""
],
[
"Chareyre",
"Patrick",
""
],
[
"Hasani",
"Ramin",
""
],
[
"... | TITLE: Human Insights Driven Latent Space for Different Driving Perspectives: A
Unified Encoder for Efficient Multi-Task Inference
ABSTRACT: Autonomous driving systems require a comprehensive understanding of the
environment, achieved by extracting visual features essential for perception,
planning, and control. ... |
2409.13112 | Mostafa Rahimi Azghadi | Adrian Langley, Matthew Lonergan, Tao Huang, Mostafa Rahimi Azghadi | Analyzing mixed construction and demolition waste in material recovery
facilities: evolution, challenges, and applications of computer vision and
deep learning | null | Resources, Conservation and Recycling Volume 217, May 2025, 108218 | 10.1016/j.resconrec.2025.108218 | null | cs.CV cs.AI | http://creativecommons.org/licenses/by/4.0/ | Improving the automatic and timely recognition of construction and demolition
waste composition is crucial for enhancing business returns, economic outcomes
and sustainability. While deep learning models show promise in recognizing and
classifying homogenous materials, the current literature lacks research
assessing ... | [
{
"version": "v1",
"created": "Thu, 19 Sep 2024 22:38:26 GMT"
},
{
"version": "v2",
"created": "Mon, 3 Mar 2025 20:48:28 GMT"
}
] | 2025-03-05T00:00:00 | [
[
"Langley",
"Adrian",
""
],
[
"Lonergan",
"Matthew",
""
],
[
"Huang",
"Tao",
""
],
[
"Azghadi",
"Mostafa Rahimi",
""
]
] | TITLE: Analyzing mixed construction and demolition waste in material recovery
facilities: evolution, challenges, and applications of computer vision and
deep learning
ABSTRACT: Improving the automatic and timely recognition of construction and demolition
waste composition is crucial for enhancing business retur... |
2409.14623 | Clementine Domine | Cl\'ementine C. J. Domin\'e, and Nicolas Anguita, and Alexandra M.
Proca, and Lukas Braun, and Daniel Kunin, and Pedro A. M. Mediano, and Andrew
M. Saxe | From Lazy to Rich: Exact Learning Dynamics in Deep Linear Networks | 10 pages, 8 figures | null | null | null | cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Biological and artificial neural networks develop internal representations
that enable them to perform complex tasks. In artificial networks, the
effectiveness of these models relies on their ability to build task specific
representation, a process influenced by interactions among datasets,
architectures, initializat... | [
{
"version": "v1",
"created": "Sun, 22 Sep 2024 23:19:04 GMT"
},
{
"version": "v2",
"created": "Tue, 4 Mar 2025 11:18:33 GMT"
}
] | 2025-03-05T00:00:00 | [
[
"Dominé",
"Clémentine C. J.",
""
],
[
"Anguita",
"Nicolas",
""
],
[
"Proca",
"Alexandra M.",
""
],
[
"Braun",
"Lukas",
""
],
[
"Kunin",
"Daniel",
""
],
[
"Mediano",
"Pedro A. M.",
""
],
[
"Saxe",
"Andrew M.",
... | TITLE: From Lazy to Rich: Exact Learning Dynamics in Deep Linear Networks
ABSTRACT: Biological and artificial neural networks develop internal representations
that enable them to perform complex tasks. In artificial networks, the
effectiveness of these models relies on their ability to build task specific
represent... |
2409.15374 | Suryansh Vidya | Suryansh Vidya, Kush Gupta, Amir Aly, Andy Wills, Emmanuel Ifeachor
and Rohit Shankar | Explainable AI for Autism Diagnosis: Identifying Critical Brain Regions
Using fMRI Data | This work has been submitted to the IEEE for possible publication | null | null | null | eess.IV cs.AI cs.CV cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Early diagnosis and intervention for Autism Spectrum Disorder (ASD) has been
shown to significantly improve the quality of life of autistic individuals.
However, diagnostics methods for ASD rely on assessments based on clinical
presentation that are prone to bias and can be challenging to arrive at an
early diagnosis... | [
{
"version": "v1",
"created": "Thu, 19 Sep 2024 23:08:09 GMT"
},
{
"version": "v2",
"created": "Tue, 4 Mar 2025 00:46:19 GMT"
}
] | 2025-03-05T00:00:00 | [
[
"Vidya",
"Suryansh",
""
],
[
"Gupta",
"Kush",
""
],
[
"Aly",
"Amir",
""
],
[
"Wills",
"Andy",
""
],
[
"Ifeachor",
"Emmanuel",
""
],
[
"Shankar",
"Rohit",
""
]
] | TITLE: Explainable AI for Autism Diagnosis: Identifying Critical Brain Regions
Using fMRI Data
ABSTRACT: Early diagnosis and intervention for Autism Spectrum Disorder (ASD) has been
shown to significantly improve the quality of life of autistic individuals.
However, diagnostics methods for ASD rely on assessments... |
2409.16850 | Chun-Jung Lin | Chun-Jung Lin, Sourav Garg, Tat-Jun Chin, Feras Dayoub | Robust Scene Change Detection Using Visual Foundation Models and
Cross-Attention Mechanisms | 7 pages | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present a novel method for scene change detection that leverages the
robust feature extraction capabilities of a visual foundational model, DINOv2,
and integrates full-image cross-attention to address key challenges such as
varying lighting, seasonal variations, and viewpoint differences. In order to
effectively l... | [
{
"version": "v1",
"created": "Wed, 25 Sep 2024 11:55:27 GMT"
},
{
"version": "v2",
"created": "Wed, 5 Feb 2025 06:25:58 GMT"
},
{
"version": "v3",
"created": "Tue, 4 Mar 2025 02:16:30 GMT"
}
] | 2025-03-05T00:00:00 | [
[
"Lin",
"Chun-Jung",
""
],
[
"Garg",
"Sourav",
""
],
[
"Chin",
"Tat-Jun",
""
],
[
"Dayoub",
"Feras",
""
]
] | TITLE: Robust Scene Change Detection Using Visual Foundation Models and
Cross-Attention Mechanisms
ABSTRACT: We present a novel method for scene change detection that leverages the
robust feature extraction capabilities of a visual foundational model, DINOv2,
and integrates full-image cross-attention to address k... |
2409.20356 | Pablo Rodriguez-Grasa | Pablo Rodriguez-Grasa, Robert Farzan-Rodriguez, Gabriele Novelli, Yue
Ban, Mikel Sanz | Satellite image classification with neural quantum kernels | null | Machine Learning: Science and Technology, 6(1), 015043, 2025 | 10.1088/2632-2153/ada86c | null | quant-ph cs.LG | http://creativecommons.org/licenses/by/4.0/ | Achieving practical applications of quantum machine learning for real-world
scenarios remains challenging despite significant theoretical progress. This
paper proposes a novel approach for classifying satellite images, a task of
particular relevance to the earth observation (EO) industry, using quantum
machine learni... | [
{
"version": "v1",
"created": "Mon, 30 Sep 2024 14:52:00 GMT"
},
{
"version": "v2",
"created": "Tue, 4 Mar 2025 08:26:23 GMT"
}
] | 2025-03-05T00:00:00 | [
[
"Rodriguez-Grasa",
"Pablo",
""
],
[
"Farzan-Rodriguez",
"Robert",
""
],
[
"Novelli",
"Gabriele",
""
],
[
"Ban",
"Yue",
""
],
[
"Sanz",
"Mikel",
""
]
] | TITLE: Satellite image classification with neural quantum kernels
ABSTRACT: Achieving practical applications of quantum machine learning for real-world
scenarios remains challenging despite significant theoretical progress. This
paper proposes a novel approach for classifying satellite images, a task of
particular ... |
2410.00911 | Da-Wei Zhou | Da-Wei Zhou, Zi-Wen Cai, Han-Jia Ye, Lijun Zhang, De-Chuan Zhan | Dual Consolidation for Pre-Trained Model-Based Domain-Incremental
Learning | Accepted to CVPR 2025. Code is available at
https://github.com/Estrella-fugaz/CVPR25-Duct | null | null | null | cs.CV cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Domain-Incremental Learning (DIL) involves the progressive adaptation of a
model to new concepts across different domains. While recent advances in
pre-trained models provide a solid foundation for DIL, learning new concepts
often results in the catastrophic forgetting of pre-trained knowledge.
Specifically, sequenti... | [
{
"version": "v1",
"created": "Tue, 1 Oct 2024 17:58:06 GMT"
},
{
"version": "v2",
"created": "Tue, 4 Mar 2025 12:45:15 GMT"
}
] | 2025-03-05T00:00:00 | [
[
"Zhou",
"Da-Wei",
""
],
[
"Cai",
"Zi-Wen",
""
],
[
"Ye",
"Han-Jia",
""
],
[
"Zhang",
"Lijun",
""
],
[
"Zhan",
"De-Chuan",
""
]
] | TITLE: Dual Consolidation for Pre-Trained Model-Based Domain-Incremental
Learning
ABSTRACT: Domain-Incremental Learning (DIL) involves the progressive adaptation of a
model to new concepts across different domains. While recent advances in
pre-trained models provide a solid foundation for DIL, learning new concep... |
2410.02712 | Tianyi Xiong | Tianyi Xiong, Xiyao Wang, Dong Guo, Qinghao Ye, Haoqi Fan, Quanquan
Gu, Heng Huang, Chunyuan Li | LLaVA-Critic: Learning to Evaluate Multimodal Models | Accepted by CVPR 2025; Project Page:
https://llava-vl.github.io/blog/2024-10-03-llava-critic | null | null | null | cs.CV cs.CL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We introduce LLaVA-Critic, the first open-source large multimodal model (LMM)
designed as a generalist evaluator to assess performance across a wide range of
multimodal tasks. LLaVA-Critic is trained using a high-quality critic
instruction-following dataset that incorporates diverse evaluation criteria and
scenarios.... | [
{
"version": "v1",
"created": "Thu, 3 Oct 2024 17:36:33 GMT"
},
{
"version": "v2",
"created": "Tue, 4 Mar 2025 00:49:07 GMT"
}
] | 2025-03-05T00:00:00 | [
[
"Xiong",
"Tianyi",
""
],
[
"Wang",
"Xiyao",
""
],
[
"Guo",
"Dong",
""
],
[
"Ye",
"Qinghao",
""
],
[
"Fan",
"Haoqi",
""
],
[
"Gu",
"Quanquan",
""
],
[
"Huang",
"Heng",
""
],
[
"Li",
"Chunyuan",
... | TITLE: LLaVA-Critic: Learning to Evaluate Multimodal Models
ABSTRACT: We introduce LLaVA-Critic, the first open-source large multimodal model (LMM)
designed as a generalist evaluator to assess performance across a wide range of
multimodal tasks. LLaVA-Critic is trained using a high-quality critic
instruction-follow... |
2410.05472 | Andrey Grabovoy | Alidar Asvarov and Andrey Grabovoy | Neural machine translation system for Lezgian, Russian and Azerbaijani
languages | null | null | 10.1109/ISPRAS64596.2024.10899143 | null | cs.CL | http://creativecommons.org/licenses/by-sa/4.0/ | We release the first neural machine translation system for translation
between Russian, Azerbaijani and the endangered Lezgian languages, as well as
monolingual and parallel datasets collected and aligned for training and
evaluating the system. Multiple experiments are conducted to identify how
different sets of trai... | [
{
"version": "v1",
"created": "Mon, 7 Oct 2024 20:08:10 GMT"
}
] | 2025-03-05T00:00:00 | [
[
"Asvarov",
"Alidar",
""
],
[
"Grabovoy",
"Andrey",
""
]
] | TITLE: Neural machine translation system for Lezgian, Russian and Azerbaijani
languages
ABSTRACT: We release the first neural machine translation system for translation
between Russian, Azerbaijani and the endangered Lezgian languages, as well as
monolingual and parallel datasets collected and aligned for trainin... |
2410.05500 | Ray Congrui Yu | Ray Congrui Yu, Sherry Wu, Jiang Gui | Residual Kolmogorov-Arnold Network for Enhanced Deep Learning | Code is available at https://github.com/withray/residualKAN.git | null | null | null | cs.CV cs.AI cs.LG | http://creativecommons.org/licenses/by/4.0/ | Despite their immense success, deep neural networks (CNNs) are costly to
train, while modern architectures can retain hundreds of convolutional layers
in network depth. Standard convolutional operations are fundamentally limited
by their linear nature along with fixed activations, where multiple layers are
needed to ... | [
{
"version": "v1",
"created": "Mon, 7 Oct 2024 21:12:32 GMT"
},
{
"version": "v2",
"created": "Tue, 4 Mar 2025 06:34:37 GMT"
}
] | 2025-03-05T00:00:00 | [
[
"Yu",
"Ray Congrui",
""
],
[
"Wu",
"Sherry",
""
],
[
"Gui",
"Jiang",
""
]
] | TITLE: Residual Kolmogorov-Arnold Network for Enhanced Deep Learning
ABSTRACT: Despite their immense success, deep neural networks (CNNs) are costly to
train, while modern architectures can retain hundreds of convolutional layers
in network depth. Standard convolutional operations are fundamentally limited
by their... |
2410.09418 | Yi-Fan Lu | Yi-Fan Lu, Xian-Ling Mao, Tian Lan, Heyan Huang, Chen Xu, Xiaoyan Gao | Beyond Exact Match: Semantically Reassessing Event Extraction by Large
Language Models | null | null | null | null | cs.CL | http://creativecommons.org/licenses/by/4.0/ | Event extraction has gained extensive research attention due to its broad
range of applications. However, the current mainstream evaluation method for
event extraction relies on token-level exact match, which misjudges numerous
semantic-level correct cases. This reliance leads to a significant discrepancy
between the... | [
{
"version": "v1",
"created": "Sat, 12 Oct 2024 07:54:01 GMT"
},
{
"version": "v2",
"created": "Tue, 4 Mar 2025 07:06:43 GMT"
}
] | 2025-03-05T00:00:00 | [
[
"Lu",
"Yi-Fan",
""
],
[
"Mao",
"Xian-Ling",
""
],
[
"Lan",
"Tian",
""
],
[
"Huang",
"Heyan",
""
],
[
"Xu",
"Chen",
""
],
[
"Gao",
"Xiaoyan",
""
]
] | TITLE: Beyond Exact Match: Semantically Reassessing Event Extraction by Large
Language Models
ABSTRACT: Event extraction has gained extensive research attention due to its broad
range of applications. However, the current mainstream evaluation method for
event extraction relies on token-level exact match, which m... |
2410.11325 | Wenda Xu | Wenda Xu, Rujun Han, Zifeng Wang, Long T. Le, Dhruv Madeka, Lei Li,
William Yang Wang, Rishabh Agarwal, Chen-Yu Lee, Tomas Pfister | Speculative Knowledge Distillation: Bridging the Teacher-Student Gap
Through Interleaved Sampling | ICLR2025 | null | null | null | cs.CL cs.AI | http://creativecommons.org/licenses/by/4.0/ | Recent advances in knowledge distillation (KD) have enabled smaller student
models to approach the performance of larger teacher models. However, popular
methods such as supervised KD and on-policy KD, are adversely impacted by the
knowledge gaps between teacher-student in practical scenarios. Supervised KD
suffers f... | [
{
"version": "v1",
"created": "Tue, 15 Oct 2024 06:51:25 GMT"
},
{
"version": "v2",
"created": "Mon, 3 Mar 2025 19:24:41 GMT"
}
] | 2025-03-05T00:00:00 | [
[
"Xu",
"Wenda",
""
],
[
"Han",
"Rujun",
""
],
[
"Wang",
"Zifeng",
""
],
[
"Le",
"Long T.",
""
],
[
"Madeka",
"Dhruv",
""
],
[
"Li",
"Lei",
""
],
[
"Wang",
"William Yang",
""
],
[
"Agarwal",
"Rish... | TITLE: Speculative Knowledge Distillation: Bridging the Teacher-Student Gap
Through Interleaved Sampling
ABSTRACT: Recent advances in knowledge distillation (KD) have enabled smaller student
models to approach the performance of larger teacher models. However, popular
methods such as supervised KD and on-policy K... |
2410.11841 | Fei Tang | Fei Tang, Yongliang Shen, Hang Zhang, Zeqi Tan, Wenqi Zhang, Zhibiao
Huang, Kaitao Song, Weiming Lu, Yueting Zhuang | GaVaMoE: Gaussian-Variational Gated Mixture of Experts for Explainable
Recommendation | null | null | null | null | cs.IR cs.AI | http://creativecommons.org/licenses/by/4.0/ | Large language model-based explainable recommendation (LLM-based ER) systems
show promise in generating human-like explanations for recommendations.
However, they face challenges in modeling user-item collaborative preferences,
personalizing explanations, and handling sparse user-item interactions. To
address these i... | [
{
"version": "v1",
"created": "Tue, 15 Oct 2024 17:59:30 GMT"
},
{
"version": "v2",
"created": "Tue, 4 Mar 2025 01:02:11 GMT"
}
] | 2025-03-05T00:00:00 | [
[
"Tang",
"Fei",
""
],
[
"Shen",
"Yongliang",
""
],
[
"Zhang",
"Hang",
""
],
[
"Tan",
"Zeqi",
""
],
[
"Zhang",
"Wenqi",
""
],
[
"Huang",
"Zhibiao",
""
],
[
"Song",
"Kaitao",
""
],
[
"Lu",
"Weiming... | TITLE: GaVaMoE: Gaussian-Variational Gated Mixture of Experts for Explainable
Recommendation
ABSTRACT: Large language model-based explainable recommendation (LLM-based ER) systems
show promise in generating human-like explanations for recommendations.
However, they face challenges in modeling user-item collaborat... |
2410.12346 | Guanzhou Lan | Guanzhou Lan, Qianli Ma, Yuqi Yang, Zhigang Wang, Dong Wang, Xuelong
Li, Bin Zhao | Efficient Diffusion as Low Light Enhancer | 8 pages | null | null | null | cs.CV cs.AI | http://creativecommons.org/licenses/by/4.0/ | The computational burden of the iterative sampling process remains a major
challenge in diffusion-based Low-Light Image Enhancement (LLIE). Current
acceleration methods, whether training-based or training-free, often lead to
significant performance degradation, highlighting the trade-off between
performance and effic... | [
{
"version": "v1",
"created": "Wed, 16 Oct 2024 08:07:18 GMT"
},
{
"version": "v2",
"created": "Thu, 21 Nov 2024 08:20:04 GMT"
}
] | 2025-03-05T00:00:00 | [
[
"Lan",
"Guanzhou",
""
],
[
"Ma",
"Qianli",
""
],
[
"Yang",
"Yuqi",
""
],
[
"Wang",
"Zhigang",
""
],
[
"Wang",
"Dong",
""
],
[
"Li",
"Xuelong",
""
],
[
"Zhao",
"Bin",
""
]
] | TITLE: Efficient Diffusion as Low Light Enhancer
ABSTRACT: The computational burden of the iterative sampling process remains a major
challenge in diffusion-based Low-Light Image Enhancement (LLIE). Current
acceleration methods, whether training-based or training-free, often lead to
significant performance degradat... |
2410.14431 | Mourad Oulghelou | Mourad Oulghelou, Soufiane Cherroud, Xavier Merle, Paola Cinnella | Machine-learning-assisted Blending of Data-Driven Turbulence Models | null | null | null | null | physics.flu-dyn | http://creativecommons.org/licenses/by/4.0/ | We present a machine learning-based framework for blending data-driven
turbulent closures in the Reynolds-Averaged Navier-Stokes (RANS) equations,
aimed at improving their generalizability across diverse flow regimes.
Specialized models (hereafter referred to as experts) are trained via sparse
Bayesian learning and s... | [
{
"version": "v1",
"created": "Fri, 18 Oct 2024 12:50:20 GMT"
},
{
"version": "v2",
"created": "Mon, 3 Mar 2025 21:06:18 GMT"
}
] | 2025-03-05T00:00:00 | [
[
"Oulghelou",
"Mourad",
""
],
[
"Cherroud",
"Soufiane",
""
],
[
"Merle",
"Xavier",
""
],
[
"Cinnella",
"Paola",
""
]
] | TITLE: Machine-learning-assisted Blending of Data-Driven Turbulence Models
ABSTRACT: We present a machine learning-based framework for blending data-driven
turbulent closures in the Reynolds-Averaged Navier-Stokes (RANS) equations,
aimed at improving their generalizability across diverse flow regimes.
Specialized m... |
2410.23825 | Amir Hossein Kargaran | Amir Hossein Kargaran, Fran\c{c}ois Yvon, Hinrich Sch\"utze | GlotCC: An Open Broad-Coverage CommonCrawl Corpus and Pipeline for
Minority Languages | NeurIPS 2024 | null | null | null | cs.CL cs.AI | http://creativecommons.org/licenses/by/4.0/ | The need for large text corpora has increased with the advent of pretrained
language models and, in particular, the discovery of scaling laws for these
models. Most available corpora have sufficient data only for languages with
large dominant communities. However, there is no corpus available that (i)
covers a wide r... | [
{
"version": "v1",
"created": "Thu, 31 Oct 2024 11:14:12 GMT"
},
{
"version": "v2",
"created": "Mon, 3 Mar 2025 21:51:52 GMT"
}
] | 2025-03-05T00:00:00 | [
[
"Kargaran",
"Amir Hossein",
""
],
[
"Yvon",
"François",
""
],
[
"Schütze",
"Hinrich",
""
]
] | TITLE: GlotCC: An Open Broad-Coverage CommonCrawl Corpus and Pipeline for
Minority Languages
ABSTRACT: The need for large text corpora has increased with the advent of pretrained
language models and, in particular, the discovery of scaling laws for these
models. Most available corpora have sufficient data only fo... |
2411.00476 | Ren Xin | Ren Xin, Jie Cheng, Hongji Liu, Jun Ma | PlanScope: Learning to Plan Within Decision Scope Does Matter | null | null | null | null | cs.RO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In the context of autonomous driving, learning-based methods have been
promising for the development of planning modules. During the training process
of planning modules, directly minimizing the discrepancy between expert-driving
logs and planning output is widely deployed. In general, driving logs consist
of suddenl... | [
{
"version": "v1",
"created": "Fri, 1 Nov 2024 09:43:49 GMT"
},
{
"version": "v2",
"created": "Tue, 4 Mar 2025 09:44:08 GMT"
}
] | 2025-03-05T00:00:00 | [
[
"Xin",
"Ren",
""
],
[
"Cheng",
"Jie",
""
],
[
"Liu",
"Hongji",
""
],
[
"Ma",
"Jun",
""
]
] | TITLE: PlanScope: Learning to Plan Within Decision Scope Does Matter
ABSTRACT: In the context of autonomous driving, learning-based methods have been
promising for the development of planning modules. During the training process
of planning modules, directly minimizing the discrepancy between expert-driving
logs an... |
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