Search is not available for this dataset
id string | submitter string | authors string | title string | comments string | journal-ref string | doi string | report-no string | categories string | license string | abstract string | versions list | update_date timestamp[s] | authors_parsed list | prompt string |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2504.06962 | Thomas Kerdreux | Thomas Kerdreux and Alexandre Tuel and Quentin Febvre and Alexis
Mouche and Bertrand Chapron | Efficient Self-Supervised Learning for Earth Observation via Dynamic
Dataset Curation | Accepted at CVPR Workshop : The First Workshop on Foundation and
Large Vision Models in Remote Sensing | null | null | null | cs.CV cs.AI | http://creativecommons.org/licenses/by/4.0/ | Self-supervised learning (SSL) has enabled the development of vision
foundation models for Earth Observation (EO), demonstrating strong
transferability across diverse remote sensing tasks. While prior work has
focused on network architectures and training strategies, the role of dataset
curation, especially in balanc... | [
{
"version": "v1",
"created": "Wed, 9 Apr 2025 15:13:26 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Kerdreux",
"Thomas",
""
],
[
"Tuel",
"Alexandre",
""
],
[
"Febvre",
"Quentin",
""
],
[
"Mouche",
"Alexis",
""
],
[
"Chapron",
"Bertrand",
""
]
] | TITLE: Efficient Self-Supervised Learning for Earth Observation via Dynamic
Dataset Curation
ABSTRACT: Self-supervised learning (SSL) has enabled the development of vision
foundation models for Earth Observation (EO), demonstrating strong
transferability across diverse remote sensing tasks. While prior work has
f... |
2504.06963 | Vladimir Bataev | Vladimir Bataev | RNN-Transducer-based Losses for Speech Recognition on Noisy Targets | Final Project Report, Bachelor's Degree in Computer Science,
University of London, March 2024 | null | null | null | eess.AS cs.AI cs.CL cs.LG cs.SD | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Training speech recognition systems on noisy transcripts is a significant
challenge in industrial pipelines, where datasets are enormous and ensuring
accurate transcription for every instance is difficult. In this work, we
introduce novel loss functions to mitigate the impact of transcription errors
in RNN-Transducer... | [
{
"version": "v1",
"created": "Wed, 9 Apr 2025 15:18:29 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Bataev",
"Vladimir",
""
]
] | TITLE: RNN-Transducer-based Losses for Speech Recognition on Noisy Targets
ABSTRACT: Training speech recognition systems on noisy transcripts is a significant
challenge in industrial pipelines, where datasets are enormous and ensuring
accurate transcription for every instance is difficult. In this work, we
introduc... |
2504.06965 | Qingsong Yan | Teng Xiao, Qi Hu, Qingsong Yan, Wei Liu, Zhiwei Ye, Fei Deng | A Deep Single Image Rectification Approach for Pan-Tilt-Zoom Cameras | Accepted to ICME 2025 | null | null | null | cs.CV | http://creativecommons.org/licenses/by/4.0/ | Pan-Tilt-Zoom (PTZ) cameras with wide-angle lenses are widely used in
surveillance but often require image rectification due to their inherent
nonlinear distortions. Current deep learning approaches typically struggle to
maintain fine-grained geometric details, resulting in inaccurate rectification.
This paper presen... | [
{
"version": "v1",
"created": "Wed, 9 Apr 2025 15:19:38 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Xiao",
"Teng",
""
],
[
"Hu",
"Qi",
""
],
[
"Yan",
"Qingsong",
""
],
[
"Liu",
"Wei",
""
],
[
"Ye",
"Zhiwei",
""
],
[
"Deng",
"Fei",
""
]
] | TITLE: A Deep Single Image Rectification Approach for Pan-Tilt-Zoom Cameras
ABSTRACT: Pan-Tilt-Zoom (PTZ) cameras with wide-angle lenses are widely used in
surveillance but often require image rectification due to their inherent
nonlinear distortions. Current deep learning approaches typically struggle to
maintain ... |
2504.06969 | Lilian Ngweta | Lilian Ngweta, Kiran Kate, Jason Tsay, Yara Rizk | Towards LLMs Robustness to Changes in Prompt Format Styles | NAACL Student Research Workshop (SRW) 2025 | null | null | null | cs.CL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Large language models (LLMs) have gained popularity in recent years for their
utility in various applications. However, they are sensitive to non-semantic
changes in prompt formats, where small changes in the prompt format can lead to
significant performance fluctuations. In the literature, this problem is
commonly r... | [
{
"version": "v1",
"created": "Wed, 9 Apr 2025 15:26:00 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Ngweta",
"Lilian",
""
],
[
"Kate",
"Kiran",
""
],
[
"Tsay",
"Jason",
""
],
[
"Rizk",
"Yara",
""
]
] | TITLE: Towards LLMs Robustness to Changes in Prompt Format Styles
ABSTRACT: Large language models (LLMs) have gained popularity in recent years for their
utility in various applications. However, they are sensitive to non-semantic
changes in prompt formats, where small changes in the prompt format can lead to
signi... |
2504.06982 | Yuhang Yang | Yuhang Yang, Fengqi Liu, Yixing Lu, Qin Zhao, Pingyu Wu, Wei Zhai, Ran
Yi, Yang Cao, Lizhuang Ma, Zheng-Jun Zha, Junting Dong | SIGMAN:Scaling 3D Human Gaussian Generation with Millions of Assets | project page:https://yyvhang.github.io/SIGMAN_3D/ | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | 3D human digitization has long been a highly pursued yet challenging task.
Existing methods aim to generate high-quality 3D digital humans from single or
multiple views, but remain primarily constrained by current paradigms and the
scarcity of 3D human assets. Specifically, recent approaches fall into several
paradig... | [
{
"version": "v1",
"created": "Wed, 9 Apr 2025 15:38:18 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Yang",
"Yuhang",
""
],
[
"Liu",
"Fengqi",
""
],
[
"Lu",
"Yixing",
""
],
[
"Zhao",
"Qin",
""
],
[
"Wu",
"Pingyu",
""
],
[
"Zhai",
"Wei",
""
],
[
"Yi",
"Ran",
""
],
[
"Cao",
"Yang",
""
],
... | TITLE: SIGMAN:Scaling 3D Human Gaussian Generation with Millions of Assets
ABSTRACT: 3D human digitization has long been a highly pursued yet challenging task.
Existing methods aim to generate high-quality 3D digital humans from single or
multiple views, but remain primarily constrained by current paradigms and the... |
2504.06991 | Ghurumuruhan Ganesan | Ghurumuruhan Ganesan | Dissimilar Batch Decompositions of Random Datasets | Accepted for publication in Sankhya A | null | null | null | cs.LG math.PR stat.ML | http://creativecommons.org/licenses/by/4.0/ | For better learning, large datasets are often split into small batches and
fed sequentially to the predictive model. In this paper, we study such batch
decompositions from a probabilistic perspective. We assume that data points
(possibly corrupted) are drawn independently from a given space and define a
concept of si... | [
{
"version": "v1",
"created": "Wed, 9 Apr 2025 15:58:06 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Ganesan",
"Ghurumuruhan",
""
]
] | TITLE: Dissimilar Batch Decompositions of Random Datasets
ABSTRACT: For better learning, large datasets are often split into small batches and
fed sequentially to the predictive model. In this paper, we study such batch
decompositions from a probabilistic perspective. We assume that data points
(possibly corrupted)... |
2504.06997 | Mingliang Pan | Mingliang Pan, Chenxu Li, Yuanzhe Zhang, Alan Mollins, Quan Wang,
Ahmet T. Erdogan, Yuanyuan Hua, Zhenya Zang, Neil Finlayson, Robert K.
Henderson, David Day-Uei Li | Cerebral blood flow monitoring using a deep learning implementation of
the two-layer DCS analytical model with a 512 512 SPAD array | 23 pages, 11 figures | null | null | null | physics.med-ph physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Diffuse correlation spectroscopy (DCS) analyzes the autocorrelation function
of photons scattered by red blood cells, enabling non-invasive, continuous
measurement of deep tissue blood flow at the bedside. Multi-layer DCS models
(two- and three-layer) enhance cerebral blood flow index (CBFi) sensitivity and
mitigate ... | [
{
"version": "v1",
"created": "Wed, 9 Apr 2025 16:09:34 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Pan",
"Mingliang",
""
],
[
"Li",
"Chenxu",
""
],
[
"Zhang",
"Yuanzhe",
""
],
[
"Mollins",
"Alan",
""
],
[
"Wang",
"Quan",
""
],
[
"Erdogan",
"Ahmet T.",
""
],
[
"Hua",
"Yuanyuan",
""
],
[
"Zang",
... | TITLE: Cerebral blood flow monitoring using a deep learning implementation of
the two-layer DCS analytical model with a 512 512 SPAD array
ABSTRACT: Diffuse correlation spectroscopy (DCS) analyzes the autocorrelation function
of photons scattered by red blood cells, enabling non-invasive, continuous
measurement o... |
2504.07002 | Yuan Xiao | Yuan Xiao, Yuchen Chen, Shiqing Ma, Haocheng Huang, Chunrong Fang,
Yanwei Chen, Weisong Sun, Yunfeng Zhu, Xiaofang Zhang, Zhenyu Chen | DeCoMa: Detecting and Purifying Code Dataset Watermarks through Dual
Channel Code Abstraction | Accepted to ISSTA 2025. Code is available at
https://github.com/xiaoyuanpigo/DeCoMa | null | 10.1145/3728952 | null | cs.CR cs.SE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Watermarking is a technique to help identify the source of data points, which
can be used to help prevent the misuse of protected datasets. Existing methods
on code watermarking, leveraging the idea from the backdoor research, embed
stealthy triggers as watermarks.Despite their high resilience against dilution
attack... | [
{
"version": "v1",
"created": "Wed, 9 Apr 2025 16:19:11 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Xiao",
"Yuan",
""
],
[
"Chen",
"Yuchen",
""
],
[
"Ma",
"Shiqing",
""
],
[
"Huang",
"Haocheng",
""
],
[
"Fang",
"Chunrong",
""
],
[
"Chen",
"Yanwei",
""
],
[
"Sun",
"Weisong",
""
],
[
"Zhu",
"Yu... | TITLE: DeCoMa: Detecting and Purifying Code Dataset Watermarks through Dual
Channel Code Abstraction
ABSTRACT: Watermarking is a technique to help identify the source of data points, which
can be used to help prevent the misuse of protected datasets. Existing methods
on code watermarking, leveraging the idea from... |
2504.07017 | Yusuf Guven | Yusuf Guven, Tufan Kumbasar | Adapting GT2-FLS for Uncertainty Quantification: A Blueprint Calibration
Strategy | in IEEE International Conference on Fuzzy Systems, 2025 | null | null | null | cs.LG | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Uncertainty Quantification (UQ) is crucial for deploying reliable Deep
Learning (DL) models in high-stakes applications. Recently, General Type-2
Fuzzy Logic Systems (GT2-FLSs) have been proven to be effective for UQ,
offering Prediction Intervals (PIs) to capture uncertainty. However, existing
methods often struggle... | [
{
"version": "v1",
"created": "Wed, 9 Apr 2025 16:32:43 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Guven",
"Yusuf",
""
],
[
"Kumbasar",
"Tufan",
""
]
] | TITLE: Adapting GT2-FLS for Uncertainty Quantification: A Blueprint Calibration
Strategy
ABSTRACT: Uncertainty Quantification (UQ) is crucial for deploying reliable Deep
Learning (DL) models in high-stakes applications. Recently, General Type-2
Fuzzy Logic Systems (GT2-FLSs) have been proven to be effective for U... |
2504.07025 | Bojian Wu | Bojian Wu, Yifan Peng, Ruizhen Hu, Xiaowei Zhou | Glossy Object Reconstruction with Cost-effective Polarized Acquisition | Accepted to CVPR 2025 as highlight | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The challenge of image-based 3D reconstruction for glossy objects lies in
separating diffuse and specular components on glossy surfaces from captured
images, a task complicated by the ambiguity in discerning lighting conditions
and material properties using RGB data alone. While state-of-the-art methods
rely on tailo... | [
{
"version": "v1",
"created": "Wed, 9 Apr 2025 16:38:51 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Wu",
"Bojian",
""
],
[
"Peng",
"Yifan",
""
],
[
"Hu",
"Ruizhen",
""
],
[
"Zhou",
"Xiaowei",
""
]
] | TITLE: Glossy Object Reconstruction with Cost-effective Polarized Acquisition
ABSTRACT: The challenge of image-based 3D reconstruction for glossy objects lies in
separating diffuse and specular components on glossy surfaces from captured
images, a task complicated by the ambiguity in discerning lighting conditions
... |
2504.07031 | Pawel Pukowski | Pawel Pukowski and Venet Osmani | Identifying Key Challenges of Hardness-Based Resampling | Submitted to IEEE TPAMI | null | null | null | cs.LG | http://creativecommons.org/licenses/by/4.0/ | Performance gap across classes remains a persistent challenge in machine
learning, often attributed to variations in class hardness. One way to quantify
class hardness is through sample complexity - the minimum number of samples
required to effectively learn a given class. Sample complexity theory suggests
that class... | [
{
"version": "v1",
"created": "Wed, 9 Apr 2025 16:45:57 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Pukowski",
"Pawel",
""
],
[
"Osmani",
"Venet",
""
]
] | TITLE: Identifying Key Challenges of Hardness-Based Resampling
ABSTRACT: Performance gap across classes remains a persistent challenge in machine
learning, often attributed to variations in class hardness. One way to quantify
class hardness is through sample complexity - the minimum number of samples
required to ef... |
2504.07061 | Shi Pan | Shi Pan and Jianan Chen and Maria Secrier | Teaching pathology foundation models to accurately predict gene
expression with parameter efficient knowledge transfer | null | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Gene expression profiling provides critical insights into cellular
heterogeneity, biological processes and disease mechanisms. There has been an
increasing interest in computational approaches that can predict gene
expression directly from digitalized histopathology images. While image
foundation models have shown pr... | [
{
"version": "v1",
"created": "Wed, 9 Apr 2025 17:24:41 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Pan",
"Shi",
""
],
[
"Chen",
"Jianan",
""
],
[
"Secrier",
"Maria",
""
]
] | TITLE: Teaching pathology foundation models to accurately predict gene
expression with parameter efficient knowledge transfer
ABSTRACT: Gene expression profiling provides critical insights into cellular
heterogeneity, biological processes and disease mechanisms. There has been an
increasing interest in computatio... |
2504.07065 | William Simon | Riselda Kodra, Hadjer Benmeziane, Irem Boybat, William Andrew Simon | Enhancing Downstream Analysis in Genome Sequencing: Species
Classification While Basecalling | Accepted as Tiny Paper at MLGenX workshop, ICLR, 2025 | null | null | null | q-bio.GN cs.LG | http://creativecommons.org/licenses/by/4.0/ | The ability to quickly and accurately identify microbial species in a sample,
known as metagenomic profiling, is critical across various fields, from
healthcare to environmental science. This paper introduces a novel method to
profile signals coming from sequencing devices in parallel with determining
their nucleotid... | [
{
"version": "v1",
"created": "Wed, 9 Apr 2025 17:30:43 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Kodra",
"Riselda",
""
],
[
"Benmeziane",
"Hadjer",
""
],
[
"Boybat",
"Irem",
""
],
[
"Simon",
"William Andrew",
""
]
] | TITLE: Enhancing Downstream Analysis in Genome Sequencing: Species
Classification While Basecalling
ABSTRACT: The ability to quickly and accurately identify microbial species in a sample,
known as metagenomic profiling, is critical across various fields, from
healthcare to environmental science. This paper introd... |
2504.07069 | Bibek Paudel | Bibek Paudel, Alexander Lyzhov, Preetam Joshi, Puneet Anand | HalluciNot: Hallucination Detection Through Context and Common Knowledge
Verification | null | null | null | null | cs.CL cs.AI | http://creativecommons.org/licenses/by-sa/4.0/ | This paper introduces a comprehensive system for detecting hallucinations in
large language model (LLM) outputs in enterprise settings. We present a novel
taxonomy of LLM responses specific to hallucination in enterprise applications,
categorizing them into context-based, common knowledge, enterprise-specific,
and in... | [
{
"version": "v1",
"created": "Wed, 9 Apr 2025 17:39:41 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Paudel",
"Bibek",
""
],
[
"Lyzhov",
"Alexander",
""
],
[
"Joshi",
"Preetam",
""
],
[
"Anand",
"Puneet",
""
]
] | TITLE: HalluciNot: Hallucination Detection Through Context and Common Knowledge
Verification
ABSTRACT: This paper introduces a comprehensive system for detecting hallucinations in
large language model (LLM) outputs in enterprise settings. We present a novel
taxonomy of LLM responses specific to hallucination in e... |
2504.07072 | Desmond Elliott | Israfel Salazar, Manuel Fern\'andez Burda, Shayekh Bin Islam, Arshia
Soltani Moakhar, Shivalika Singh, Fabian Farestam, Angelika Romanou, Danylo
Boiko, Dipika Khullar, Mike Zhang, Dominik Krzemi\'nski, Jekaterina Novikova,
Lu\'isa Shimabucoro, Joseph Marvin Imperial, Rishabh Maheshwary, Sharad
Duwal, Alfonso Am... | Kaleidoscope: In-language Exams for Massively Multilingual Vision
Evaluation | null | null | null | null | cs.CL cs.CV | http://creativecommons.org/licenses/by/4.0/ | The evaluation of vision-language models (VLMs) has mainly relied on
English-language benchmarks, leaving significant gaps in both multilingual and
multicultural coverage. While multilingual benchmarks have expanded, both in
size and languages, many rely on translations of English datasets, failing to
capture cultura... | [
{
"version": "v1",
"created": "Wed, 9 Apr 2025 17:43:16 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Salazar",
"Israfel",
""
],
[
"Burda",
"Manuel Fernández",
""
],
[
"Islam",
"Shayekh Bin",
""
],
[
"Moakhar",
"Arshia Soltani",
""
],
[
"Singh",
"Shivalika",
""
],
[
"Farestam",
"Fabian",
""
],
[
"Romanou",
"An... | TITLE: Kaleidoscope: In-language Exams for Massively Multilingual Vision
Evaluation
ABSTRACT: The evaluation of vision-language models (VLMs) has mainly relied on
English-language benchmarks, leaving significant gaps in both multilingual and
multicultural coverage. While multilingual benchmarks have expanded, bot... |
2504.07080 | Atharva Pandey | Atharva Pandey, Kshitij Dubey, Rahul Sharma, Amit Sharma | DeduCE: Deductive Consistency as a Framework to Evaluate LLM Reasoning | null | null | null | null | cs.CL cs.AI cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Despite great performance on Olympiad-level reasoning problems, frontier
large language models can still struggle on high school math when presented
with novel problems outside standard benchmarks. Going beyond final accuracy,
we propose a deductive consistency metric to analyze chain-of-thought output
from language ... | [
{
"version": "v1",
"created": "Wed, 9 Apr 2025 17:53:55 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Pandey",
"Atharva",
""
],
[
"Dubey",
"Kshitij",
""
],
[
"Sharma",
"Rahul",
""
],
[
"Sharma",
"Amit",
""
]
] | TITLE: DeduCE: Deductive Consistency as a Framework to Evaluate LLM Reasoning
ABSTRACT: Despite great performance on Olympiad-level reasoning problems, frontier
large language models can still struggle on high school math when presented
with novel problems outside standard benchmarks. Going beyond final accuracy,
w... |
2504.07093 | Gene Chou | Gene Chou, Wenqi Xian, Guandao Yang, Mohamed Abdelfattah, Bharath
Hariharan, Noah Snavely, Ning Yu, Paul Debevec | FlashDepth: Real-time Streaming Video Depth Estimation at 2K Resolution | null | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A versatile video depth estimation model should (1) be accurate and
consistent across frames, (2) produce high-resolution depth maps, and (3)
support real-time streaming. We propose FlashDepth, a method that satisfies all
three requirements, performing depth estimation on a 2044x1148 streaming video
at 24 FPS. We sho... | [
{
"version": "v1",
"created": "Wed, 9 Apr 2025 17:59:31 GMT"
}
] | 2025-04-10T00:00:00 | [
[
"Chou",
"Gene",
""
],
[
"Xian",
"Wenqi",
""
],
[
"Yang",
"Guandao",
""
],
[
"Abdelfattah",
"Mohamed",
""
],
[
"Hariharan",
"Bharath",
""
],
[
"Snavely",
"Noah",
""
],
[
"Yu",
"Ning",
""
],
[
"Debeve... | TITLE: FlashDepth: Real-time Streaming Video Depth Estimation at 2K Resolution
ABSTRACT: A versatile video depth estimation model should (1) be accurate and
consistent across frames, (2) produce high-resolution depth maps, and (3)
support real-time streaming. We propose FlashDepth, a method that satisfies all
three... |
2110.03427 | Atanu Mandal | Atanu Mandal, Santanu Pal, Indranil Dutta, Mahidas Bhattacharya, Sudip
Kumar Naskar | Is Attention always needed? A Case Study on Language Identification from
Speech | Accepted for publication in Natural Language Engineering | Nat. lang. process. 31 (2025) 250-276 | 10.1017/nlp.2024.22 | null | cs.LG cs.CL cs.SD eess.AS eess.SP | http://creativecommons.org/licenses/by/4.0/ | Language Identification (LID) is a crucial preliminary process in the field
of Automatic Speech Recognition (ASR) that involves the identification of a
spoken language from audio samples. Contemporary systems that can process
speech in multiple languages require users to expressly designate one or more
languages prio... | [
{
"version": "v1",
"created": "Tue, 5 Oct 2021 16:38:57 GMT"
},
{
"version": "v2",
"created": "Sun, 10 Jul 2022 03:47:05 GMT"
},
{
"version": "v3",
"created": "Wed, 25 Oct 2023 15:21:08 GMT"
}
] | 2025-04-09T00:00:00 | [
[
"Mandal",
"Atanu",
""
],
[
"Pal",
"Santanu",
""
],
[
"Dutta",
"Indranil",
""
],
[
"Bhattacharya",
"Mahidas",
""
],
[
"Naskar",
"Sudip Kumar",
""
]
] | TITLE: Is Attention always needed? A Case Study on Language Identification from
Speech
ABSTRACT: Language Identification (LID) is a crucial preliminary process in the field
of Automatic Speech Recognition (ASR) that involves the identification of a
spoken language from audio samples. Contemporary systems that can... |
2111.13463 | Ivica Kostric | Ivica Kostric and Krisztian Balog and Filip Radlinski | Generating Usage-related Questions for Preference Elicitation in
Conversational Recommender Systems | Journal extension of our RecSys '21 paper titled "Soliciting User
Preferences in Conversational Recommender Systems via Usage-related
Questions." This version appears in ACM Transactions on Recommender Systems
(ToRS), 2(2), Article 12, April 2024, with expanded experiments and new
analysis | ACM Transactions on Recommender Systems (ToRS), Volume 2, Issue 2,
Article 12 (April 2024) | 10.1145/3629981 | null | cs.IR cs.AI cs.CL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A key distinguishing feature of conversational recommender systems over
traditional recommender systems is their ability to elicit user preferences
using natural language. Currently, the predominant approach to preference
elicitation is to ask questions directly about items or item attributes. Users
searching for rec... | [
{
"version": "v1",
"created": "Fri, 26 Nov 2021 12:23:14 GMT"
},
{
"version": "v2",
"created": "Tue, 8 Apr 2025 13:25:51 GMT"
}
] | 2025-04-09T00:00:00 | [
[
"Kostric",
"Ivica",
""
],
[
"Balog",
"Krisztian",
""
],
[
"Radlinski",
"Filip",
""
]
] | TITLE: Generating Usage-related Questions for Preference Elicitation in
Conversational Recommender Systems
ABSTRACT: A key distinguishing feature of conversational recommender systems over
traditional recommender systems is their ability to elicit user preferences
using natural language. Currently, the predominan... |
2210.15527 | Yun-Hin Chan | Yun-Hin Chan, Edith C.-H. Ngai | Exploiting Features and Logits in Heterogeneous Federated Learning | Accepted by Computer Networks | null | 10.1016/j.comnet.2025.111271 | null | cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Due to the rapid growth of IoT and artificial intelligence, deploying neural
networks on IoT devices is becoming increasingly crucial for edge intelligence.
Federated learning (FL) facilitates the management of edge devices to
collaboratively train a shared model while maintaining training data local and
private. How... | [
{
"version": "v1",
"created": "Thu, 27 Oct 2022 15:11:46 GMT"
},
{
"version": "v2",
"created": "Tue, 8 Apr 2025 09:54:58 GMT"
}
] | 2025-04-09T00:00:00 | [
[
"Chan",
"Yun-Hin",
""
],
[
"Ngai",
"Edith C. -H.",
""
]
] | TITLE: Exploiting Features and Logits in Heterogeneous Federated Learning
ABSTRACT: Due to the rapid growth of IoT and artificial intelligence, deploying neural
networks on IoT devices is becoming increasingly crucial for edge intelligence.
Federated learning (FL) facilitates the management of edge devices to
colla... |
2301.00539 | Sudhansu Bala Das | Sudhansu Bala Das, Divyajoti Panda, Tapas Kumar Mishra, Bidyut Kr.
Patra | Statistical Machine Translation for Indic Languages | 32pages, 1 figure, 4 tables | Nat. lang. process. 31 (2025) 328-345 | 10.1017/nlp.2024.26 | null | cs.CL | http://creativecommons.org/licenses/by/4.0/ | Machine Translation (MT) system generally aims at automatic representation of
source language into target language retaining the originality of context using
various Natural Language Processing (NLP) techniques. Among various NLP
methods, Statistical Machine Translation(SMT). SMT uses probabilistic and
statistical te... | [
{
"version": "v1",
"created": "Mon, 2 Jan 2023 06:23:12 GMT"
}
] | 2025-04-09T00:00:00 | [
[
"Das",
"Sudhansu Bala",
""
],
[
"Panda",
"Divyajoti",
""
],
[
"Mishra",
"Tapas Kumar",
""
],
[
"Patra",
"Bidyut Kr.",
""
]
] | TITLE: Statistical Machine Translation for Indic Languages
ABSTRACT: Machine Translation (MT) system generally aims at automatic representation of
source language into target language retaining the originality of context using
various Natural Language Processing (NLP) techniques. Among various NLP
methods, Statisti... |
2301.06650 | Lijun Sun Dr. | Vincent Zhihao Zheng, Seongjin Choi, Lijun Sun | Probabilistic Traffic Forecasting with Dynamic Regression | null | Probabilistic Traffic Forecasting with Dynamic Regression.
Transportation Science (2025) | 10.1287/trsc.2024.0560 | null | cs.LG stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper proposes a dynamic regression (DR) framework that enhances
existing deep spatiotemporal models by incorporating structured learning for
the error process in traffic forecasting. The framework relaxes the assumption
of time independence by modeling the error series of the base model (i.e., a
well-establishe... | [
{
"version": "v1",
"created": "Tue, 17 Jan 2023 01:12:44 GMT"
},
{
"version": "v2",
"created": "Fri, 31 May 2024 15:05:40 GMT"
},
{
"version": "v3",
"created": "Tue, 8 Apr 2025 14:26:10 GMT"
}
] | 2025-04-09T00:00:00 | [
[
"Zheng",
"Vincent Zhihao",
""
],
[
"Choi",
"Seongjin",
""
],
[
"Sun",
"Lijun",
""
]
] | TITLE: Probabilistic Traffic Forecasting with Dynamic Regression
ABSTRACT: This paper proposes a dynamic regression (DR) framework that enhances
existing deep spatiotemporal models by incorporating structured learning for
the error process in traffic forecasting. The framework relaxes the assumption
of time indepen... |
2305.15203 | Lorenzo Basile | Lorenzo Basile, Nikos Karantzas, Alberto d'Onofrio, Luca Manzoni, Luca
Bortolussi, Alex Rodriguez, Fabio Anselmi | Frequency maps reveal the correlation between Adversarial Attacks and
Implicit Bias | Accepted at IJCNN 2025 | null | null | null | cs.LG cs.AI cs.CR stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Despite their impressive performance in classification tasks, neural networks
are known to be vulnerable to adversarial attacks, subtle perturbations of the
input data designed to deceive the model. In this work, we investigate the
correlation between these perturbations and the implicit bias of neural
networks train... | [
{
"version": "v1",
"created": "Wed, 24 May 2023 14:40:23 GMT"
},
{
"version": "v2",
"created": "Wed, 17 Jul 2024 16:34:48 GMT"
},
{
"version": "v3",
"created": "Tue, 8 Apr 2025 14:29:39 GMT"
}
] | 2025-04-09T00:00:00 | [
[
"Basile",
"Lorenzo",
""
],
[
"Karantzas",
"Nikos",
""
],
[
"d'Onofrio",
"Alberto",
""
],
[
"Manzoni",
"Luca",
""
],
[
"Bortolussi",
"Luca",
""
],
[
"Rodriguez",
"Alex",
""
],
[
"Anselmi",
"Fabio",
""
]
] | TITLE: Frequency maps reveal the correlation between Adversarial Attacks and
Implicit Bias
ABSTRACT: Despite their impressive performance in classification tasks, neural networks
are known to be vulnerable to adversarial attacks, subtle perturbations of the
input data designed to deceive the model. In this work, ... |
2310.16810 | Yongxin Zhou | Yongxin Zhou, Fabien Ringeval, Fran\c{c}ois Portet | Can GPT models Follow Human Summarization Guidelines? A Study for
Targeted Communication Goals | null | null | null | null | cs.CL cs.AI | http://creativecommons.org/licenses/by-nc-sa/4.0/ | This study investigates the ability of GPT models (ChatGPT, GPT-4 and GPT-4o)
to generate dialogue summaries that adhere to human guidelines. Our evaluation
involved experimenting with various prompts to guide the models in complying
with guidelines on two datasets: DialogSum (English social conversations) and
DECODA... | [
{
"version": "v1",
"created": "Wed, 25 Oct 2023 17:39:07 GMT"
},
{
"version": "v2",
"created": "Mon, 7 Apr 2025 21:42:15 GMT"
}
] | 2025-04-09T00:00:00 | [
[
"Zhou",
"Yongxin",
""
],
[
"Ringeval",
"Fabien",
""
],
[
"Portet",
"François",
""
]
] | TITLE: Can GPT models Follow Human Summarization Guidelines? A Study for
Targeted Communication Goals
ABSTRACT: This study investigates the ability of GPT models (ChatGPT, GPT-4 and GPT-4o)
to generate dialogue summaries that adhere to human guidelines. Our evaluation
involved experimenting with various prompts t... |
2311.01759 | Jianlei Yang | Jianlei Yang, Jiacheng Liao, Fanding Lei, Meichen Liu, Junyi Chen,
Lingkun Long, Han Wan, Bei Yu, Weisheng Zhao | TinyFormer: Efficient Transformer Design and Deployment on Tiny Devices | This work has been submitted to the IEEE for possible publication | null | null | null | cs.LG cs.AR | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Developing deep learning models on tiny devices (e.g. Microcontroller units,
MCUs) has attracted much attention in various embedded IoT applications.
However, it is challenging to efficiently design and deploy recent advanced
models (e.g. transformers) on tiny devices due to their severe hardware
resource constraints... | [
{
"version": "v1",
"created": "Fri, 3 Nov 2023 07:34:47 GMT"
},
{
"version": "v2",
"created": "Tue, 8 Apr 2025 11:42:15 GMT"
}
] | 2025-04-09T00:00:00 | [
[
"Yang",
"Jianlei",
""
],
[
"Liao",
"Jiacheng",
""
],
[
"Lei",
"Fanding",
""
],
[
"Liu",
"Meichen",
""
],
[
"Chen",
"Junyi",
""
],
[
"Long",
"Lingkun",
""
],
[
"Wan",
"Han",
""
],
[
"Yu",
"Bei",
... | TITLE: TinyFormer: Efficient Transformer Design and Deployment on Tiny Devices
ABSTRACT: Developing deep learning models on tiny devices (e.g. Microcontroller units,
MCUs) has attracted much attention in various embedded IoT applications.
However, it is challenging to efficiently design and deploy recent advanced
m... |
2311.18681 | Chantal Pellegrini | Chantal Pellegrini, Ege \"Ozsoy, Benjamin Busam, Nassir Navab,
Matthias Keicher | RaDialog: A Large Vision-Language Model for Radiology Report Generation
and Conversational Assistance | improved version accepted at MIDL 2025:
https://openreview.net/pdf?id=trUvr1gSNI | null | null | null | cs.CV cs.CL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Conversational AI tools that can generate and discuss clinically correct
radiology reports for a given medical image have the potential to transform
radiology. Such a human-in-the-loop radiology assistant could facilitate a
collaborative diagnostic process, thus saving time and improving the quality of
reports. Towar... | [
{
"version": "v1",
"created": "Thu, 30 Nov 2023 16:28:40 GMT"
},
{
"version": "v2",
"created": "Tue, 8 Apr 2025 07:32:34 GMT"
}
] | 2025-04-09T00:00:00 | [
[
"Pellegrini",
"Chantal",
""
],
[
"Özsoy",
"Ege",
""
],
[
"Busam",
"Benjamin",
""
],
[
"Navab",
"Nassir",
""
],
[
"Keicher",
"Matthias",
""
]
] | TITLE: RaDialog: A Large Vision-Language Model for Radiology Report Generation
and Conversational Assistance
ABSTRACT: Conversational AI tools that can generate and discuss clinically correct
radiology reports for a given medical image have the potential to transform
radiology. Such a human-in-the-loop radiology ... |
2312.16379 | Alexey Melnikov | Asel Sagingalieva, Stefan Komornyik, Ayush Joshi, Christopher Mansell,
Karan Pinto, Markus Pflitsch, and Alexey Melnikov | Photovoltaic power forecasting using quantum machine learning | 12 pages, 4 figures, 1 table | null | null | null | cs.LG cs.ET quant-ph | http://creativecommons.org/licenses/by/4.0/ | Predicting solar panel power output is crucial for advancing the transition
to renewable energy but is complicated by the variable and non-linear nature of
solar energy. This is influenced by numerous meteorological factors,
geographical positioning, and photovoltaic cell properties, posing significant
challenges to ... | [
{
"version": "v1",
"created": "Wed, 27 Dec 2023 02:37:46 GMT"
},
{
"version": "v2",
"created": "Mon, 7 Apr 2025 22:55:21 GMT"
}
] | 2025-04-09T00:00:00 | [
[
"Sagingalieva",
"Asel",
""
],
[
"Komornyik",
"Stefan",
""
],
[
"Joshi",
"Ayush",
""
],
[
"Mansell",
"Christopher",
""
],
[
"Pinto",
"Karan",
""
],
[
"Pflitsch",
"Markus",
""
],
[
"Melnikov",
"Alexey",
""
... | TITLE: Photovoltaic power forecasting using quantum machine learning
ABSTRACT: Predicting solar panel power output is crucial for advancing the transition
to renewable energy but is complicated by the variable and non-linear nature of
solar energy. This is influenced by numerous meteorological factors,
geographical... |
2402.04051 | Akira Ito | Akira Ito, Masanori Yamada, Atsutoshi Kumagai | Analysis of Linear Mode Connectivity via Permutation-Based Weight
Matching: With Insights into Other Permutation Search Methods | In Proceedings of the Thirteenth International Conference on Learning
Representations (ICLR 2025) | null | null | null | cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recently, Ainsworth et al. showed that using weight matching (WM) to minimize
the $L^2$ distance in a permutation search of model parameters effectively
identifies permutations that satisfy linear mode connectivity (LMC), where the
loss along a linear path between two independently trained models with
different seeds... | [
{
"version": "v1",
"created": "Tue, 6 Feb 2024 14:53:28 GMT"
},
{
"version": "v2",
"created": "Mon, 19 Feb 2024 10:36:25 GMT"
},
{
"version": "v3",
"created": "Mon, 15 Apr 2024 05:57:26 GMT"
},
{
"version": "v4",
"created": "Thu, 3 Oct 2024 11:36:28 GMT"
},
{
"ver... | 2025-04-09T00:00:00 | [
[
"Ito",
"Akira",
""
],
[
"Yamada",
"Masanori",
""
],
[
"Kumagai",
"Atsutoshi",
""
]
] | TITLE: Analysis of Linear Mode Connectivity via Permutation-Based Weight
Matching: With Insights into Other Permutation Search Methods
ABSTRACT: Recently, Ainsworth et al. showed that using weight matching (WM) to minimize
the $L^2$ distance in a permutation search of model parameters effectively
identifies permu... |
2403.02437 | Hyejun Jeong | Hyejun Jeong, Shiqing Ma, Amir Houmansadr | A Survey on Federated Unlearning: Challenges and Opportunities | null | null | null | null | cs.LG cs.AI cs.DC | http://creativecommons.org/licenses/by/4.0/ | Federated learning (FL), introduced in 2017, facilitates collaborative
learning between non-trusting parties with no need for the parties to
explicitly share their data among themselves. This allows training models on
user data while respecting privacy regulations such as GDPR and CPRA. However,
emerging privacy requ... | [
{
"version": "v1",
"created": "Mon, 4 Mar 2024 19:35:08 GMT"
},
{
"version": "v2",
"created": "Wed, 5 Jun 2024 19:00:03 GMT"
},
{
"version": "v3",
"created": "Mon, 7 Apr 2025 19:55:57 GMT"
}
] | 2025-04-09T00:00:00 | [
[
"Jeong",
"Hyejun",
""
],
[
"Ma",
"Shiqing",
""
],
[
"Houmansadr",
"Amir",
""
]
] | TITLE: A Survey on Federated Unlearning: Challenges and Opportunities
ABSTRACT: Federated learning (FL), introduced in 2017, facilitates collaborative
learning between non-trusting parties with no need for the parties to
explicitly share their data among themselves. This allows training models on
user data while re... |
2404.03543 | JiaWei Guo | Jiawei Guo, Ziming Li, Xueling Liu, Kaijing Ma, Tianyu Zheng,
Zhouliang Yu, Ding Pan, Yizhi LI, Ruibo Liu, Yue Wang, Shuyue Guo, Xingwei
Qu, Xiang Yue, Ge Zhang, Wenhu Chen, Jie Fu | CodeEditorBench: Evaluating Code Editing Capability of Large Language
Models | null | null | null | null | cs.SE cs.AI cs.CL cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Large Language Models (LLMs) for code are rapidly evolving, with code editing
emerging as a critical capability. We introduce CodeEditorBench, an evaluation
framework designed to rigorously assess the performance of LLMs in code editing
tasks, including debugging, translating, polishing, and requirement switching.
Un... | [
{
"version": "v1",
"created": "Thu, 4 Apr 2024 15:49:49 GMT"
},
{
"version": "v2",
"created": "Sat, 6 Apr 2024 04:29:25 GMT"
},
{
"version": "v3",
"created": "Tue, 8 Apr 2025 09:39:25 GMT"
}
] | 2025-04-09T00:00:00 | [
[
"Guo",
"Jiawei",
""
],
[
"Li",
"Ziming",
""
],
[
"Liu",
"Xueling",
""
],
[
"Ma",
"Kaijing",
""
],
[
"Zheng",
"Tianyu",
""
],
[
"Yu",
"Zhouliang",
""
],
[
"Pan",
"Ding",
""
],
[
"LI",
"Yizhi",
... | TITLE: CodeEditorBench: Evaluating Code Editing Capability of Large Language
Models
ABSTRACT: Large Language Models (LLMs) for code are rapidly evolving, with code editing
emerging as a critical capability. We introduce CodeEditorBench, an evaluation
framework designed to rigorously assess the performance of LLMs... |
2405.10577 | Yizhe Zhao | Zhe Huang, Yizhe Zhao, Hao Xiao, Chenyan Wu, Lingting Ge | DuoSpaceNet: Leveraging Both Bird's-Eye-View and Perspective View
Representations for 3D Object Detection | CVPR 2025 Workshop on Autonomous Driving (WAD) | null | null | null | cs.CV cs.RO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Multi-view camera-only 3D object detection largely follows two primary
paradigms: exploiting bird's-eye-view (BEV) representations or focusing on
perspective-view (PV) features, each with distinct advantages. Although several
recent approaches explore combining BEV and PV, many rely on partial fusion or
maintain sepa... | [
{
"version": "v1",
"created": "Fri, 17 May 2024 07:04:29 GMT"
},
{
"version": "v2",
"created": "Thu, 29 Aug 2024 02:09:11 GMT"
},
{
"version": "v3",
"created": "Mon, 7 Apr 2025 18:00:17 GMT"
}
] | 2025-04-09T00:00:00 | [
[
"Huang",
"Zhe",
""
],
[
"Zhao",
"Yizhe",
""
],
[
"Xiao",
"Hao",
""
],
[
"Wu",
"Chenyan",
""
],
[
"Ge",
"Lingting",
""
]
] | TITLE: DuoSpaceNet: Leveraging Both Bird's-Eye-View and Perspective View
Representations for 3D Object Detection
ABSTRACT: Multi-view camera-only 3D object detection largely follows two primary
paradigms: exploiting bird's-eye-view (BEV) representations or focusing on
perspective-view (PV) features, each with dis... |
2405.13955 | Xiaoshan Zhou | Xiaoshan Zhou, Carol C. Menassa, and Vineet R. Kamat | Decoding Brain Dynamics in Motor Planning Based on EEG Microstates for
Predicting Pedestrian Road-Crossing in Vehicle-to-Everything Architectures | 38 pages, 11 figures | null | null | null | cs.HC cs.ET | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Pedestrians who cross roads, often emerge from occlusion or abruptly begin
crossing from a standstill, frequently leading to unintended collisions with
vehicular traffic that result in accidents and interruptions. Existing studies
have predominantly relied on external network sensing and observational data to
anticip... | [
{
"version": "v1",
"created": "Wed, 22 May 2024 19:40:37 GMT"
},
{
"version": "v2",
"created": "Mon, 7 Apr 2025 19:58:30 GMT"
}
] | 2025-04-09T00:00:00 | [
[
"Zhou",
"Xiaoshan",
""
],
[
"Menassa",
"Carol C.",
""
],
[
"Kamat",
"Vineet R.",
""
]
] | TITLE: Decoding Brain Dynamics in Motor Planning Based on EEG Microstates for
Predicting Pedestrian Road-Crossing in Vehicle-to-Everything Architectures
ABSTRACT: Pedestrians who cross roads, often emerge from occlusion or abruptly begin
crossing from a standstill, frequently leading to unintended collisions with... |
2405.13983 | Anton Morgunov | Yu Shee, Anton Morgunov, Haote Li, Victor S. Batista | DirectMultiStep: Direct Route Generation for Multistep Retrosynthesis | null | null | 10.1021/acs.jcim.4c01982 | null | cs.LG cs.AI | http://creativecommons.org/licenses/by/4.0/ | Traditional computer-aided synthesis planning (CASP) methods rely on
iterative single-step predictions, leading to exponential search space growth
that limits efficiency and scalability. We introduce a series of
transformer-based models, that leverage a mixture of experts approach to
directly generate multistep synth... | [
{
"version": "v1",
"created": "Wed, 22 May 2024 20:39:05 GMT"
},
{
"version": "v2",
"created": "Tue, 21 Jan 2025 17:37:07 GMT"
},
{
"version": "v3",
"created": "Thu, 20 Mar 2025 01:58:12 GMT"
}
] | 2025-04-09T00:00:00 | [
[
"Shee",
"Yu",
""
],
[
"Morgunov",
"Anton",
""
],
[
"Li",
"Haote",
""
],
[
"Batista",
"Victor S.",
""
]
] | TITLE: DirectMultiStep: Direct Route Generation for Multistep Retrosynthesis
ABSTRACT: Traditional computer-aided synthesis planning (CASP) methods rely on
iterative single-step predictions, leading to exponential search space growth
that limits efficiency and scalability. We introduce a series of
transformer-based... |
2405.20445 | Jianan Zhao | Jianan Zhao, Zhaocheng Zhu, Mikhail Galkin, Hesham Mostafa, Michael
Bronstein, Jian Tang | Fully-inductive Node Classification on Arbitrary Graphs | ICLR2025 | null | null | null | cs.LG cs.SI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | One fundamental challenge in graph machine learning is generalizing to new
graphs. Many existing methods following the inductive setup can generalize to
test graphs with new structures, but assuming the feature and label spaces
remain the same as the training ones. This paper introduces a fully-inductive
setup, where... | [
{
"version": "v1",
"created": "Thu, 30 May 2024 19:43:29 GMT"
},
{
"version": "v2",
"created": "Mon, 3 Jun 2024 02:08:54 GMT"
},
{
"version": "v3",
"created": "Sun, 9 Feb 2025 03:14:20 GMT"
},
{
"version": "v4",
"created": "Fri, 28 Feb 2025 00:56:45 GMT"
},
{
"ver... | 2025-04-09T00:00:00 | [
[
"Zhao",
"Jianan",
""
],
[
"Zhu",
"Zhaocheng",
""
],
[
"Galkin",
"Mikhail",
""
],
[
"Mostafa",
"Hesham",
""
],
[
"Bronstein",
"Michael",
""
],
[
"Tang",
"Jian",
""
]
] | TITLE: Fully-inductive Node Classification on Arbitrary Graphs
ABSTRACT: One fundamental challenge in graph machine learning is generalizing to new
graphs. Many existing methods following the inductive setup can generalize to
test graphs with new structures, but assuming the feature and label spaces
remain the same... |
2405.20769 | Matthew Regehr | Christian Janos Lebeda, Matthew Regehr, Gautam Kamath, Thomas Steinke | Avoiding Pitfalls for Privacy Accounting of Subsampled Mechanisms under
Composition | null | null | null | null | cs.CR cs.DS cs.LG stat.ML | http://creativecommons.org/licenses/by/4.0/ | We consider the problem of computing tight privacy guarantees for the
composition of subsampled differentially private mechanisms. Recent algorithms
can numerically compute the privacy parameters to arbitrary precision but must
be carefully applied.
Our main contribution is to address two common points of confusion... | [
{
"version": "v1",
"created": "Mon, 27 May 2024 20:30:12 GMT"
},
{
"version": "v2",
"created": "Tue, 8 Apr 2025 15:21:03 GMT"
}
] | 2025-04-09T00:00:00 | [
[
"Lebeda",
"Christian Janos",
""
],
[
"Regehr",
"Matthew",
""
],
[
"Kamath",
"Gautam",
""
],
[
"Steinke",
"Thomas",
""
]
] | TITLE: Avoiding Pitfalls for Privacy Accounting of Subsampled Mechanisms under
Composition
ABSTRACT: We consider the problem of computing tight privacy guarantees for the
composition of subsampled differentially private mechanisms. Recent algorithms
can numerically compute the privacy parameters to arbitrary prec... |
2406.00984 | Hiroaki Yamagiwa | Hiroaki Yamagiwa, Ryoma Hashimoto, Kiwamu Arakane, Ken Murakami, Shou
Soeda, Momose Oyama, Yihua Zhu, Mariko Okada, Hidetoshi Shimodaira | Predicting Drug-Gene Relations via Analogy Tasks with Word Embeddings | null | null | null | null | cs.CL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Natural language processing (NLP) is utilized in a wide range of fields,
where words in text are typically transformed into feature vectors called
embeddings. BioConceptVec is a specific example of embeddings tailored for
biology, trained on approximately 30 million PubMed abstracts using models such
as skip-gram. Ge... | [
{
"version": "v1",
"created": "Mon, 3 Jun 2024 04:36:38 GMT"
},
{
"version": "v2",
"created": "Wed, 4 Sep 2024 20:22:41 GMT"
},
{
"version": "v3",
"created": "Sun, 8 Dec 2024 09:03:03 GMT"
},
{
"version": "v4",
"created": "Tue, 8 Apr 2025 17:50:27 GMT"
}
] | 2025-04-09T00:00:00 | [
[
"Yamagiwa",
"Hiroaki",
""
],
[
"Hashimoto",
"Ryoma",
""
],
[
"Arakane",
"Kiwamu",
""
],
[
"Murakami",
"Ken",
""
],
[
"Soeda",
"Shou",
""
],
[
"Oyama",
"Momose",
""
],
[
"Zhu",
"Yihua",
""
],
[
"Okad... | TITLE: Predicting Drug-Gene Relations via Analogy Tasks with Word Embeddings
ABSTRACT: Natural language processing (NLP) is utilized in a wide range of fields,
where words in text are typically transformed into feature vectors called
embeddings. BioConceptVec is a specific example of embeddings tailored for
biology... |
2406.07467 | Fatemeh Hadadi | Fatemeh Hadadi, Qinghua Xu, Domenico Bianculli, Lionel Briand | LLM meets ML: Data-efficient Anomaly Detection on Unseen Unstable Logs | null | null | null | null | cs.SE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Most log-based anomaly detectors assume logs are stable, though logs are
often unstable due to software or environmental changes. Anomaly detection on
unstable logs (ULAD) is therefore a more realistic, yet under-investigated
challenge. Current approaches predominantly employ machine learning (ML)
models, which often... | [
{
"version": "v1",
"created": "Tue, 11 Jun 2024 17:13:18 GMT"
},
{
"version": "v2",
"created": "Mon, 7 Apr 2025 20:52:04 GMT"
}
] | 2025-04-09T00:00:00 | [
[
"Hadadi",
"Fatemeh",
""
],
[
"Xu",
"Qinghua",
""
],
[
"Bianculli",
"Domenico",
""
],
[
"Briand",
"Lionel",
""
]
] | TITLE: LLM meets ML: Data-efficient Anomaly Detection on Unseen Unstable Logs
ABSTRACT: Most log-based anomaly detectors assume logs are stable, though logs are
often unstable due to software or environmental changes. Anomaly detection on
unstable logs (ULAD) is therefore a more realistic, yet under-investigated
ch... |
2406.08092 | Zhi Qu | Zhi Qu, Chenchen Ding, Taro Watanabe | Languages Transferred Within the Encoder: On Representation Transfer in
Zero-Shot Multilingual Translation | Accepted by MT Summit 2025 | null | null | null | cs.CL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Understanding representation transfer in multilingual neural machine
translation (MNMT) can reveal the reason for the zero-shot translation
deficiency. In this work, we systematically analyze the representational issue
of MNMT models. We first introduce the identity pair, translating a sentence to
itself, to address ... | [
{
"version": "v1",
"created": "Wed, 12 Jun 2024 11:16:30 GMT"
},
{
"version": "v2",
"created": "Tue, 8 Apr 2025 03:39:51 GMT"
}
] | 2025-04-09T00:00:00 | [
[
"Qu",
"Zhi",
""
],
[
"Ding",
"Chenchen",
""
],
[
"Watanabe",
"Taro",
""
]
] | TITLE: Languages Transferred Within the Encoder: On Representation Transfer in
Zero-Shot Multilingual Translation
ABSTRACT: Understanding representation transfer in multilingual neural machine
translation (MNMT) can reveal the reason for the zero-shot translation
deficiency. In this work, we systematically analyz... |
2406.11917 | Chao He | Chao He and Hongmei Shi and Ruixin Li and Jianbo Li and ZuJun Yu | Modulated Differentiable STFT and Balanced Spectrum Metric for Freight
Train Wheelset Bearing Cross-machine Transfer Fault Diagnosis under Speed
Fluctuations | null | Advanced Engineering Informatics 62 (2024) 102568 | 10.1016/j.aei.2024.102568 | null | cs.LG eess.SP | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The service conditions of wheelset bearings has a direct impact on the safe
operation of railway heavy haul freight trains as the key components. However,
speed fluctuation of the trains and few fault samples are the two main problems
that restrict the accuracy of bearing fault diagnosis. Therefore, a
cross-machine t... | [
{
"version": "v1",
"created": "Mon, 17 Jun 2024 02:43:24 GMT"
},
{
"version": "v2",
"created": "Tue, 8 Apr 2025 04:01:43 GMT"
}
] | 2025-04-09T00:00:00 | [
[
"He",
"Chao",
""
],
[
"Shi",
"Hongmei",
""
],
[
"Li",
"Ruixin",
""
],
[
"Li",
"Jianbo",
""
],
[
"Yu",
"ZuJun",
""
]
] | TITLE: Modulated Differentiable STFT and Balanced Spectrum Metric for Freight
Train Wheelset Bearing Cross-machine Transfer Fault Diagnosis under Speed
Fluctuations
ABSTRACT: The service conditions of wheelset bearings has a direct impact on the safe
operation of railway heavy haul freight trains as the key com... |
2406.15341 | Haoyang Liu | Haoyang Liu, Shuyu Chen, Ye Zhang, Haohan Wang | GenoTEX: An LLM Agent Benchmark for Automated Gene Expression Data
Analysis | 31 pages, 4 figures | null | null | null | cs.LG cs.AI q-bio.GN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recent advancements in machine learning have significantly improved the
identification of disease-associated genes from gene expression datasets.
However, these processes often require extensive expertise and manual effort,
limiting their scalability. Large Language Model (LLM)-based agents have shown
promise in auto... | [
{
"version": "v1",
"created": "Fri, 21 Jun 2024 17:55:24 GMT"
},
{
"version": "v2",
"created": "Thu, 27 Mar 2025 17:59:22 GMT"
},
{
"version": "v3",
"created": "Tue, 8 Apr 2025 17:09:04 GMT"
}
] | 2025-04-09T00:00:00 | [
[
"Liu",
"Haoyang",
""
],
[
"Chen",
"Shuyu",
""
],
[
"Zhang",
"Ye",
""
],
[
"Wang",
"Haohan",
""
]
] | TITLE: GenoTEX: An LLM Agent Benchmark for Automated Gene Expression Data
Analysis
ABSTRACT: Recent advancements in machine learning have significantly improved the
identification of disease-associated genes from gene expression datasets.
However, these processes often require extensive expertise and manual effor... |
2407.21077 | Vahid Noroozi | Somshubra Majumdar, Vahid Noroozi, Mehrzad Samadi, Sean Narenthiran,
Aleksander Ficek, Wasi Uddin Ahmad, Jocelyn Huang, Jagadeesh Balam, Boris
Ginsburg | Genetic Instruct: Scaling up Synthetic Generation of Coding Instructions
for Large Language Models | null | null | null | null | cs.CL cs.LG cs.NE | http://creativecommons.org/licenses/by/4.0/ | Large Language Models (LLMs) require high quality instruction data for
effective alignment, particularly in code generation tasks where expert curated
datasets are expensive to produce. We present Genetic-Instruct, a scalable
algorithm for synthesizing large-scale, high quality coding instructions using
evolutionary ... | [
{
"version": "v1",
"created": "Mon, 29 Jul 2024 20:42:59 GMT"
},
{
"version": "v2",
"created": "Mon, 7 Apr 2025 23:35:11 GMT"
}
] | 2025-04-09T00:00:00 | [
[
"Majumdar",
"Somshubra",
""
],
[
"Noroozi",
"Vahid",
""
],
[
"Samadi",
"Mehrzad",
""
],
[
"Narenthiran",
"Sean",
""
],
[
"Ficek",
"Aleksander",
""
],
[
"Ahmad",
"Wasi Uddin",
""
],
[
"Huang",
"Jocelyn",
""
... | TITLE: Genetic Instruct: Scaling up Synthetic Generation of Coding Instructions
for Large Language Models
ABSTRACT: Large Language Models (LLMs) require high quality instruction data for
effective alignment, particularly in code generation tasks where expert curated
datasets are expensive to produce. We present G... |
2408.04290 | Amirreza Fateh | Alireza Saber, Pouria Parhami, Alimohammad Siahkarzadeh, Mansoor
Fateh, Amirreza Fateh | Efficient and Accurate Pneumonia Detection Using a Novel Multi-Scale
Transformer Approach | null | null | null | null | eess.IV cs.CV cs.LG | http://creativecommons.org/licenses/by/4.0/ | Pneumonia, a prevalent respiratory infection, remains a leading cause of
morbidity and mortality worldwide, particularly among vulnerable populations.
Chest X-rays serve as a primary tool for pneumonia detection; however,
variations in imaging conditions and subtle visual indicators complicate
consistent interpretati... | [
{
"version": "v1",
"created": "Thu, 8 Aug 2024 08:06:42 GMT"
},
{
"version": "v2",
"created": "Sun, 3 Nov 2024 11:51:50 GMT"
},
{
"version": "v3",
"created": "Sun, 26 Jan 2025 17:04:30 GMT"
},
{
"version": "v4",
"created": "Tue, 8 Apr 2025 07:00:02 GMT"
}
] | 2025-04-09T00:00:00 | [
[
"Saber",
"Alireza",
""
],
[
"Parhami",
"Pouria",
""
],
[
"Siahkarzadeh",
"Alimohammad",
""
],
[
"Fateh",
"Mansoor",
""
],
[
"Fateh",
"Amirreza",
""
]
] | TITLE: Efficient and Accurate Pneumonia Detection Using a Novel Multi-Scale
Transformer Approach
ABSTRACT: Pneumonia, a prevalent respiratory infection, remains a leading cause of
morbidity and mortality worldwide, particularly among vulnerable populations.
Chest X-rays serve as a primary tool for pneumonia detec... |
2408.06828 | Jingzhi Bao | Jingzhi Bao, Guanying Chen, Shuguang Cui | PIR: Photometric Inverse Rendering with Shading Cues Modeling and
Surface Reflectance Regularization | Accepted to 3DV 2025. Project page:
https://jzbao03.site/projects/PIR/ | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper addresses the problem of inverse rendering from photometric
images. Existing approaches for this problem suffer from the effects of
self-shadows, inter-reflections, and lack of constraints on the surface
reflectance, leading to inaccurate decomposition of reflectance and
illumination due to the ill-posed n... | [
{
"version": "v1",
"created": "Tue, 13 Aug 2024 11:39:14 GMT"
},
{
"version": "v2",
"created": "Wed, 29 Jan 2025 17:18:18 GMT"
},
{
"version": "v3",
"created": "Tue, 8 Apr 2025 03:08:44 GMT"
}
] | 2025-04-09T00:00:00 | [
[
"Bao",
"Jingzhi",
""
],
[
"Chen",
"Guanying",
""
],
[
"Cui",
"Shuguang",
""
]
] | TITLE: PIR: Photometric Inverse Rendering with Shading Cues Modeling and
Surface Reflectance Regularization
ABSTRACT: This paper addresses the problem of inverse rendering from photometric
images. Existing approaches for this problem suffer from the effects of
self-shadows, inter-reflections, and lack of constrai... |
2408.12598 | Ziyu Tang | Ziyu Tang, Weicai Ye, Yifan Wang, Di Huang, Hujun Bao, Tong He,
Guofeng Zhang | ND-SDF: Learning Normal Deflection Fields for High-Fidelity Indoor
Reconstruction | null | null | null | null | cs.CV cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Neural implicit reconstruction via volume rendering has demonstrated its
effectiveness in recovering dense 3D surfaces. However, it is non-trivial to
simultaneously recover meticulous geometry and preserve smoothness across
regions with differing characteristics. To address this issue, previous methods
typically empl... | [
{
"version": "v1",
"created": "Thu, 22 Aug 2024 17:59:01 GMT"
},
{
"version": "v2",
"created": "Thu, 26 Sep 2024 06:31:25 GMT"
},
{
"version": "v3",
"created": "Tue, 8 Apr 2025 15:24:36 GMT"
}
] | 2025-04-09T00:00:00 | [
[
"Tang",
"Ziyu",
""
],
[
"Ye",
"Weicai",
""
],
[
"Wang",
"Yifan",
""
],
[
"Huang",
"Di",
""
],
[
"Bao",
"Hujun",
""
],
[
"He",
"Tong",
""
],
[
"Zhang",
"Guofeng",
""
]
] | TITLE: ND-SDF: Learning Normal Deflection Fields for High-Fidelity Indoor
Reconstruction
ABSTRACT: Neural implicit reconstruction via volume rendering has demonstrated its
effectiveness in recovering dense 3D surfaces. However, it is non-trivial to
simultaneously recover meticulous geometry and preserve smoothnes... |
2408.13378 | Yoshitaka Inoue | Yoshitaka Inoue, Tianci Song, Xinling Wang, Augustin Luna, Tianfan Fu | DrugAgent: Multi-Agent Large Language Model-Based Reasoning for
Drug-Target Interaction Prediction | 15 pages, 1 figure | null | null | null | cs.AI cs.CL cs.IR cs.LG q-bio.QM | http://creativecommons.org/licenses/by/4.0/ | Advancements in large language models (LLMs) allow them to address diverse
questions using human-like interfaces. Still, limitations in their training
prevent them from answering accurately in scenarios that could benefit from
multiple perspectives. Multi-agent systems allow the resolution of questions to
enhance res... | [
{
"version": "v1",
"created": "Fri, 23 Aug 2024 21:24:59 GMT"
},
{
"version": "v2",
"created": "Thu, 12 Sep 2024 16:06:37 GMT"
},
{
"version": "v3",
"created": "Mon, 16 Sep 2024 22:13:30 GMT"
},
{
"version": "v4",
"created": "Mon, 7 Apr 2025 19:32:55 GMT"
}
] | 2025-04-09T00:00:00 | [
[
"Inoue",
"Yoshitaka",
""
],
[
"Song",
"Tianci",
""
],
[
"Wang",
"Xinling",
""
],
[
"Luna",
"Augustin",
""
],
[
"Fu",
"Tianfan",
""
]
] | TITLE: DrugAgent: Multi-Agent Large Language Model-Based Reasoning for
Drug-Target Interaction Prediction
ABSTRACT: Advancements in large language models (LLMs) allow them to address diverse
questions using human-like interfaces. Still, limitations in their training
prevent them from answering accurately in scena... |
2409.00134 | Alexey Skrynnik | Anton Andreychuk, Konstantin Yakovlev, Aleksandr Panov, Alexey
Skrynnik | MAPF-GPT: Imitation Learning for Multi-Agent Pathfinding at Scale | null | null | null | null | cs.MA cs.AI cs.LG | http://creativecommons.org/licenses/by/4.0/ | Multi-agent pathfinding (MAPF) is a problem that generally requires finding
collision-free paths for multiple agents in a shared environment. Solving MAPF
optimally, even under restrictive assumptions, is NP-hard, yet efficient
solutions for this problem are critical for numerous applications, such as
automated wareh... | [
{
"version": "v1",
"created": "Thu, 29 Aug 2024 12:55:10 GMT"
},
{
"version": "v2",
"created": "Thu, 12 Sep 2024 13:49:00 GMT"
},
{
"version": "v3",
"created": "Wed, 25 Sep 2024 13:09:35 GMT"
},
{
"version": "v4",
"created": "Tue, 11 Feb 2025 12:28:36 GMT"
},
{
"v... | 2025-04-09T00:00:00 | [
[
"Andreychuk",
"Anton",
""
],
[
"Yakovlev",
"Konstantin",
""
],
[
"Panov",
"Aleksandr",
""
],
[
"Skrynnik",
"Alexey",
""
]
] | TITLE: MAPF-GPT: Imitation Learning for Multi-Agent Pathfinding at Scale
ABSTRACT: Multi-agent pathfinding (MAPF) is a problem that generally requires finding
collision-free paths for multiple agents in a shared environment. Solving MAPF
optimally, even under restrictive assumptions, is NP-hard, yet efficient
solut... |
2409.13717 | Yiheng Wu | Yiheng Wu, Roman Yangarber, Xian Mao | DiVA-DocRE: A Discriminative and Voice-Aware Paradigm for Document-Level
Relation Extraction | After internal discussions among the co-authors, we have decided to
withdraw the manuscript due to a change in research direction and a lack of
unanimous agreement to proceed with publication at this time | null | null | null | cs.CL cs.AI cs.IR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The remarkable capabilities of Large Language Models (LLMs) in text
comprehension and generation have revolutionized Information Extraction (IE).
One such advancement is in Document-level Relation Triplet Extraction (DocRTE),
a critical task in information systems that aims to extract entities and their
semantic rela... | [
{
"version": "v1",
"created": "Sat, 7 Sep 2024 18:47:38 GMT"
},
{
"version": "v2",
"created": "Tue, 8 Apr 2025 10:43:00 GMT"
}
] | 2025-04-09T00:00:00 | [
[
"Wu",
"Yiheng",
""
],
[
"Yangarber",
"Roman",
""
],
[
"Mao",
"Xian",
""
]
] | TITLE: DiVA-DocRE: A Discriminative and Voice-Aware Paradigm for Document-Level
Relation Extraction
ABSTRACT: The remarkable capabilities of Large Language Models (LLMs) in text
comprehension and generation have revolutionized Information Extraction (IE).
One such advancement is in Document-level Relation Triplet... |
2409.16681 | Kun Zhou | Kun Zhou, You Zhang, Shengkui Zhao, Hao Wang, Zexu Pan, Dianwen Ng,
Chong Zhang, Chongjia Ni, Yukun Ma, Trung Hieu Nguyen, Jia Qi Yip, Bin Ma | Emotional Dimension Control in Language Model-Based Text-to-Speech:
Spanning a Broad Spectrum of Human Emotions | null | null | null | null | eess.AS cs.CL cs.SD | http://creativecommons.org/licenses/by/4.0/ | Current emotional text-to-speech systems face challenges in conveying the
full spectrum of human emotions, largely due to the inherent complexity of
human emotions and the limited range of emotional labels in existing speech
datasets. To address these limitations, this paper introduces a TTS framework
that provides f... | [
{
"version": "v1",
"created": "Wed, 25 Sep 2024 07:16:16 GMT"
},
{
"version": "v2",
"created": "Tue, 8 Apr 2025 08:08:08 GMT"
}
] | 2025-04-09T00:00:00 | [
[
"Zhou",
"Kun",
""
],
[
"Zhang",
"You",
""
],
[
"Zhao",
"Shengkui",
""
],
[
"Wang",
"Hao",
""
],
[
"Pan",
"Zexu",
""
],
[
"Ng",
"Dianwen",
""
],
[
"Zhang",
"Chong",
""
],
[
"Ni",
"Chongjia",
... | TITLE: Emotional Dimension Control in Language Model-Based Text-to-Speech:
Spanning a Broad Spectrum of Human Emotions
ABSTRACT: Current emotional text-to-speech systems face challenges in conveying the
full spectrum of human emotions, largely due to the inherent complexity of
human emotions and the limited range... |
2410.05454 | Ayesha Vermani | Ayesha Vermani, Josue Nassar, Hyungju Jeon, Matthew Dowling, Il
Memming Park | Meta-Dynamical State Space Models for Integrative Neural Data Analysis | null | null | null | null | stat.ML cs.LG q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Learning shared structure across environments facilitates rapid learning and
adaptive behavior in neural systems. This has been widely demonstrated and
applied in machine learning to train models that are capable of generalizing to
novel settings. However, there has been limited work exploiting the shared
structure i... | [
{
"version": "v1",
"created": "Mon, 7 Oct 2024 19:35:49 GMT"
},
{
"version": "v2",
"created": "Mon, 7 Apr 2025 21:44:06 GMT"
}
] | 2025-04-09T00:00:00 | [
[
"Vermani",
"Ayesha",
""
],
[
"Nassar",
"Josue",
""
],
[
"Jeon",
"Hyungju",
""
],
[
"Dowling",
"Matthew",
""
],
[
"Park",
"Il Memming",
""
]
] | TITLE: Meta-Dynamical State Space Models for Integrative Neural Data Analysis
ABSTRACT: Learning shared structure across environments facilitates rapid learning and
adaptive behavior in neural systems. This has been widely demonstrated and
applied in machine learning to train models that are capable of generalizing... |
2410.08527 | Yangyi Chen | Yangyi Chen, Binxuan Huang, Yifan Gao, Zhengyang Wang, Jingfeng Yang,
Heng Ji | Scaling Laws for Predicting Downstream Performance in LLMs | Accepted to TMLR | null | null | null | cs.CL cs.AI cs.LG | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Precise estimation of downstream performance in large language models (LLMs)
prior to training is essential for guiding their development process. Scaling
laws analysis utilizes the statistics of a series of significantly smaller
sampling language models (LMs) to predict the performance of the target LLM.
For downstr... | [
{
"version": "v1",
"created": "Fri, 11 Oct 2024 04:57:48 GMT"
},
{
"version": "v2",
"created": "Mon, 7 Apr 2025 21:47:09 GMT"
}
] | 2025-04-09T00:00:00 | [
[
"Chen",
"Yangyi",
""
],
[
"Huang",
"Binxuan",
""
],
[
"Gao",
"Yifan",
""
],
[
"Wang",
"Zhengyang",
""
],
[
"Yang",
"Jingfeng",
""
],
[
"Ji",
"Heng",
""
]
] | TITLE: Scaling Laws for Predicting Downstream Performance in LLMs
ABSTRACT: Precise estimation of downstream performance in large language models (LLMs)
prior to training is essential for guiding their development process. Scaling
laws analysis utilizes the statistics of a series of significantly smaller
sampling l... |
2410.12779 | Xingzhi Sun | Xingzhi Sun, Danqi Liao, Kincaid MacDonald, Yanlei Zhang, Chen Liu,
Guillaume Huguet, Guy Wolf, Ian Adelstein, Tim G. J. Rudner, Smita
Krishnaswamy | Geometry-Aware Generative Autoencoders for Warped Riemannian Metric
Learning and Generative Modeling on Data Manifolds | Published in Proceedings of the 28th International Conference on
Artificial Intelligence and Statistics (AISTATS 2025) | null | null | null | cs.LG math.DG stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Rapid growth of high-dimensional datasets in fields such as single-cell RNA
sequencing and spatial genomics has led to unprecedented opportunities for
scientific discovery, but it also presents unique computational and statistical
challenges. Traditional methods struggle with geometry-aware data generation,
interpola... | [
{
"version": "v1",
"created": "Wed, 16 Oct 2024 17:53:26 GMT"
},
{
"version": "v2",
"created": "Fri, 18 Oct 2024 18:27:10 GMT"
},
{
"version": "v3",
"created": "Sat, 25 Jan 2025 16:39:26 GMT"
},
{
"version": "v4",
"created": "Mon, 7 Apr 2025 19:30:58 GMT"
}
] | 2025-04-09T00:00:00 | [
[
"Sun",
"Xingzhi",
""
],
[
"Liao",
"Danqi",
""
],
[
"MacDonald",
"Kincaid",
""
],
[
"Zhang",
"Yanlei",
""
],
[
"Liu",
"Chen",
""
],
[
"Huguet",
"Guillaume",
""
],
[
"Wolf",
"Guy",
""
],
[
"Adelstein"... | TITLE: Geometry-Aware Generative Autoencoders for Warped Riemannian Metric
Learning and Generative Modeling on Data Manifolds
ABSTRACT: Rapid growth of high-dimensional datasets in fields such as single-cell RNA
sequencing and spatial genomics has led to unprecedented opportunities for
scientific discovery, but i... |
2410.16520 | Naba Rizvi | Naba Rizvi, Harper Strickland, Daniel Gitelman, Tristan Cooper, Alexis
Morales-Flores, Michael Golden, Aekta Kallepalli, Akshat Alurkar, Haaset
Owens, Saleha Ahmedi, Isha Khirwadkar, Imani Munyaka, Nedjma Ousidhoum | AUTALIC: A Dataset for Anti-AUTistic Ableist Language In Context | 9 pages, 5 figures, 7 tables | null | null | null | cs.CL cs.AI | http://creativecommons.org/licenses/by/4.0/ | As our understanding of autism and ableism continues to increase, so does our
understanding of ableist language towards autistic people. Such language poses
a significant challenge in NLP research due to its subtle and context-dependent
nature. Yet, detecting anti-autistic ableist language remains underexplored,
with... | [
{
"version": "v1",
"created": "Mon, 21 Oct 2024 21:21:29 GMT"
},
{
"version": "v2",
"created": "Thu, 21 Nov 2024 16:43:06 GMT"
},
{
"version": "v3",
"created": "Tue, 8 Apr 2025 17:08:26 GMT"
}
] | 2025-04-09T00:00:00 | [
[
"Rizvi",
"Naba",
""
],
[
"Strickland",
"Harper",
""
],
[
"Gitelman",
"Daniel",
""
],
[
"Cooper",
"Tristan",
""
],
[
"Morales-Flores",
"Alexis",
""
],
[
"Golden",
"Michael",
""
],
[
"Kallepalli",
"Aekta",
""... | TITLE: AUTALIC: A Dataset for Anti-AUTistic Ableist Language In Context
ABSTRACT: As our understanding of autism and ableism continues to increase, so does our
understanding of ableist language towards autistic people. Such language poses
a significant challenge in NLP research due to its subtle and context-depende... |
2410.17875 | Guangyuan Shi | Guangyuan Shi, Zexin Lu, Xiaoyu Dong, Wenlong Zhang, Xuanyu Zhang,
Yujie Feng, Xiao-Ming Wu | Understanding Layer Significance in LLM Alignment | null | null | null | null | cs.CL cs.AI | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Aligning large language models (LLMs) through supervised fine-tuning is
essential for tailoring them to specific applications. Recent studies suggest
that alignment primarily adjusts a model's presentation style rather than its
foundational knowledge, indicating that only certain components of the model
are significa... | [
{
"version": "v1",
"created": "Wed, 23 Oct 2024 13:47:05 GMT"
},
{
"version": "v2",
"created": "Fri, 20 Dec 2024 19:24:24 GMT"
},
{
"version": "v3",
"created": "Tue, 8 Apr 2025 09:44:28 GMT"
}
] | 2025-04-09T00:00:00 | [
[
"Shi",
"Guangyuan",
""
],
[
"Lu",
"Zexin",
""
],
[
"Dong",
"Xiaoyu",
""
],
[
"Zhang",
"Wenlong",
""
],
[
"Zhang",
"Xuanyu",
""
],
[
"Feng",
"Yujie",
""
],
[
"Wu",
"Xiao-Ming",
""
]
] | TITLE: Understanding Layer Significance in LLM Alignment
ABSTRACT: Aligning large language models (LLMs) through supervised fine-tuning is
essential for tailoring them to specific applications. Recent studies suggest
that alignment primarily adjusts a model's presentation style rather than its
foundational knowledg... |
2410.18358 | Henrik Ebel | Henrik Ebel, Jan van Delden, Timo L\"uddecke, Aditya Borse, Rutwik
Gulakala, Marcus Stoffel, Manish Yadav, Merten Stender, Leon Schindler,
Kristin Miriam de Payrebrune, Maximilian Raff, C. David Remy, Benedict
R\"oder, Rohit Raj, Tobias Rentschler, Alexander Tismer, Stefan Riedelbauch,
Peter Eberhard | Data Publishing in Mechanics and Dynamics: Challenges, Guidelines, and
Examples from Engineering Design | 25 pages, 10 figures | DCE 6 (2025) e23 | 10.1017/dce.2025.13 | null | cs.CY cs.AI cs.CE cs.ET cs.SY eess.SY | http://creativecommons.org/licenses/by/4.0/ | Data-based methods have gained increasing importance in engineering,
especially but not only driven by successes with deep artificial neural
networks. Success stories are prevalent, e.g., in areas such as data-driven
modeling, control and automation, as well as surrogate modeling for accelerated
simulation. Beyond en... | [
{
"version": "v1",
"created": "Mon, 7 Oct 2024 18:26:05 GMT"
},
{
"version": "v2",
"created": "Fri, 20 Dec 2024 12:58:09 GMT"
}
] | 2025-04-09T00:00:00 | [
[
"Ebel",
"Henrik",
""
],
[
"van Delden",
"Jan",
""
],
[
"Lüddecke",
"Timo",
""
],
[
"Borse",
"Aditya",
""
],
[
"Gulakala",
"Rutwik",
""
],
[
"Stoffel",
"Marcus",
""
],
[
"Yadav",
"Manish",
""
],
[
"S... | TITLE: Data Publishing in Mechanics and Dynamics: Challenges, Guidelines, and
Examples from Engineering Design
ABSTRACT: Data-based methods have gained increasing importance in engineering,
especially but not only driven by successes with deep artificial neural
networks. Success stories are prevalent, e.g., in ar... |
2410.19426 | Daniel Galperin | Daniel Galperin, Ullrich K\"othe | Analyzing Generative Models by Manifold Entropic Metrics | Camera-ready version: accepted at AISTATS 2025 | null | null | null | cs.LG stat.ML | http://creativecommons.org/licenses/by/4.0/ | Good generative models should not only synthesize high quality data, but also
utilize interpretable representations that aid human understanding of their
behavior. However, it is difficult to measure objectively if and to what degree
desirable properties of disentangled representations have been achieved.
Inspired by... | [
{
"version": "v1",
"created": "Fri, 25 Oct 2024 09:35:00 GMT"
},
{
"version": "v2",
"created": "Mon, 7 Apr 2025 15:47:53 GMT"
}
] | 2025-04-09T00:00:00 | [
[
"Galperin",
"Daniel",
""
],
[
"Köthe",
"Ullrich",
""
]
] | TITLE: Analyzing Generative Models by Manifold Entropic Metrics
ABSTRACT: Good generative models should not only synthesize high quality data, but also
utilize interpretable representations that aid human understanding of their
behavior. However, it is difficult to measure objectively if and to what degree
desirabl... |
2411.02540 | Mateusz Cedro | Mateusz Cedro, David Martens | GraphXAIN: Narratives to Explain Graph Neural Networks | 19 pages, 9 figures, 2 tables | World Conference on Explainable Artificial Intelligence 2025 | null | null | cs.LG cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Graph Neural Networks (GNNs) are a powerful technique for machine learning on
graph-structured data, yet they pose challenges in interpretability. Existing
GNN explanation methods usually yield technical outputs, such as subgraphs and
feature importance scores, that are difficult for non-data scientists to
understand... | [
{
"version": "v1",
"created": "Mon, 4 Nov 2024 19:21:06 GMT"
},
{
"version": "v2",
"created": "Fri, 8 Nov 2024 08:29:10 GMT"
},
{
"version": "v3",
"created": "Wed, 12 Feb 2025 15:14:01 GMT"
}
] | 2025-04-09T00:00:00 | [
[
"Cedro",
"Mateusz",
""
],
[
"Martens",
"David",
""
]
] | TITLE: GraphXAIN: Narratives to Explain Graph Neural Networks
ABSTRACT: Graph Neural Networks (GNNs) are a powerful technique for machine learning on
graph-structured data, yet they pose challenges in interpretability. Existing
GNN explanation methods usually yield technical outputs, such as subgraphs and
feature i... |
2411.04794 | Yuxin Zuo | Yuxin Zuo, Wenxuan Jiang, Wenxuan Liu, Zixuan Li, Long Bai, Hanbin
Wang, Yutao Zeng, Xiaolong Jin, Jiafeng Guo, Xueqi Cheng | KnowCoder-X: Boosting Multilingual Information Extraction via Code | 26 pages, 3 figures | null | null | null | cs.CL cs.AI cs.LG | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Empirical evidence indicates that LLMs exhibit spontaneous cross-lingual
alignment. However, although LLMs show promising cross-lingual alignment in IE,
a significant imbalance across languages persists, highlighting an underlying
deficiency. To address this, we propose KnowCoder-X, a powerful code LLM with
advanced ... | [
{
"version": "v1",
"created": "Thu, 7 Nov 2024 15:36:05 GMT"
},
{
"version": "v2",
"created": "Tue, 8 Apr 2025 16:16:30 GMT"
}
] | 2025-04-09T00:00:00 | [
[
"Zuo",
"Yuxin",
""
],
[
"Jiang",
"Wenxuan",
""
],
[
"Liu",
"Wenxuan",
""
],
[
"Li",
"Zixuan",
""
],
[
"Bai",
"Long",
""
],
[
"Wang",
"Hanbin",
""
],
[
"Zeng",
"Yutao",
""
],
[
"Jin",
"Xiaolong",... | TITLE: KnowCoder-X: Boosting Multilingual Information Extraction via Code
ABSTRACT: Empirical evidence indicates that LLMs exhibit spontaneous cross-lingual
alignment. However, although LLMs show promising cross-lingual alignment in IE,
a significant imbalance across languages persists, highlighting an underlying
d... |
2411.08872 | Sadjad Alikhani | Sadjad Alikhani, Gouranga Charan, and Ahmed Alkhateeb | Large Wireless Model (LWM): A Foundation Model for Wireless Channels | The LWM model and relevant scripts are available on the LWM website:
https://lwm-wireless.net/ | null | null | null | cs.IT eess.SP math.IT | http://creativecommons.org/licenses/by-nc-sa/4.0/ | This paper presents Large Wireless Model (LWM) -- the world's first
foundation model for wireless channels. Designed as a task-agnostic model, LWM
generates universal, rich, contextualized channel embeddings (features) that
potentially enhance performance across a wide range of downstream tasks in
wireless communicat... | [
{
"version": "v1",
"created": "Wed, 13 Nov 2024 18:51:10 GMT"
},
{
"version": "v2",
"created": "Mon, 7 Apr 2025 19:49:37 GMT"
}
] | 2025-04-09T00:00:00 | [
[
"Alikhani",
"Sadjad",
""
],
[
"Charan",
"Gouranga",
""
],
[
"Alkhateeb",
"Ahmed",
""
]
] | TITLE: Large Wireless Model (LWM): A Foundation Model for Wireless Channels
ABSTRACT: This paper presents Large Wireless Model (LWM) -- the world's first
foundation model for wireless channels. Designed as a task-agnostic model, LWM
generates universal, rich, contextualized channel embeddings (features) that
potent... |
2411.13951 | Lucas Correia | Lucas Correia, Jan-Christoph Goos, Thomas B\"ack, Anna V. Kononova | PATH: A Discrete-sequence Dataset for Evaluating Online Unsupervised
Anomaly Detection Approaches for Multivariate Time Series | Submitted to the Big Data Research journal | null | null | null | cs.LG cs.AI cs.CE cs.SY eess.SY | http://creativecommons.org/licenses/by/4.0/ | Benchmarking anomaly detection approaches for multivariate time series is a
challenging task due to a lack of high-quality datasets. Current publicly
available datasets are too small, not diverse and feature trivial anomalies,
which hinders measurable progress in this research area. We propose a solution:
a diverse, ... | [
{
"version": "v1",
"created": "Thu, 21 Nov 2024 09:03:12 GMT"
},
{
"version": "v2",
"created": "Mon, 25 Nov 2024 14:24:57 GMT"
},
{
"version": "v3",
"created": "Wed, 15 Jan 2025 17:16:22 GMT"
},
{
"version": "v4",
"created": "Tue, 8 Apr 2025 15:26:49 GMT"
}
] | 2025-04-09T00:00:00 | [
[
"Correia",
"Lucas",
""
],
[
"Goos",
"Jan-Christoph",
""
],
[
"Bäck",
"Thomas",
""
],
[
"Kononova",
"Anna V.",
""
]
] | TITLE: PATH: A Discrete-sequence Dataset for Evaluating Online Unsupervised
Anomaly Detection Approaches for Multivariate Time Series
ABSTRACT: Benchmarking anomaly detection approaches for multivariate time series is a
challenging task due to a lack of high-quality datasets. Current publicly
available datasets a... |
2411.16199 | Haojie Zheng | Shuchen Weng, Haojie Zheng, Peixuan Zhang, Yuchen Hong, Han Jiang, Si
Li, Boxin Shi | VIRES: Video Instance Repainting via Sketch and Text Guided Generation | null | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We introduce VIRES, a video instance repainting method with sketch and text
guidance, enabling video instance repainting, replacement, generation, and
removal. Existing approaches struggle with temporal consistency and accurate
alignment with the provided sketch sequence. VIRES leverages the generative
priors of text... | [
{
"version": "v1",
"created": "Mon, 25 Nov 2024 08:55:41 GMT"
},
{
"version": "v2",
"created": "Tue, 26 Nov 2024 11:43:01 GMT"
},
{
"version": "v3",
"created": "Fri, 28 Feb 2025 08:57:48 GMT"
},
{
"version": "v4",
"created": "Tue, 4 Mar 2025 05:28:29 GMT"
},
{
"ve... | 2025-04-09T00:00:00 | [
[
"Weng",
"Shuchen",
""
],
[
"Zheng",
"Haojie",
""
],
[
"Zhang",
"Peixuan",
""
],
[
"Hong",
"Yuchen",
""
],
[
"Jiang",
"Han",
""
],
[
"Li",
"Si",
""
],
[
"Shi",
"Boxin",
""
]
] | TITLE: VIRES: Video Instance Repainting via Sketch and Text Guided Generation
ABSTRACT: We introduce VIRES, a video instance repainting method with sketch and text
guidance, enabling video instance repainting, replacement, generation, and
removal. Existing approaches struggle with temporal consistency and accurate
... |
2411.16260 | Fu-Chieh Chang | Fu-Chieh Chang, You-Chen Lin, Pei-Yuan Wu | Unraveling Arithmetic in Large Language Models: The Role of Algebraic
Structures | null | ICLR 2025 Workshop on Reasoning and Planning for Large Language
Models | null | null | cs.LG cs.CL | http://creativecommons.org/licenses/by/4.0/ | The reasoning abilities of large language models (LLMs) have improved with
chain-of-thought (CoT) prompting, allowing models to solve complex tasks
stepwise. However, training CoT capabilities requires detailed reasoning data,
which is often scarce. The self-taught reasoner (STaR) framework addresses this
by using re... | [
{
"version": "v1",
"created": "Mon, 25 Nov 2024 10:23:11 GMT"
},
{
"version": "v2",
"created": "Tue, 8 Apr 2025 15:19:23 GMT"
}
] | 2025-04-09T00:00:00 | [
[
"Chang",
"Fu-Chieh",
""
],
[
"Lin",
"You-Chen",
""
],
[
"Wu",
"Pei-Yuan",
""
]
] | TITLE: Unraveling Arithmetic in Large Language Models: The Role of Algebraic
Structures
ABSTRACT: The reasoning abilities of large language models (LLMs) have improved with
chain-of-thought (CoT) prompting, allowing models to solve complex tasks
stepwise. However, training CoT capabilities requires detailed reaso... |
2411.16310 | Jaime Corsetti | Jaime Corsetti, Francesco Giuliari, Alice Fasoli, Davide Boscaini,
Fabio Poiesi | Functionality understanding and segmentation in 3D scenes | CVPR 2025 Highlight. Camera ready version. 20 pages, 12 figures, 7
tables | null | null | null | cs.CV | http://creativecommons.org/licenses/by/4.0/ | Understanding functionalities in 3D scenes involves interpreting natural
language descriptions to locate functional interactive objects, such as handles
and buttons, in a 3D environment. Functionality understanding is highly
challenging, as it requires both world knowledge to interpret language and
spatial perception... | [
{
"version": "v1",
"created": "Mon, 25 Nov 2024 11:57:48 GMT"
},
{
"version": "v2",
"created": "Tue, 26 Nov 2024 16:45:22 GMT"
},
{
"version": "v3",
"created": "Wed, 4 Dec 2024 15:12:06 GMT"
},
{
"version": "v4",
"created": "Tue, 8 Apr 2025 08:30:11 GMT"
}
] | 2025-04-09T00:00:00 | [
[
"Corsetti",
"Jaime",
""
],
[
"Giuliari",
"Francesco",
""
],
[
"Fasoli",
"Alice",
""
],
[
"Boscaini",
"Davide",
""
],
[
"Poiesi",
"Fabio",
""
]
] | TITLE: Functionality understanding and segmentation in 3D scenes
ABSTRACT: Understanding functionalities in 3D scenes involves interpreting natural
language descriptions to locate functional interactive objects, such as handles
and buttons, in a 3D environment. Functionality understanding is highly
challenging, as ... |
2411.17191 | Naoki Matsumura | Naoki Matsumura, Yuta Yoshimoto, Tamio Yamazaki, Tomohito Amano,
Tomoyuki Noda, Naoki Ebata, Takatoshi Kasano and Yasufumi Sakai | Generator of Neural Network Potential for Molecular Dynamics:
Constructing Robust and Accurate Potentials with Active Learning for
Nanosecond-scale Simulations | null | null | 10.1021/acs.jctc.4c01613 | null | cond-mat.mtrl-sci physics.comp-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Neural network potentials (NNPs) enable large-scale molecular dynamics (MD)
simulations of systems containing >10,000 atoms with the accuracy comparable to
ab initio methods and play a crucial role in material studies. Although NNPs
are valuable for short-duration MD simulations, maintaining the stability of
long-dur... | [
{
"version": "v1",
"created": "Tue, 26 Nov 2024 08:03:13 GMT"
},
{
"version": "v2",
"created": "Wed, 19 Mar 2025 07:20:57 GMT"
},
{
"version": "v3",
"created": "Tue, 8 Apr 2025 07:53:26 GMT"
}
] | 2025-04-09T00:00:00 | [
[
"Matsumura",
"Naoki",
""
],
[
"Yoshimoto",
"Yuta",
""
],
[
"Yamazaki",
"Tamio",
""
],
[
"Amano",
"Tomohito",
""
],
[
"Noda",
"Tomoyuki",
""
],
[
"Ebata",
"Naoki",
""
],
[
"Kasano",
"Takatoshi",
""
],
[
... | TITLE: Generator of Neural Network Potential for Molecular Dynamics:
Constructing Robust and Accurate Potentials with Active Learning for
Nanosecond-scale Simulations
ABSTRACT: Neural network potentials (NNPs) enable large-scale molecular dynamics (MD)
simulations of systems containing >10,000 atoms with the ac... |
2412.06206 | Nan Zhang | Nan Zhang, Prafulla Kumar Choubey, Alexander Fabbri, Gabriel
Bernadett-Shapiro, Rui Zhang, Prasenjit Mitra, Caiming Xiong, Chien-Sheng Wu | SiReRAG: Indexing Similar and Related Information for Multihop Reasoning | ICLR 2025 | null | null | null | cs.CL cs.AI | http://creativecommons.org/licenses/by/4.0/ | Indexing is an important step towards strong performance in
retrieval-augmented generation (RAG) systems. However, existing methods
organize data based on either semantic similarity (similarity) or related
information (relatedness), but do not cover both perspectives comprehensively.
Our analysis reveals that modelin... | [
{
"version": "v1",
"created": "Mon, 9 Dec 2024 04:56:43 GMT"
},
{
"version": "v2",
"created": "Mon, 7 Apr 2025 19:47:16 GMT"
}
] | 2025-04-09T00:00:00 | [
[
"Zhang",
"Nan",
""
],
[
"Choubey",
"Prafulla Kumar",
""
],
[
"Fabbri",
"Alexander",
""
],
[
"Bernadett-Shapiro",
"Gabriel",
""
],
[
"Zhang",
"Rui",
""
],
[
"Mitra",
"Prasenjit",
""
],
[
"Xiong",
"Caiming",
... | TITLE: SiReRAG: Indexing Similar and Related Information for Multihop Reasoning
ABSTRACT: Indexing is an important step towards strong performance in
retrieval-augmented generation (RAG) systems. However, existing methods
organize data based on either semantic similarity (similarity) or related
information (related... |
2412.06717 | Sahil Sethi | Sahil Sethi, Sai Reddy, Mansi Sakarvadia, Jordan Serotte, Darlington
Nwaudo, Nicholas Maassen, Lewis Shi | Toward Non-Invasive Diagnosis of Bankart Lesions with Deep Learning | Accepted for presentation at SPIE Medical Imaging 2025:
Computer-Aided Diagnosis. The manuscript is expected to appear in the
conference proceedings | null | 10.1117/12.3046251 | null | cs.CV cs.AI cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Bankart lesions, or anterior-inferior glenoid labral tears, are
diagnostically challenging on standard MRIs due to their subtle imaging
features-often necessitating invasive MRI arthrograms (MRAs). This study
develops deep learning (DL) models to detect Bankart lesions on both standard
MRIs and MRAs, aiming to improv... | [
{
"version": "v1",
"created": "Mon, 9 Dec 2024 18:04:27 GMT"
}
] | 2025-04-09T00:00:00 | [
[
"Sethi",
"Sahil",
""
],
[
"Reddy",
"Sai",
""
],
[
"Sakarvadia",
"Mansi",
""
],
[
"Serotte",
"Jordan",
""
],
[
"Nwaudo",
"Darlington",
""
],
[
"Maassen",
"Nicholas",
""
],
[
"Shi",
"Lewis",
""
]
] | TITLE: Toward Non-Invasive Diagnosis of Bankart Lesions with Deep Learning
ABSTRACT: Bankart lesions, or anterior-inferior glenoid labral tears, are
diagnostically challenging on standard MRIs due to their subtle imaging
features-often necessitating invasive MRI arthrograms (MRAs). This study
develops deep learning... |
2412.06947 | Bardia Nadimi | Bardia Nadimi and Ghali Omar Boutaib and Hao Zheng | PyraNet: A Multi-Layered Hierarchical Dataset for Verilog | null | null | null | null | cs.AR cs.AI cs.LG cs.PL | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Recently, there has been a growing interest in leveraging Large Language
Models for Verilog code generation. However, the current quality of the
generated Verilog code remains suboptimal. This is largely due to the absence
of well-defined, well-organized datasets with high-quality samples, as well as
a lack of innova... | [
{
"version": "v1",
"created": "Mon, 9 Dec 2024 19:45:54 GMT"
},
{
"version": "v2",
"created": "Fri, 27 Dec 2024 01:07:02 GMT"
},
{
"version": "v3",
"created": "Mon, 7 Apr 2025 21:58:26 GMT"
}
] | 2025-04-09T00:00:00 | [
[
"Nadimi",
"Bardia",
""
],
[
"Boutaib",
"Ghali Omar",
""
],
[
"Zheng",
"Hao",
""
]
] | TITLE: PyraNet: A Multi-Layered Hierarchical Dataset for Verilog
ABSTRACT: Recently, there has been a growing interest in leveraging Large Language
Models for Verilog code generation. However, the current quality of the
generated Verilog code remains suboptimal. This is largely due to the absence
of well-defined, w... |
2412.07456 | Ben Steinfurth | Jonas Schulte-Sasse, Ben Steinfurth and Julien Weiss | Automatic extraction of wall streamlines from oil-flow visualizations
using a convolutional neural network | null | Exp. Fluids 66 (2025) | 10.1007/s00348-025-04016-x | null | physics.flu-dyn | http://creativecommons.org/licenses/by/4.0/ | Oil-flow visualizations represent a simple means to reveal time-averaged wall
streamline patterns. Yet, the evaluation of such images can be a time-consuming
process and is subjective to human perception. In this study, we present a fast
and robust method to obtain quantitative insight based on qualitative oil-flow
v... | [
{
"version": "v1",
"created": "Tue, 10 Dec 2024 12:21:44 GMT"
}
] | 2025-04-09T00:00:00 | [
[
"Schulte-Sasse",
"Jonas",
""
],
[
"Steinfurth",
"Ben",
""
],
[
"Weiss",
"Julien",
""
]
] | TITLE: Automatic extraction of wall streamlines from oil-flow visualizations
using a convolutional neural network
ABSTRACT: Oil-flow visualizations represent a simple means to reveal time-averaged wall
streamline patterns. Yet, the evaluation of such images can be a time-consuming
process and is subjective to hum... |
2412.08307 | Shijian Wang | Shijian Wang, Linxin Song, Jieyu Zhang, Ryotaro Shimizu, Jiarui Jin,
Ao Luo, Yuan Lu, Li Yao, Cunjian Chen, Julian McAuley, Wentao Zhang, Hanqian
Wu | Investigating the Scaling Effect of Instruction Templates for Training
Multimodal Language Model | null | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Current multimodal language model (MLM) training approaches overlook the
influence of instruction templates. Previous research deals with this problem
by leveraging hand-crafted or model-generated templates, failing to investigate
the scaling effect of instruction templates on MLM training. In this work, we
propose a... | [
{
"version": "v1",
"created": "Wed, 11 Dec 2024 11:39:42 GMT"
},
{
"version": "v2",
"created": "Sun, 6 Apr 2025 14:45:49 GMT"
},
{
"version": "v3",
"created": "Tue, 8 Apr 2025 08:30:30 GMT"
}
] | 2025-04-09T00:00:00 | [
[
"Wang",
"Shijian",
""
],
[
"Song",
"Linxin",
""
],
[
"Zhang",
"Jieyu",
""
],
[
"Shimizu",
"Ryotaro",
""
],
[
"Jin",
"Jiarui",
""
],
[
"Luo",
"Ao",
""
],
[
"Lu",
"Yuan",
""
],
[
"Yao",
"Li",
... | TITLE: Investigating the Scaling Effect of Instruction Templates for Training
Multimodal Language Model
ABSTRACT: Current multimodal language model (MLM) training approaches overlook the
influence of instruction templates. Previous research deals with this problem
by leveraging hand-crafted or model-generated tem... |
2412.08755 | Kyle Stein | Kyle Stein, Andrew Arash Mahyari, Guillermo Francia, Eman El-Sheikh | Proactive Adversarial Defense: Harnessing Prompt Tuning in
Vision-Language Models to Detect Unseen Backdoored Images | null | null | null | null | cs.CV cs.AI cs.CR cs.LG | http://creativecommons.org/licenses/by/4.0/ | Backdoor attacks pose a critical threat by embedding hidden triggers into
inputs, causing models to misclassify them into target labels. While extensive
research has focused on mitigating these attacks in object recognition models
through weight fine-tuning, much less attention has been given to detecting
backdoored ... | [
{
"version": "v1",
"created": "Wed, 11 Dec 2024 19:54:14 GMT"
},
{
"version": "v2",
"created": "Thu, 9 Jan 2025 19:15:20 GMT"
},
{
"version": "v3",
"created": "Fri, 14 Mar 2025 19:24:34 GMT"
},
{
"version": "v4",
"created": "Mon, 7 Apr 2025 18:01:26 GMT"
}
] | 2025-04-09T00:00:00 | [
[
"Stein",
"Kyle",
""
],
[
"Mahyari",
"Andrew Arash",
""
],
[
"Francia",
"Guillermo",
""
],
[
"El-Sheikh",
"Eman",
""
]
] | TITLE: Proactive Adversarial Defense: Harnessing Prompt Tuning in
Vision-Language Models to Detect Unseen Backdoored Images
ABSTRACT: Backdoor attacks pose a critical threat by embedding hidden triggers into
inputs, causing models to misclassify them into target labels. While extensive
research has focused on mit... |
2412.11530 | Junda Cheng | Junda Cheng, Zhipeng Cai, Zhaoxing Zhang, Wei Yin, Matthias Muller,
Michael Paulitsch, Xin Yang | RoMeO: Robust Metric Visual Odometry | null | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Visual odometry (VO) aims to estimate camera poses from visual inputs -- a
fundamental building block for many applications such as VR/AR and robotics.
This work focuses on monocular RGB VO where the input is a monocular RGB video
without IMU or 3D sensors. Existing approaches lack robustness under this
challenging s... | [
{
"version": "v1",
"created": "Mon, 16 Dec 2024 08:08:35 GMT"
},
{
"version": "v2",
"created": "Thu, 19 Dec 2024 06:32:22 GMT"
},
{
"version": "v3",
"created": "Tue, 8 Apr 2025 13:16:35 GMT"
}
] | 2025-04-09T00:00:00 | [
[
"Cheng",
"Junda",
""
],
[
"Cai",
"Zhipeng",
""
],
[
"Zhang",
"Zhaoxing",
""
],
[
"Yin",
"Wei",
""
],
[
"Muller",
"Matthias",
""
],
[
"Paulitsch",
"Michael",
""
],
[
"Yang",
"Xin",
""
]
] | TITLE: RoMeO: Robust Metric Visual Odometry
ABSTRACT: Visual odometry (VO) aims to estimate camera poses from visual inputs -- a
fundamental building block for many applications such as VR/AR and robotics.
This work focuses on monocular RGB VO where the input is a monocular RGB video
without IMU or 3D sensors. Exis... |
2412.17867 | Ziming Guo | Ziming Guo, Chao Ma, Yinggang Sun, Tiancheng Zhao, Guangyao Wang, Hai
Huang | Evaluating and Enhancing LLMs for Multi-turn Text-to-SQL with Multiple
Question Types | International Joint Conference on Neural Networks 2025 (IJCNN 2025) | null | null | null | cs.CL cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recent advancements in large language models (LLMs) have significantly
advanced text-to-SQL systems. However, most LLM-based methods often narrowly
focus on SQL generation, neglecting the complexities of real-world
conversational queries. This oversight can lead to unreliable responses,
particularly for ambiguous que... | [
{
"version": "v1",
"created": "Sat, 21 Dec 2024 10:13:45 GMT"
},
{
"version": "v2",
"created": "Thu, 3 Apr 2025 07:13:30 GMT"
},
{
"version": "v3",
"created": "Sun, 6 Apr 2025 09:47:45 GMT"
},
{
"version": "v4",
"created": "Tue, 8 Apr 2025 02:23:17 GMT"
}
] | 2025-04-09T00:00:00 | [
[
"Guo",
"Ziming",
""
],
[
"Ma",
"Chao",
""
],
[
"Sun",
"Yinggang",
""
],
[
"Zhao",
"Tiancheng",
""
],
[
"Wang",
"Guangyao",
""
],
[
"Huang",
"Hai",
""
]
] | TITLE: Evaluating and Enhancing LLMs for Multi-turn Text-to-SQL with Multiple
Question Types
ABSTRACT: Recent advancements in large language models (LLMs) have significantly
advanced text-to-SQL systems. However, most LLM-based methods often narrowly
focus on SQL generation, neglecting the complexities of real-wo... |
2501.00952 | Maxim Ziatdinov | Sarah I. Allec, Maxim Ziatdinov | Active and transfer learning with partially Bayesian neural networks for
materials and chemicals | Minor revisions | null | null | null | cond-mat.dis-nn cond-mat.mtrl-sci physics.data-an | http://creativecommons.org/licenses/by/4.0/ | Active learning, an iterative process of selecting the most informative data
points for exploration, is crucial for efficient characterization of materials
and chemicals property space. Neural networks excel at predicting these
properties but lack the uncertainty quantification needed for active
learning-driven explo... | [
{
"version": "v1",
"created": "Wed, 1 Jan 2025 20:48:26 GMT"
},
{
"version": "v2",
"created": "Mon, 7 Apr 2025 20:33:33 GMT"
}
] | 2025-04-09T00:00:00 | [
[
"Allec",
"Sarah I.",
""
],
[
"Ziatdinov",
"Maxim",
""
]
] | TITLE: Active and transfer learning with partially Bayesian neural networks for
materials and chemicals
ABSTRACT: Active learning, an iterative process of selecting the most informative data
points for exploration, is crucial for efficient characterization of materials
and chemicals property space. Neural network... |
2501.04671 | Charles Corbi\`ere | Charles Corbi\`ere, Simon Roburin, Syrielle Montariol, Antoine
Bosselut and Alexandre Alahi | Retrieval-Based Interleaved Visual Chain-of-Thought in Real-World
Driving Scenarios | Project page: https://vita-epfl.github.io/DrivingVQA | null | null | null | cs.CV cs.AI | http://creativecommons.org/licenses/by/4.0/ | While chain-of-thought (CoT) prompting improves reasoning in large language
models, its effectiveness in vision-language models (VLMs) remains limited due
to over-reliance on textual cues and memorized knowledge. To investigate the
visual reasoning capabilities of VLMs in complex real-world scenarios, we
introduce Dr... | [
{
"version": "v1",
"created": "Wed, 8 Jan 2025 18:31:16 GMT"
},
{
"version": "v2",
"created": "Tue, 8 Apr 2025 17:09:59 GMT"
}
] | 2025-04-09T00:00:00 | [
[
"Corbière",
"Charles",
""
],
[
"Roburin",
"Simon",
""
],
[
"Montariol",
"Syrielle",
""
],
[
"Bosselut",
"Antoine",
""
],
[
"Alahi",
"Alexandre",
""
]
] | TITLE: Retrieval-Based Interleaved Visual Chain-of-Thought in Real-World
Driving Scenarios
ABSTRACT: While chain-of-thought (CoT) prompting improves reasoning in large language
models, its effectiveness in vision-language models (VLMs) remains limited due
to over-reliance on textual cues and memorized knowledge. ... |
2501.05446 | Yifan Yu | Yifan Yu, Shaohui Liu, R\'emi Pautrat, Marc Pollefeys, Viktor Larsson | Relative Pose Estimation through Affine Corrections of Monocular Depth
Priors | CVPR 2025 (Highlight) | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Monocular depth estimation (MDE) models have undergone significant
advancements over recent years. Many MDE models aim to predict affine-invariant
relative depth from monocular images, while recent developments in large-scale
training and vision foundation models enable reasonable estimation of metric
(absolute) dept... | [
{
"version": "v1",
"created": "Thu, 9 Jan 2025 18:58:30 GMT"
},
{
"version": "v2",
"created": "Mon, 24 Mar 2025 17:14:43 GMT"
},
{
"version": "v3",
"created": "Tue, 8 Apr 2025 03:59:21 GMT"
}
] | 2025-04-09T00:00:00 | [
[
"Yu",
"Yifan",
""
],
[
"Liu",
"Shaohui",
""
],
[
"Pautrat",
"Rémi",
""
],
[
"Pollefeys",
"Marc",
""
],
[
"Larsson",
"Viktor",
""
]
] | TITLE: Relative Pose Estimation through Affine Corrections of Monocular Depth
Priors
ABSTRACT: Monocular depth estimation (MDE) models have undergone significant
advancements over recent years. Many MDE models aim to predict affine-invariant
relative depth from monocular images, while recent developments in large... |
2501.09333 | Wei-Lun Chao | Arpita Chowdhury, Dipanjyoti Paul, Zheda Mai, Jianyang Gu, Ziheng
Zhang, Kazi Sajeed Mehrab, Elizabeth G. Campolongo, Daniel Rubenstein,
Charles V. Stewart, Anuj Karpatne, Tanya Berger-Wolf, Yu Su, Wei-Lun Chao | Prompt-CAM: Making Vision Transformers Interpretable for Fine-Grained
Analysis | Accepted by CVPR 2025 Main Conference | null | null | null | cs.CV cs.AI | http://creativecommons.org/licenses/by-nc-sa/4.0/ | We present a simple approach to make pre-trained Vision Transformers (ViTs)
interpretable for fine-grained analysis, aiming to identify and localize the
traits that distinguish visually similar categories, such as bird species.
Pre-trained ViTs, such as DINO, have demonstrated remarkable capabilities in
extracting lo... | [
{
"version": "v1",
"created": "Thu, 16 Jan 2025 07:07:41 GMT"
},
{
"version": "v2",
"created": "Mon, 7 Apr 2025 18:03:40 GMT"
}
] | 2025-04-09T00:00:00 | [
[
"Chowdhury",
"Arpita",
""
],
[
"Paul",
"Dipanjyoti",
""
],
[
"Mai",
"Zheda",
""
],
[
"Gu",
"Jianyang",
""
],
[
"Zhang",
"Ziheng",
""
],
[
"Mehrab",
"Kazi Sajeed",
""
],
[
"Campolongo",
"Elizabeth G.",
""
... | TITLE: Prompt-CAM: Making Vision Transformers Interpretable for Fine-Grained
Analysis
ABSTRACT: We present a simple approach to make pre-trained Vision Transformers (ViTs)
interpretable for fine-grained analysis, aiming to identify and localize the
traits that distinguish visually similar categories, such as bird... |
2501.11014 | Ken Enda | Ken Enda, Yoshitaka Oda, Zen-ichi Tanei, Kenichi Satoh, Hiroaki
Motegi, Terasaka Shunsuke, Shigeru Yamaguchi, Takahiro Ogawa, Wang Lei,
Masumi Tsuda and Shinya Tanaka | Transfer Learning Strategies for Pathological Foundation Models: A
Systematic Evaluation in Brain Tumor Classification | 25 pages, 7 figures | null | null | null | eess.IV cs.CV | http://creativecommons.org/licenses/by/4.0/ | Foundation models pretrained on large-scale pathology datasets have shown
promising results across various diagnostic tasks. Here, we present a
systematic evaluation of transfer learning strategies for brain tumor
classification using these models. We analyzed 254 cases comprising five major
tumor types: glioblastoma... | [
{
"version": "v1",
"created": "Sun, 19 Jan 2025 11:18:34 GMT"
},
{
"version": "v2",
"created": "Tue, 8 Apr 2025 01:49:45 GMT"
}
] | 2025-04-09T00:00:00 | [
[
"Enda",
"Ken",
""
],
[
"Oda",
"Yoshitaka",
""
],
[
"Tanei",
"Zen-ichi",
""
],
[
"Satoh",
"Kenichi",
""
],
[
"Motegi",
"Hiroaki",
""
],
[
"Shunsuke",
"Terasaka",
""
],
[
"Yamaguchi",
"Shigeru",
""
],
[
... | TITLE: Transfer Learning Strategies for Pathological Foundation Models: A
Systematic Evaluation in Brain Tumor Classification
ABSTRACT: Foundation models pretrained on large-scale pathology datasets have shown
promising results across various diagnostic tasks. Here, we present a
systematic evaluation of transfer ... |
2501.17848 | Fabricio Olivetti de Franca | Fabricio Olivetti de Franca and Gabriel Kronberger | Improving Genetic Programming for Symbolic Regression with Equality
Graphs | 10 pages, 5 figures, 4 tables. In Genetic and Evolutionary
Computation Conference (GECCO 25) | null | 10.1145/3712256.3726383 | null | cs.LG | http://creativecommons.org/licenses/by/4.0/ | The search for symbolic regression models with genetic programming (GP) has a
tendency of revisiting expressions in their original or equivalent forms.
Repeatedly evaluating equivalent expressions is inefficient, as it does not
immediately lead to better solutions. However, evolutionary algorithms require
diversity a... | [
{
"version": "v1",
"created": "Wed, 29 Jan 2025 18:49:34 GMT"
},
{
"version": "v2",
"created": "Tue, 8 Apr 2025 16:48:10 GMT"
}
] | 2025-04-09T00:00:00 | [
[
"de Franca",
"Fabricio Olivetti",
""
],
[
"Kronberger",
"Gabriel",
""
]
] | TITLE: Improving Genetic Programming for Symbolic Regression with Equality
Graphs
ABSTRACT: The search for symbolic regression models with genetic programming (GP) has a
tendency of revisiting expressions in their original or equivalent forms.
Repeatedly evaluating equivalent expressions is inefficient, as it doe... |
2502.03251 | Li Sun | Li Sun, Zhenhao Huang, Suyang Zhou, Qiqi Wan, Hao Peng, Philip Yu | RiemannGFM: Learning a Graph Foundation Model from Riemannian Geometry | Accepted by WWW 2025 (Oral) | null | null | null | cs.LG | http://creativecommons.org/licenses/by-nc-sa/4.0/ | The foundation model has heralded a new era in artificial intelligence,
pretraining a single model to offer cross-domain transferability on different
datasets. Graph neural networks excel at learning graph data, the omnipresent
non-Euclidean structure, but often lack the generalization capacity. Hence,
graph foundati... | [
{
"version": "v1",
"created": "Wed, 5 Feb 2025 15:06:09 GMT"
},
{
"version": "v2",
"created": "Tue, 8 Apr 2025 07:04:29 GMT"
}
] | 2025-04-09T00:00:00 | [
[
"Sun",
"Li",
""
],
[
"Huang",
"Zhenhao",
""
],
[
"Zhou",
"Suyang",
""
],
[
"Wan",
"Qiqi",
""
],
[
"Peng",
"Hao",
""
],
[
"Yu",
"Philip",
""
]
] | TITLE: RiemannGFM: Learning a Graph Foundation Model from Riemannian Geometry
ABSTRACT: The foundation model has heralded a new era in artificial intelligence,
pretraining a single model to offer cross-domain transferability on different
datasets. Graph neural networks excel at learning graph data, the omnipresent
... |
2502.04760 | Rui Wang | Rui Wang | Graph Federated Learning Based Proactive Content Caching in Edge
Computing | null | null | null | null | cs.LG cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | With the rapid growth of mobile data traffic and the increasing prevalence of
video streaming, proactive content caching in edge computing has become crucial
for reducing latency and alleviating network congestion. However, traditional
caching strategies such as FIFO, LRU, and LFU fail to effectively predict
future c... | [
{
"version": "v1",
"created": "Fri, 7 Feb 2025 08:48:06 GMT"
},
{
"version": "v2",
"created": "Tue, 8 Apr 2025 12:46:45 GMT"
}
] | 2025-04-09T00:00:00 | [
[
"Wang",
"Rui",
""
]
] | TITLE: Graph Federated Learning Based Proactive Content Caching in Edge
Computing
ABSTRACT: With the rapid growth of mobile data traffic and the increasing prevalence of
video streaming, proactive content caching in edge computing has become crucial
for reducing latency and alleviating network congestion. However... |
2502.07847 | Behraj Khan | Behraj Khan, Rizwan Qureshi, Nouman Muhammad Durrani, Tahir Syed | Confidence-calibrated covariate shift correction for few-shot
classification in Vision-Language Models | null | null | null | null | cs.CV cs.LG | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Since the establishment of vision-language foundation models as the new
mainstay in low-shot vision classification tasks, the question of domain
generalization arising from insufficient target data is assuming more
importance. This scarcity challenge induces sampling bias and amplifies model
sensitivity to variations... | [
{
"version": "v1",
"created": "Tue, 11 Feb 2025 10:10:15 GMT"
},
{
"version": "v2",
"created": "Tue, 8 Apr 2025 07:54:30 GMT"
}
] | 2025-04-09T00:00:00 | [
[
"Khan",
"Behraj",
""
],
[
"Qureshi",
"Rizwan",
""
],
[
"Durrani",
"Nouman Muhammad",
""
],
[
"Syed",
"Tahir",
""
]
] | TITLE: Confidence-calibrated covariate shift correction for few-shot
classification in Vision-Language Models
ABSTRACT: Since the establishment of vision-language foundation models as the new
mainstay in low-shot vision classification tasks, the question of domain
generalization arising from insufficient target d... |
2502.11007 | Liangqi Yuan | Liangqi Yuan and Dong-Jun Han and Shiqiang Wang and Christopher G.
Brinton | Local-Cloud Inference Offloading for LLMs in Multi-Modal, Multi-Task,
Multi-Dialogue Settings | null | null | null | null | cs.LG cs.DC | http://creativecommons.org/licenses/by/4.0/ | Compared to traditional machine learning models, recent large language models
(LLMs) can exhibit multi-task-solving capabilities through multiple dialogues
and multi-modal data sources. These unique characteristics of LLMs, together
with their large model size, make their deployment more challenging.
Specifically, (i... | [
{
"version": "v1",
"created": "Sun, 16 Feb 2025 06:18:28 GMT"
},
{
"version": "v2",
"created": "Mon, 7 Apr 2025 18:49:28 GMT"
}
] | 2025-04-09T00:00:00 | [
[
"Yuan",
"Liangqi",
""
],
[
"Han",
"Dong-Jun",
""
],
[
"Wang",
"Shiqiang",
""
],
[
"Brinton",
"Christopher G.",
""
]
] | TITLE: Local-Cloud Inference Offloading for LLMs in Multi-Modal, Multi-Task,
Multi-Dialogue Settings
ABSTRACT: Compared to traditional machine learning models, recent large language models
(LLMs) can exhibit multi-task-solving capabilities through multiple dialogues
and multi-modal data sources. These unique char... |
2502.14270 | Rajeshwari Mistri | Nachiket Kapure, Harsh Joshi, Rajeshwari Mistri, Parul Kumari, Manasi
Mali, Seema Purohit, Neha Sharma, Mrityunjoy Panday, Chittaranjan S. Yajnik | Predicting Fetal Birthweight from High Dimensional Data using Advanced
Machine Learning | null | null | null | null | cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Birth weight serves as a fundamental indicator of neonatal health, closely
linked to both early medical interventions and long-term developmental risks.
Traditional predictive models, often constrained by limited feature selection
and incomplete datasets, struggle to achieve overlooking complex maternal and
fetal int... | [
{
"version": "v1",
"created": "Thu, 20 Feb 2025 05:17:39 GMT"
},
{
"version": "v2",
"created": "Tue, 8 Apr 2025 07:54:17 GMT"
}
] | 2025-04-09T00:00:00 | [
[
"Kapure",
"Nachiket",
""
],
[
"Joshi",
"Harsh",
""
],
[
"Mistri",
"Rajeshwari",
""
],
[
"Kumari",
"Parul",
""
],
[
"Mali",
"Manasi",
""
],
[
"Purohit",
"Seema",
""
],
[
"Sharma",
"Neha",
""
],
[
"Pa... | TITLE: Predicting Fetal Birthweight from High Dimensional Data using Advanced
Machine Learning
ABSTRACT: Birth weight serves as a fundamental indicator of neonatal health, closely
linked to both early medical interventions and long-term developmental risks.
Traditional predictive models, often constrained by limi... |
2502.19363 | Ru Peng | Ru Peng, Kexin Yang, Yawen Zeng, Junyang Lin, Dayiheng Liu, Junbo Zhao | DataMan: Data Manager for Pre-training Large Language Models | ICLR2025 paper | null | null | null | cs.CL cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The performance emergence of large language models (LLMs) driven by data
scaling laws makes the selection of pre-training data increasingly important.
However, existing methods rely on limited heuristics and human intuition,
lacking comprehensive and clear guidelines. To address this, we are inspired by
``reverse thi... | [
{
"version": "v1",
"created": "Wed, 26 Feb 2025 18:01:19 GMT"
},
{
"version": "v2",
"created": "Thu, 13 Mar 2025 15:42:07 GMT"
},
{
"version": "v3",
"created": "Tue, 8 Apr 2025 03:21:10 GMT"
}
] | 2025-04-09T00:00:00 | [
[
"Peng",
"Ru",
""
],
[
"Yang",
"Kexin",
""
],
[
"Zeng",
"Yawen",
""
],
[
"Lin",
"Junyang",
""
],
[
"Liu",
"Dayiheng",
""
],
[
"Zhao",
"Junbo",
""
]
] | TITLE: DataMan: Data Manager for Pre-training Large Language Models
ABSTRACT: The performance emergence of large language models (LLMs) driven by data
scaling laws makes the selection of pre-training data increasingly important.
However, existing methods rely on limited heuristics and human intuition,
lacking compr... |
2502.19679 | Linzhuo Li | Linzhuo li | Old Experience Helps: Leveraging Survey Methodology to Improve AI Text
Annotation Reliability in Social Sciences | 7 figures | null | null | null | cs.DL cs.HC | http://creativecommons.org/licenses/by/4.0/ | This paper introduces a framework for assessing the reliability of Large
Language Model (LLM) text annotations in social science research by adapting
established survey methodology principles. Drawing parallels between survey
respondent behavior and LLM outputs, the study implements three key
interventions: option ra... | [
{
"version": "v1",
"created": "Thu, 27 Feb 2025 01:42:10 GMT"
},
{
"version": "v2",
"created": "Thu, 13 Mar 2025 03:06:47 GMT"
},
{
"version": "v3",
"created": "Tue, 8 Apr 2025 06:48:04 GMT"
}
] | 2025-04-09T00:00:00 | [
[
"li",
"Linzhuo",
""
]
] | TITLE: Old Experience Helps: Leveraging Survey Methodology to Improve AI Text
Annotation Reliability in Social Sciences
ABSTRACT: This paper introduces a framework for assessing the reliability of Large
Language Model (LLM) text annotations in social science research by adapting
established survey methodology pri... |
2502.21024 | Abdelrahman E.M. Abdallah | Abdelrahman Abdallah, Bhawna Piryani, Jonas Wallat, Avishek Anand,
Adam Jatowt | TempRetriever: Fusion-based Temporal Dense Passage Retrieval for
Time-Sensitive Questions | null | null | null | null | cs.IR cs.CL | http://creativecommons.org/licenses/by/4.0/ | Temporal awareness is crucial in many information retrieval tasks,
particularly in scenarios where the relevance of documents depends on their
alignment with the query's temporal context. Traditional approaches such as
BM25 and Dense Passage Retrieval (DPR) focus on lexical or semantic similarity
but tend to neglect ... | [
{
"version": "v1",
"created": "Fri, 28 Feb 2025 13:06:25 GMT"
},
{
"version": "v2",
"created": "Tue, 8 Apr 2025 13:11:58 GMT"
}
] | 2025-04-09T00:00:00 | [
[
"Abdallah",
"Abdelrahman",
""
],
[
"Piryani",
"Bhawna",
""
],
[
"Wallat",
"Jonas",
""
],
[
"Anand",
"Avishek",
""
],
[
"Jatowt",
"Adam",
""
]
] | TITLE: TempRetriever: Fusion-based Temporal Dense Passage Retrieval for
Time-Sensitive Questions
ABSTRACT: Temporal awareness is crucial in many information retrieval tasks,
particularly in scenarios where the relevance of documents depends on their
alignment with the query's temporal context. Traditional approac... |
2503.05050 | Melkamu Mersha | Melkamu Abay Mersha, Mesay Gemeda Yigezu, Hassan Shakil, Ali K.
AlShami, Sanghyun Byun, Jugal Kalita | A Unified Framework with Novel Metrics for Evaluating the Effectiveness
of XAI Techniques in LLMs | arXiv admin note: substantial text overlap with arXiv:2501.15374 | null | null | null | cs.CL cs.AI cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The increasing complexity of LLMs presents significant challenges to their
transparency and interpretability, necessitating the use of eXplainable AI
(XAI) techniques to enhance trustworthiness and usability. This study
introduces a comprehensive evaluation framework with four novel metrics for
assessing the effectiv... | [
{
"version": "v1",
"created": "Thu, 6 Mar 2025 23:59:50 GMT"
},
{
"version": "v2",
"created": "Mon, 7 Apr 2025 20:37:11 GMT"
}
] | 2025-04-09T00:00:00 | [
[
"Mersha",
"Melkamu Abay",
""
],
[
"Yigezu",
"Mesay Gemeda",
""
],
[
"Shakil",
"Hassan",
""
],
[
"AlShami",
"Ali K.",
""
],
[
"Byun",
"Sanghyun",
""
],
[
"Kalita",
"Jugal",
""
]
] | TITLE: A Unified Framework with Novel Metrics for Evaluating the Effectiveness
of XAI Techniques in LLMs
ABSTRACT: The increasing complexity of LLMs presents significant challenges to their
transparency and interpretability, necessitating the use of eXplainable AI
(XAI) techniques to enhance trustworthiness and u... |
2503.05725 | Kim Duc Tran | T.Q.D. Pham, K.D. Tran, Khanh T. P. Nguyen, X.V. Tran, L. K\"oehl, and
K.P. Tran | A new framework for prognostics in decentralized industries: Enhancing
fairness, security, and transparency through Blockchain and Federated
Learning | null | null | null | null | cs.CY cs.AI | http://creativecommons.org/licenses/by/4.0/ | As global industries transition towards Industry 5.0 predictive maintenance
PM remains crucial for cost effective operations resilience and minimizing
downtime in increasingly smart manufacturing environments In this chapter we
explore how the integration of Federated Learning FL and blockchain BC
technologies enhanc... | [
{
"version": "v1",
"created": "Mon, 17 Feb 2025 20:28:40 GMT"
},
{
"version": "v2",
"created": "Tue, 8 Apr 2025 16:53:33 GMT"
}
] | 2025-04-09T00:00:00 | [
[
"Pham",
"T. Q. D.",
""
],
[
"Tran",
"K. D.",
""
],
[
"Nguyen",
"Khanh T. P.",
""
],
[
"Tran",
"X. V.",
""
],
[
"Köehl",
"L.",
""
],
[
"Tran",
"K. P.",
""
]
] | TITLE: A new framework for prognostics in decentralized industries: Enhancing
fairness, security, and transparency through Blockchain and Federated
Learning
ABSTRACT: As global industries transition towards Industry 5.0 predictive maintenance
PM remains crucial for cost effective operations resilience and minim... |
2503.07378 | Yusuke Hashimoto | Yusuke Hashimoto, Xue Jia, Hao Li, Takaaki Tomai | A Materials Map Integrating Experimental and Computational Data via
Graph-Based Machine Learning for Enhanced Materials Discovery | null | null | null | null | cond-mat.mtrl-sci cs.LG | http://creativecommons.org/licenses/by/4.0/ | Materials informatics (MI), emerging from the integration of materials
science and data science, is expected to significantly accelerate material
development and discovery. The data used in MI are derived from both
computational and experimental studies; however, their integration remains
challenging. In our previous... | [
{
"version": "v1",
"created": "Mon, 10 Mar 2025 14:31:34 GMT"
},
{
"version": "v2",
"created": "Tue, 11 Mar 2025 06:31:52 GMT"
},
{
"version": "v3",
"created": "Thu, 13 Mar 2025 10:04:14 GMT"
},
{
"version": "v4",
"created": "Tue, 18 Mar 2025 04:43:10 GMT"
},
{
"v... | 2025-04-09T00:00:00 | [
[
"Hashimoto",
"Yusuke",
""
],
[
"Jia",
"Xue",
""
],
[
"Li",
"Hao",
""
],
[
"Tomai",
"Takaaki",
""
]
] | TITLE: A Materials Map Integrating Experimental and Computational Data via
Graph-Based Machine Learning for Enhanced Materials Discovery
ABSTRACT: Materials informatics (MI), emerging from the integration of materials
science and data science, is expected to significantly accelerate material
development and disco... |
2503.08111 | Jianhui Wang | Jianhui Wang, Zhifei Yang, Yangfan He, Huixiong Zhang, Yuxuan Chen,
Jingwei Huang | MaRI: Material Retrieval Integration across Domains | null | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Accurate material retrieval is critical for creating realistic 3D assets.
Existing methods rely on datasets that capture shape-invariant and
lighting-varied representations of materials, which are scarce and face
challenges due to limited diversity and inadequate real-world generalization.
Most current approaches ado... | [
{
"version": "v1",
"created": "Tue, 11 Mar 2025 07:23:11 GMT"
},
{
"version": "v2",
"created": "Thu, 3 Apr 2025 07:30:21 GMT"
},
{
"version": "v3",
"created": "Tue, 8 Apr 2025 08:53:57 GMT"
}
] | 2025-04-09T00:00:00 | [
[
"Wang",
"Jianhui",
""
],
[
"Yang",
"Zhifei",
""
],
[
"He",
"Yangfan",
""
],
[
"Zhang",
"Huixiong",
""
],
[
"Chen",
"Yuxuan",
""
],
[
"Huang",
"Jingwei",
""
]
] | TITLE: MaRI: Material Retrieval Integration across Domains
ABSTRACT: Accurate material retrieval is critical for creating realistic 3D assets.
Existing methods rely on datasets that capture shape-invariant and
lighting-varied representations of materials, which are scarce and face
challenges due to limited diversit... |
2503.09516 | Bowen Jin | Bowen Jin, Hansi Zeng, Zhenrui Yue, Jinsung Yoon, Sercan Arik, Dong
Wang, Hamed Zamani, Jiawei Han | Search-R1: Training LLMs to Reason and Leverage Search Engines with
Reinforcement Learning | 31 pages | null | null | null | cs.CL cs.AI cs.IR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Efficiently acquiring external knowledge and up-to-date information is
essential for effective reasoning and text generation in large language models
(LLMs). Prompting advanced LLMs with reasoning capabilities to use search
engines during inference is often suboptimal, as the LLM might not fully
possess the capabilit... | [
{
"version": "v1",
"created": "Wed, 12 Mar 2025 16:26:39 GMT"
},
{
"version": "v2",
"created": "Wed, 19 Mar 2025 21:40:12 GMT"
},
{
"version": "v3",
"created": "Tue, 8 Apr 2025 14:03:26 GMT"
}
] | 2025-04-09T00:00:00 | [
[
"Jin",
"Bowen",
""
],
[
"Zeng",
"Hansi",
""
],
[
"Yue",
"Zhenrui",
""
],
[
"Yoon",
"Jinsung",
""
],
[
"Arik",
"Sercan",
""
],
[
"Wang",
"Dong",
""
],
[
"Zamani",
"Hamed",
""
],
[
"Han",
"Jiawei"... | TITLE: Search-R1: Training LLMs to Reason and Leverage Search Engines with
Reinforcement Learning
ABSTRACT: Efficiently acquiring external knowledge and up-to-date information is
essential for effective reasoning and text generation in large language models
(LLMs). Prompting advanced LLMs with reasoning capabilit... |
2503.12763 | Kewei Sui | Kewei Sui, Anindita Ghosh, Inwoo Hwang, Bing Zhou, Jian Wang, Chuan
Guo | A Survey on Human Interaction Motion Generation | The repository listing relevant papers is accessible at:
https://github.com/soraproducer/Awesome-Human-Interaction-Motion-Generation | null | null | null | cs.CV cs.LG | http://creativecommons.org/licenses/by/4.0/ | Humans inhabit a world defined by interactions -- with other humans, objects,
and environments. These interactive movements not only convey our relationships
with our surroundings but also demonstrate how we perceive and communicate with
the real world. Therefore, replicating these interaction behaviors in digital
sy... | [
{
"version": "v1",
"created": "Mon, 17 Mar 2025 02:55:10 GMT"
},
{
"version": "v2",
"created": "Mon, 7 Apr 2025 23:38:41 GMT"
}
] | 2025-04-09T00:00:00 | [
[
"Sui",
"Kewei",
""
],
[
"Ghosh",
"Anindita",
""
],
[
"Hwang",
"Inwoo",
""
],
[
"Zhou",
"Bing",
""
],
[
"Wang",
"Jian",
""
],
[
"Guo",
"Chuan",
""
]
] | TITLE: A Survey on Human Interaction Motion Generation
ABSTRACT: Humans inhabit a world defined by interactions -- with other humans, objects,
and environments. These interactive movements not only convey our relationships
with our surroundings but also demonstrate how we perceive and communicate with
the real worl... |
2503.17486 | Zhengqing Gao | Zhengqing Gao, Dongting Hu, Jia-Wang Bian, Huan Fu, Yan Li, Tongliang
Liu, Mingming Gong, Kun Zhang | ProtoGS: Efficient and High-Quality Rendering with 3D Gaussian
Prototypes | null | null | null | null | cs.CV cs.AI | http://creativecommons.org/licenses/by/4.0/ | 3D Gaussian Splatting (3DGS) has made significant strides in novel view
synthesis but is limited by the substantial number of Gaussian primitives
required, posing challenges for deployment on lightweight devices. Recent
methods address this issue by compressing the storage size of densified
Gaussians, yet fail to pre... | [
{
"version": "v1",
"created": "Fri, 21 Mar 2025 18:55:14 GMT"
},
{
"version": "v2",
"created": "Tue, 25 Mar 2025 13:03:48 GMT"
},
{
"version": "v3",
"created": "Tue, 8 Apr 2025 12:19:01 GMT"
}
] | 2025-04-09T00:00:00 | [
[
"Gao",
"Zhengqing",
""
],
[
"Hu",
"Dongting",
""
],
[
"Bian",
"Jia-Wang",
""
],
[
"Fu",
"Huan",
""
],
[
"Li",
"Yan",
""
],
[
"Liu",
"Tongliang",
""
],
[
"Gong",
"Mingming",
""
],
[
"Zhang",
"Kun... | TITLE: ProtoGS: Efficient and High-Quality Rendering with 3D Gaussian
Prototypes
ABSTRACT: 3D Gaussian Splatting (3DGS) has made significant strides in novel view
synthesis but is limited by the substantial number of Gaussian primitives
required, posing challenges for deployment on lightweight devices. Recent
met... |
2503.22926 | Zikang Yuan | Zikang Yuan, Ruiye Ming, Chengwei Zhao, Yonghao Tan, Pingcheng Dong,
Hongcheng Luo, Yuzhong Jiao, Xin Yang and Kwang-Ting Cheng | SR-LIO++: Efficient LiDAR-Inertial Odometry and Quantized Mapping with
Sweep Reconstruction | 10 pages, 12 figures | null | null | null | cs.RO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Addressing the inherent low acquisition frequency limitation of 3D LiDAR to
achieve high-frequency output has become a critical research focus in the
LiDAR-Inertial Odometry (LIO) domain. To ensure real-time performance,
frequency-enhanced LIO systems must process each sweep within significantly
reduced timeframe, wh... | [
{
"version": "v1",
"created": "Sat, 29 Mar 2025 01:06:54 GMT"
},
{
"version": "v2",
"created": "Tue, 8 Apr 2025 05:27:15 GMT"
}
] | 2025-04-09T00:00:00 | [
[
"Yuan",
"Zikang",
""
],
[
"Ming",
"Ruiye",
""
],
[
"Zhao",
"Chengwei",
""
],
[
"Tan",
"Yonghao",
""
],
[
"Dong",
"Pingcheng",
""
],
[
"Luo",
"Hongcheng",
""
],
[
"Jiao",
"Yuzhong",
""
],
[
"Yang",
... | TITLE: SR-LIO++: Efficient LiDAR-Inertial Odometry and Quantized Mapping with
Sweep Reconstruction
ABSTRACT: Addressing the inherent low acquisition frequency limitation of 3D LiDAR to
achieve high-frequency output has become a critical research focus in the
LiDAR-Inertial Odometry (LIO) domain. To ensure real-ti... |
2504.00597 | Jirui Qi | Jirui Qi, Raquel Fern\'andez, Arianna Bisazza | On the Consistency of Multilingual Context Utilization in
Retrieval-Augmented Generation | Under review at COLM2025. All codes and data are released at
https://github.com/Betswish/mRAG-Context-Consistency | null | null | null | cs.CL cs.AI | http://creativecommons.org/licenses/by/4.0/ | Retrieval-augmented generation (RAG) with large language models (LLMs) has
demonstrated strong performance in multilingual question-answering (QA) tasks
by leveraging relevant passages retrieved from corpora. In multilingual RAG
(mRAG), the retrieved passages can be written in languages other than that of
the query e... | [
{
"version": "v1",
"created": "Tue, 1 Apr 2025 09:55:23 GMT"
},
{
"version": "v2",
"created": "Tue, 8 Apr 2025 12:40:23 GMT"
}
] | 2025-04-09T00:00:00 | [
[
"Qi",
"Jirui",
""
],
[
"Fernández",
"Raquel",
""
],
[
"Bisazza",
"Arianna",
""
]
] | TITLE: On the Consistency of Multilingual Context Utilization in
Retrieval-Augmented Generation
ABSTRACT: Retrieval-augmented generation (RAG) with large language models (LLMs) has
demonstrated strong performance in multilingual question-answering (QA) tasks
by leveraging relevant passages retrieved from corpora.... |
2504.01698 | Yilong Lu | Yi-Long Lu, Chunhui Zhang, Jiajun Song, Lifeng Fan, Wei Wang | ToM-RL: Reinforcement Learning Unlocks Theory of Mind in Small LLMs | null | null | null | null | cs.CL cs.AI | http://creativecommons.org/licenses/by/4.0/ | Recent advancements in rule-based reinforcement learning (RL), applied during
the post-training phase of large language models (LLMs), have significantly
enhanced their capabilities in structured reasoning tasks such as mathematics
and logical inference. However, the effectiveness of RL in social reasoning,
particula... | [
{
"version": "v1",
"created": "Wed, 2 Apr 2025 12:58:42 GMT"
},
{
"version": "v2",
"created": "Tue, 8 Apr 2025 03:58:20 GMT"
}
] | 2025-04-09T00:00:00 | [
[
"Lu",
"Yi-Long",
""
],
[
"Zhang",
"Chunhui",
""
],
[
"Song",
"Jiajun",
""
],
[
"Fan",
"Lifeng",
""
],
[
"Wang",
"Wei",
""
]
] | TITLE: ToM-RL: Reinforcement Learning Unlocks Theory of Mind in Small LLMs
ABSTRACT: Recent advancements in rule-based reinforcement learning (RL), applied during
the post-training phase of large language models (LLMs), have significantly
enhanced their capabilities in structured reasoning tasks such as mathematics... |
2504.02010 | Nan Zhang | Nan Zhang, Yusen Zhang, Prasenjit Mitra, Rui Zhang | When Reasoning Meets Compression: Benchmarking Compressed Large
Reasoning Models on Complex Reasoning Tasks | null | null | null | null | cs.LG cs.AI | http://creativecommons.org/licenses/by/4.0/ | Recent open-source large reasoning models (LRMs) exhibit strong performance
on complex reasoning tasks, but their large parameter count makes them
prohibitively expensive for individuals. The compression of large language
models (LLMs) offers an effective solution to reduce cost of computational
resources. However, s... | [
{
"version": "v1",
"created": "Wed, 2 Apr 2025 05:17:46 GMT"
}
] | 2025-04-09T00:00:00 | [
[
"Zhang",
"Nan",
""
],
[
"Zhang",
"Yusen",
""
],
[
"Mitra",
"Prasenjit",
""
],
[
"Zhang",
"Rui",
""
]
] | TITLE: When Reasoning Meets Compression: Benchmarking Compressed Large
Reasoning Models on Complex Reasoning Tasks
ABSTRACT: Recent open-source large reasoning models (LRMs) exhibit strong performance
on complex reasoning tasks, but their large parameter count makes them
prohibitively expensive for individuals. T... |
2504.02329 | Seif Mzoughi Msc | Seif Mzoughi, Ahmed Haj yahmed, Mohamed Elshafei, Foutse Khomh, Diego
Elias Costa | Towards Assessing Deep Learning Test Input Generators | Accepted to EASE 2025 | null | null | null | cs.LG cs.CV cs.SE | http://creativecommons.org/licenses/by/4.0/ | Deep Learning (DL) systems are increasingly deployed in safety-critical
applications, yet they remain vulnerable to robustness issues that can lead to
significant failures. While numerous Test Input Generators (TIGs) have been
developed to evaluate DL robustness, a comprehensive assessment of their
effectiveness acro... | [
{
"version": "v1",
"created": "Thu, 3 Apr 2025 07:06:55 GMT"
},
{
"version": "v2",
"created": "Mon, 7 Apr 2025 18:35:13 GMT"
}
] | 2025-04-09T00:00:00 | [
[
"Mzoughi",
"Seif",
""
],
[
"yahmed",
"Ahmed Haj",
""
],
[
"Elshafei",
"Mohamed",
""
],
[
"Khomh",
"Foutse",
""
],
[
"Costa",
"Diego Elias",
""
]
] | TITLE: Towards Assessing Deep Learning Test Input Generators
ABSTRACT: Deep Learning (DL) systems are increasingly deployed in safety-critical
applications, yet they remain vulnerable to robustness issues that can lead to
significant failures. While numerous Test Input Generators (TIGs) have been
developed to evalu... |
2504.02971 | Shaoyuan Xu Ph.D. | Binh M. Le, Shaoyuan Xu, Jinmiao Fu, Zhishen Huang, Moyan Li, Yanhui
Guo, Hongdong Li, Sameera Ramasinghe, Bryan Wang | QID: Efficient Query-Informed ViTs in Data-Scarce Regimes for OCR-free
Visual Document Understanding | 8 pages, accepted by CVPR 2025 MULA | null | null | null | cs.CV cs.CL | http://creativecommons.org/licenses/by/4.0/ | In Visual Document Understanding (VDU) tasks, fine-tuning a pre-trained
Vision-Language Model (VLM) with new datasets often falls short in optimizing
the vision encoder to identify query-specific regions in text-rich document
images. Existing methods that directly inject queries into model layers by
modifying the net... | [
{
"version": "v1",
"created": "Thu, 3 Apr 2025 18:47:16 GMT"
},
{
"version": "v2",
"created": "Mon, 7 Apr 2025 17:58:44 GMT"
}
] | 2025-04-09T00:00:00 | [
[
"Le",
"Binh M.",
""
],
[
"Xu",
"Shaoyuan",
""
],
[
"Fu",
"Jinmiao",
""
],
[
"Huang",
"Zhishen",
""
],
[
"Li",
"Moyan",
""
],
[
"Guo",
"Yanhui",
""
],
[
"Li",
"Hongdong",
""
],
[
"Ramasinghe",
"S... | TITLE: QID: Efficient Query-Informed ViTs in Data-Scarce Regimes for OCR-free
Visual Document Understanding
ABSTRACT: In Visual Document Understanding (VDU) tasks, fine-tuning a pre-trained
Vision-Language Model (VLM) with new datasets often falls short in optimizing
the vision encoder to identify query-specific ... |
2504.03809 | Niclas Boehmer | Stanis{\l}aw Szufa, Niclas Boehmer, Robert Bredereck, Piotr
Faliszewski, Rolf Niedermeier, Piotr Skowron, Arkadii Slinko, Nimrod Talmon | Drawing a Map of Elections | Journal article merging results from arxiv:2105.07815,
arXiv:2407.11889 and Szufa et al., "Drawing a Map of Elections in the Space
of Statistical Cultures", AAMAS '20 | null | 10.1016/j.artint.2025.104332 | null | cs.MA cs.AI cs.GT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Our main contribution is the introduction of the map of elections framework.
A map of elections consists of three main elements: (1) a dataset of elections
(i.e., collections of ordinal votes over given sets of candidates), (2) a way
of measuring similarities between these elections, and (3) a representation of
the e... | [
{
"version": "v1",
"created": "Fri, 4 Apr 2025 11:44:56 GMT"
},
{
"version": "v2",
"created": "Tue, 8 Apr 2025 10:52:54 GMT"
}
] | 2025-04-09T00:00:00 | [
[
"Szufa",
"Stanisław",
""
],
[
"Boehmer",
"Niclas",
""
],
[
"Bredereck",
"Robert",
""
],
[
"Faliszewski",
"Piotr",
""
],
[
"Niedermeier",
"Rolf",
""
],
[
"Skowron",
"Piotr",
""
],
[
"Slinko",
"Arkadii",
""
... | TITLE: Drawing a Map of Elections
ABSTRACT: Our main contribution is the introduction of the map of elections framework.
A map of elections consists of three main elements: (1) a dataset of elections
(i.e., collections of ordinal votes over given sets of candidates), (2) a way
of measuring similarities between thes... |
2504.03814 | Grgur Kova\v{c} | Grgur Kova\v{c}, J\'er\'emy Perez, R\'emy Portelas, Peter Ford
Dominey, Pierre-Yves Oudeyer | Recursive Training Loops in LLMs: How training data properties modulate
distribution shift in generated data? | null | null | null | null | cs.LG cs.AI cs.CL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Large language models (LLMs) are increasingly contributing to the creation of
content on the Internet. This creates a feedback loop as subsequent generations
of models will be trained on this generated, synthetic data. This phenomenon is
receiving increasing interest, in particular because previous studies have
shown... | [
{
"version": "v1",
"created": "Fri, 4 Apr 2025 14:41:41 GMT"
},
{
"version": "v2",
"created": "Tue, 8 Apr 2025 08:45:26 GMT"
}
] | 2025-04-09T00:00:00 | [
[
"Kovač",
"Grgur",
""
],
[
"Perez",
"Jérémy",
""
],
[
"Portelas",
"Rémy",
""
],
[
"Dominey",
"Peter Ford",
""
],
[
"Oudeyer",
"Pierre-Yves",
""
]
] | TITLE: Recursive Training Loops in LLMs: How training data properties modulate
distribution shift in generated data?
ABSTRACT: Large language models (LLMs) are increasingly contributing to the creation of
content on the Internet. This creates a feedback loop as subsequent generations
of models will be trained on ... |
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