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Is Your Model Fairly Certain? Uncertainty-Aware Fairness Evaluation for
LLMs | 2505.23996v1 | levesque2012winograd | \cite{levesque2012winograd} | The Defeat of the Winograd Schema Challenge | http://arxiv.org/abs/2201.02387v3 | The Winograd Schema Challenge - a set of twin sentences involving pronoun
reference disambiguation that seem to require the use of commonsense knowledge
- was proposed by Hector Levesque in 2011. By 2019, a number of AI systems,
based on large pre-trained transformer-based language models and fine-tuned on
these kinds ... | true | true | Levesque, Hector and Davis, Ernest and Morgenstern, Leora | 2,012 | null | null | null | null | The Defeat of the Winograd Schema Challenge | The Defeat of the Winograd Schema Challenge | http://arxiv.org/pdf/2201.02387v3 | The Winograd Schema Challenge - a set of twin sentences involving pronoun
reference disambiguation that seem to require the use of commonsense knowledge
- was proposed by Hector Levesque in 2011. By 2019, a number of AI systems,
based on large pre-trained transformer-based language models and fine-tuned on
these kinds ... |
Is Your Model Fairly Certain? Uncertainty-Aware Fairness Evaluation for
LLMs | 2505.23996v1 | zhao2018gender | \cite{zhao2018gender} | Gender Bias in Coreference Resolution: Evaluation and Debiasing Methods | http://arxiv.org/abs/1804.06876v1 | We introduce a new benchmark, WinoBias, for coreference resolution focused on
gender bias. Our corpus contains Winograd-schema style sentences with entities
corresponding to people referred by their occupation (e.g. the nurse, the
doctor, the carpenter). We demonstrate that a rule-based, a feature-rich, and a
neural co... | true | true | Zhao, Jieyu and Wang, Tianlu and Yatskar, Mark and Ordonez, Vicente and Chang, Kai-Wei | 2,018 | null | null | null | null | Gender Bias in Coreference Resolution: Evaluation and Debiasing Methods | Gender Bias in Coreference Resolution: Evaluation and Debiasing Methods | http://arxiv.org/pdf/1804.06876v1 | We introduce a new benchmark, WinoBias, for coreference resolution focused on
gender bias. Our corpus contains Winograd-schema style sentences with entities
corresponding to people referred by their occupation (e.g. the nurse, the
doctor, the carpenter). We demonstrate that a rule-based, a feature-rich, and a
neural co... |
Is Your Model Fairly Certain? Uncertainty-Aware Fairness Evaluation for
LLMs | 2505.23996v1 | vanmassenhove2021neutral | \cite{vanmassenhove2021neutral} | NeuTral Rewriter: A Rule-Based and Neural Approach to Automatic
Rewriting into Gender-Neutral Alternatives | http://arxiv.org/abs/2109.06105v1 | Recent years have seen an increasing need for gender-neutral and inclusive
language. Within the field of NLP, there are various mono- and bilingual use
cases where gender inclusive language is appropriate, if not preferred due to
ambiguity or uncertainty in terms of the gender of referents. In this work, we
present a r... | true | true | Vanmassenhove, Eva and Emmery, Chris and Shterionov, Dimitar | 2,021 | null | null | null | null | NeuTral Rewriter: A Rule-Based and Neural Approach to Automatic
Rewriting into Gender-Neutral Alternatives | NeuTral Rewriter: A Rule-Based and Neural Approach to Automatic ... | https://www.researchgate.net/publication/357122955_NeuTral_Rewriter_A_Rule-Based_and_Neural_Approach_to_Automatic_Rewriting_into_Gender_Neutral_Alternatives | Our work falls Round-trip translation (from gender-neural to gender-biased) and neural text paraphrasing German [18] Rule-based gender rewriting |
Is Your Model Fairly Certain? Uncertainty-Aware Fairness Evaluation for
LLMs | 2505.23996v1 | rudinger2018gender | \cite{rudinger2018gender} | Gender Bias in Coreference Resolution | http://arxiv.org/abs/1804.09301v1 | We present an empirical study of gender bias in coreference resolution
systems. We first introduce a novel, Winograd schema-style set of minimal pair
sentences that differ only by pronoun gender. With these "Winogender schemas,"
we evaluate and confirm systematic gender bias in three publicly-available
coreference reso... | true | true | Rudinger, Rachel and Naradowsky, Jason and Leonard, Brian and Van Durme, Benjamin | 2,018 | null | null | null | null | Gender Bias in Coreference Resolution | Gender Bias in Coreference Resolution | http://arxiv.org/pdf/1804.09301v1 | We present an empirical study of gender bias in coreference resolution
systems. We first introduce a novel, Winograd schema-style set of minimal pair
sentences that differ only by pronoun gender. With these "Winogender schemas,"
we evaluate and confirm systematic gender bias in three publicly-available
coreference reso... |
Is Your Model Fairly Certain? Uncertainty-Aware Fairness Evaluation for
LLMs | 2505.23996v1 | srivastava2023beyond | \cite{srivastava2023beyond} | Beyond the Imitation Game: Quantifying and extrapolating the
capabilities of language models | http://arxiv.org/abs/2206.04615v3 | Language models demonstrate both quantitative improvement and new qualitative
capabilities with increasing scale. Despite their potentially transformative
impact, these new capabilities are as yet poorly characterized. In order to
inform future research, prepare for disruptive new model capabilities, and
ameliorate soc... | true | true | {BIG-bench authors} | 2,023 | null | null | null | TMLR | Beyond the Imitation Game: Quantifying and extrapolating the
capabilities of language models | Quantifying and extrapolating the capabilities of language models | https://openreview.net/forum?id=uyTL5Bvosj | The paper introduces the Beyond the Imitation Game benchmark (BIG-bench) as a way to better understand the current and near-future capabilities and limitations |
Is Your Model Fairly Certain? Uncertainty-Aware Fairness Evaluation for
LLMs | 2505.23996v1 | dhamala2021bold | \cite{dhamala2021bold} | BOLD: Dataset and Metrics for Measuring Biases in Open-Ended Language
Generation | http://arxiv.org/abs/2101.11718v1 | Recent advances in deep learning techniques have enabled machines to generate
cohesive open-ended text when prompted with a sequence of words as context.
While these models now empower many downstream applications from conversation
bots to automatic storytelling, they have been shown to generate texts that
exhibit soci... | true | true | Dhamala, Jwala and Sun, Tony and Kumar, Varun and Krishna, Satyapriya and Pruksachatkun, Yada and Chang, Kai-Wei and Gupta, Rahul | 2,021 | null | null | null | null | BOLD: Dataset and Metrics for Measuring Biases in Open-Ended Language
Generation | Bias in Open-ended Language Generation Dataset (BOLD) - GitHub | https://github.com/amazon-science/bold | Bias in Open-ended Language Generation Dataset (BOLD) is a dataset to evaluate fairness in open-ended language generation in English language. |
Is Your Model Fairly Certain? Uncertainty-Aware Fairness Evaluation for
LLMs | 2505.23996v1 | kotek2023gender | \cite{kotek2023gender} | Gender bias and stereotypes in Large Language Models | http://arxiv.org/abs/2308.14921v1 | Large Language Models (LLMs) have made substantial progress in the past
several months, shattering state-of-the-art benchmarks in many domains. This
paper investigates LLMs' behavior with respect to gender stereotypes, a known
issue for prior models. We use a simple paradigm to test the presence of gender
bias, buildin... | true | true | Kotek, Hadas and Dockum, Rikker and Sun, David | 2,023 | null | null | null | null | Gender bias and stereotypes in Large Language Models | Gender bias and stereotypes in Large Language Models | http://arxiv.org/pdf/2308.14921v1 | Large Language Models (LLMs) have made substantial progress in the past
several months, shattering state-of-the-art benchmarks in many domains. This
paper investigates LLMs' behavior with respect to gender stereotypes, a known
issue for prior models. We use a simple paradigm to test the presence of gender
bias, buildin... |
Is Your Model Fairly Certain? Uncertainty-Aware Fairness Evaluation for
LLMs | 2505.23996v1 | parrish2021bbq | \cite{parrish2021bbq} | BBQ: A Hand-Built Bias Benchmark for Question Answering | http://arxiv.org/abs/2110.08193v2 | It is well documented that NLP models learn social biases, but little work
has been done on how these biases manifest in model outputs for applied tasks
like question answering (QA). We introduce the Bias Benchmark for QA (BBQ), a
dataset of question sets constructed by the authors that highlight attested
social biases... | true | true | Parrish, Alicia and Chen, Angelica and Nangia, Nikita and Padmakumar, Vishakh and Phang, Jason and Thompson, Jana and Htut, Phu Mon and Bowman, Samuel R | 2,021 | null | null | null | null | BBQ: A Hand-Built Bias Benchmark for Question Answering | BBQ: A hand-built bias benchmark for question answering | https://aclanthology.org/2022.findings-acl.165/ | by A Parrish · 2022 · Cited by 512 — We introduce the Bias Benchmark for QA (BBQ), a dataset of question-sets constructed by the authors that highlight attested social biases. |
Is Your Model Fairly Certain? Uncertainty-Aware Fairness Evaluation for
LLMs | 2505.23996v1 | webster-etal-2018-mind | \cite{webster-etal-2018-mind} | Mind the GAP: A Balanced Corpus of Gendered Ambiguous Pronouns | http://arxiv.org/abs/1810.05201v1 | Coreference resolution is an important task for natural language
understanding, and the resolution of ambiguous pronouns a longstanding
challenge. Nonetheless, existing corpora do not capture ambiguous pronouns in
sufficient volume or diversity to accurately indicate the practical utility of
models. Furthermore, we fin... | true | true | Webster, Kellie and
Recasens, Marta and
Axelrod, Vera and
Baldridge, Jason | 2,018 | null | null | null | Transactions of the Association for Computational Linguistics | Mind the GAP: A Balanced Corpus of Gendered Ambiguous Pronouns | Mind the GAP: A Balanced Corpus of Gendered Ambiguous Pronouns | http://arxiv.org/pdf/1810.05201v1 | Coreference resolution is an important task for natural language
understanding, and the resolution of ambiguous pronouns a longstanding
challenge. Nonetheless, existing corpora do not capture ambiguous pronouns in
sufficient volume or diversity to accurately indicate the practical utility of
models. Furthermore, we fin... |
Is Your Model Fairly Certain? Uncertainty-Aware Fairness Evaluation for
LLMs | 2505.23996v1 | pant-dadu-2022-incorporating | \cite{pant-dadu-2022-incorporating} | Incorporating Subjectivity into Gendered Ambiguous Pronoun ({GAP}) Resolution using Style Transfer | null | null | true | false | Pant, Kartikey and
Dadu, Tanvi | 2,022 | null | null | null | null | Incorporating Subjectivity into Gendered Ambiguous Pronoun ({GAP}) Resolution using Style Transfer | Incorporating Subjectivity into Gendered Ambiguous Pronoun (GAP ... | https://www.researchgate.net/publication/362266417_Incorporating_Subjectivity_into_Gendered_Ambiguous_Pronoun_GAP_Resolution_using_Style_Transfer | Incorporating Subjectivity into Gendered Ambiguous Pronoun (GAP) Resolution using Style Transfer ... GAP-Subjective is the same size as GAP, with 8,908 instances. |
Is Your Model Fairly Certain? Uncertainty-Aware Fairness Evaluation for
LLMs | 2505.23996v1 | levy-etal-2021-collecting-large | \cite{levy-etal-2021-collecting-large} | Collecting a Large-Scale Gender Bias Dataset for Coreference Resolution
and Machine Translation | http://arxiv.org/abs/2109.03858v2 | Recent works have found evidence of gender bias in models of machine
translation and coreference resolution using mostly synthetic diagnostic
datasets. While these quantify bias in a controlled experiment, they often do
so on a small scale and consist mostly of artificial, out-of-distribution
sentences. In this work, w... | true | true | Levy, Shahar and
Lazar, Koren and
Stanovsky, Gabriel | 2,021 | null | null | null | null | Collecting a Large-Scale Gender Bias Dataset for Coreference Resolution
and Machine Translation | [PDF] Collecting a Large-Scale Gender Bias Dataset for Coreference ... | https://aclanthology.org/2021.findings-emnlp.211.pdf | We use BUG to evaluate gender bias in various coref- erence resolution and machine translation models, finding that models tend to make |
Is Your Model Fairly Certain? Uncertainty-Aware Fairness Evaluation for
LLMs | 2505.23996v1 | gawlikowski2023survey | \cite{gawlikowski2023survey} | A Survey of Uncertainty in Deep Neural Networks | http://arxiv.org/abs/2107.03342v3 | Due to their increasing spread, confidence in neural network predictions
became more and more important. However, basic neural networks do not deliver
certainty estimates or suffer from over or under confidence. Many researchers
have been working on understanding and quantifying uncertainty in a neural
network's predic... | true | true | Gawlikowski, Jakob and Tassi, Cedrique Rovile Njieutcheu and Ali, Mohsin and Lee, Jongseok and Humt, Matthias and Feng, Jianxiang and Kruspe, Anna and Triebel, Rudolph and Jung, Peter and Roscher, Ribana and others | 2,023 | null | null | null | Artificial Intelligence Review | A Survey of Uncertainty in Deep Neural Networks | A Survey of Uncertainty in Deep Neural Networks | http://arxiv.org/pdf/2107.03342v3 | Due to their increasing spread, confidence in neural network predictions
became more and more important. However, basic neural networks do not deliver
certainty estimates or suffer from over or under confidence. Many researchers
have been working on understanding and quantifying uncertainty in a neural
network's predic... |
Is Your Model Fairly Certain? Uncertainty-Aware Fairness Evaluation for
LLMs | 2505.23996v1 | hu2023uncertainty | \cite{hu2023uncertainty} | Uncertainty in Natural Language Processing: Sources, Quantification, and
Applications | http://arxiv.org/abs/2306.04459v1 | As a main field of artificial intelligence, natural language processing (NLP)
has achieved remarkable success via deep neural networks. Plenty of NLP tasks
have been addressed in a unified manner, with various tasks being associated
with each other through sharing the same paradigm. However, neural networks are
black b... | true | true | Hu, Mengting and Zhang, Zhen and Zhao, Shiwan and Huang, Minlie and Wu, Bingzhe | 2,023 | null | null | null | arXiv preprint arXiv:2306.04459 | Uncertainty in Natural Language Processing: Sources, Quantification, and
Applications | [PDF] Uncertainty in Natural Language Processing: Sources ... - arXiv | https://arxiv.org/pdf/2306.04459 | Then, we systemically review uncertainty quantification approaches and the main applications. Finally, we discuss the challenges of uncertainty. |
Is Your Model Fairly Certain? Uncertainty-Aware Fairness Evaluation for
LLMs | 2505.23996v1 | huang2023look | \cite{huang2023look} | Look Before You Leap: An Exploratory Study of Uncertainty Measurement
for Large Language Models | http://arxiv.org/abs/2307.10236v4 | The recent performance leap of Large Language Models (LLMs) opens up new
opportunities across numerous industrial applications and domains. However,
erroneous generations, such as false predictions, misinformation, and
hallucination made by LLMs, have also raised severe concerns for the
trustworthiness of LLMs', especi... | true | true | Huang, Yuheng and Song, Jiayang and Wang, Zhijie and Zhao, Shengming and Chen, Huaming and Juefei-Xu, Felix and Ma, Lei | 2,023 | null | null | null | arXiv preprint arXiv:2307.10236 | Look Before You Leap: An Exploratory Study of Uncertainty Measurement
for Large Language Models | Look Before You Leap: An Exploratory Study of Uncertainty ... - arXiv | https://arxiv.org/abs/2307.10236 | The recent performance leap of Large Language Models (LLMs) opens up new opportunities across numerous industrial applications and domains. |
Is Your Model Fairly Certain? Uncertainty-Aware Fairness Evaluation for
LLMs | 2505.23996v1 | fadeeva2023lm | \cite{fadeeva2023lm} | LM-polygraph: Uncertainty estimation for language models | null | null | true | false | Fadeeva, Ekaterina and Vashurin, Roman and Tsvigun, Akim and Vazhentsev, Artem and Petrakov, Sergey and Fedyanin, Kirill and Vasilev, Daniil and Goncharova, Elizaveta and Panchenko, Alexander and Panov, Maxim and others | 2,023 | null | null | null | null | LM-polygraph: Uncertainty estimation for language models | LM-Polygraph: Uncertainty Estimation for Language Models | http://arxiv.org/pdf/2311.07383v1 | Recent advancements in the capabilities of large language models (LLMs) have
paved the way for a myriad of groundbreaking applications in various fields.
However, a significant challenge arises as these models often "hallucinate",
i.e., fabricate facts without providing users an apparent means to discern the
veracity o... |
Is Your Model Fairly Certain? Uncertainty-Aware Fairness Evaluation for
LLMs | 2505.23996v1 | kendall2017uncertainties | \cite{kendall2017uncertainties} | What Uncertainties Do We Need in Bayesian Deep Learning for Computer
Vision? | http://arxiv.org/abs/1703.04977v2 | There are two major types of uncertainty one can model. Aleatoric uncertainty
captures noise inherent in the observations. On the other hand, epistemic
uncertainty accounts for uncertainty in the model -- uncertainty which can be
explained away given enough data. Traditionally it has been difficult to model
epistemic u... | true | true | Kendall, Alex and Gal, Yarin | 2,017 | null | null | null | NeurIPS | What Uncertainties Do We Need in Bayesian Deep Learning for Computer
Vision? | [PDF] What Uncertainties Do We Need in Bayesian Deep Learning ... - NIPS | http://papers.neurips.cc/paper/7141-what-uncertainties-do-we-need-in-bayesian-deep-learning-for-computer-vision.pdf | Quantifying uncertainty in computer vision applications can be largely divided into regression set- tings such as depth regression, and classification settings |
Is Your Model Fairly Certain? Uncertainty-Aware Fairness Evaluation for
LLMs | 2505.23996v1 | bridle1990probabilistic | \cite{bridle1990probabilistic} | Probabilistic interpretation of feedforward classification network outputs, with relationships to statistical pattern recognition | null | null | true | false | Bridle, John S | 1,990 | null | null | null | null | Probabilistic interpretation of feedforward classification network outputs, with relationships to statistical pattern recognition | PROBABILISTIC INTERPRETATION OF FEEDFORWARD ... | https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=818b3279ba393e0c0aeea200652199e8f4c59942 | by M COSTA · Cited by 37 — J. S. Bridle 1989, \Probabilistic interpretation of feedforward classi cation network outputs, with rela- tionships to statistical pattern recognition," in Neu-. |
Is Your Model Fairly Certain? Uncertainty-Aware Fairness Evaluation for
LLMs | 2505.23996v1 | hendrycks2017a | \cite{hendrycks2017a} | A Baseline for Detecting Misclassified and Out-of-Distribution Examples
in Neural Networks | http://arxiv.org/abs/1610.02136v3 | We consider the two related problems of detecting if an example is
misclassified or out-of-distribution. We present a simple baseline that
utilizes probabilities from softmax distributions. Correctly classified
examples tend to have greater maximum softmax probabilities than erroneously
classified and out-of-distributi... | true | true | Dan Hendrycks and Kevin Gimpel | 2,017 | null | null | null | null | A Baseline for Detecting Misclassified and Out-of-Distribution Examples
in Neural Networks | A Baseline for Detecting Misclassified and Out-of- ... | https://arxiv.org/abs/1610.02136 | by D Hendrycks · 2016 · Cited by 4553 — We present a simple baseline that utilizes probabilities from softmax distributions. Correctly classified examples tend to have greater maximum softmax |
Is Your Model Fairly Certain? Uncertainty-Aware Fairness Evaluation for
LLMs | 2505.23996v1 | jurafsky2000speech | \cite{jurafsky2000speech} | Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition | null | null | true | false | Jurafsky, Daniel and Martin, James H | 2,000 | null | null | null | null | Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition | Speech and Language Processing: An Introduction to Natural ... | https://www.amazon.com/Speech-Language-Processing-Introduction-Computational/dp/0130950696 | An introduction to natural language processing, computational linguistics and speech recognition. ISBN-13: 978-0130950697, ISBN-10: 0130950696. |
Is Your Model Fairly Certain? Uncertainty-Aware Fairness Evaluation for
LLMs | 2505.23996v1 | fomicheva2020unsupervised | \cite{fomicheva2020unsupervised} | Unsupervised Quality Estimation for Neural Machine Translation | http://arxiv.org/abs/2005.10608v2 | Quality Estimation (QE) is an important component in making Machine
Translation (MT) useful in real-world applications, as it is aimed to inform
the user on the quality of the MT output at test time. Existing approaches
require large amounts of expert annotated data, computation and time for
training. As an alternative... | true | true | Fomicheva, Marina and Sun, Shuo and Yankovskaya, Lisa and Blain, Fr{\'e}d{\'e}ric and Guzm{\'a}n, Francisco and Fishel, Mark and Aletras, Nikolaos and Chaudhary, Vishrav and Specia, Lucia | 2,020 | null | null | null | null | Unsupervised Quality Estimation for Neural Machine Translation | Unsupervised Quality Estimation for Neural Machine Translation | http://arxiv.org/pdf/2005.10608v2 | Quality Estimation (QE) is an important component in making Machine
Translation (MT) useful in real-world applications, as it is aimed to inform
the user on the quality of the MT output at test time. Existing approaches
require large amounts of expert annotated data, computation and time for
training. As an alternative... |
Is Your Model Fairly Certain? Uncertainty-Aware Fairness Evaluation for
LLMs | 2505.23996v1 | malinin2021uncertainty | \cite{malinin2021uncertainty} | Uncertainty Estimation in Autoregressive Structured Prediction | http://arxiv.org/abs/2002.07650v5 | Uncertainty estimation is important for ensuring safety and robustness of AI
systems. While most research in the area has focused on un-structured
prediction tasks, limited work has investigated general uncertainty estimation
approaches for structured prediction. Thus, this work aims to investigate
uncertainty estimati... | true | true | Malinin, Andrey and Gales, Mark | 2,021 | null | null | null | null | Uncertainty Estimation in Autoregressive Structured Prediction | Uncertainty Estimation in Autoregressive Structured Prediction | http://arxiv.org/pdf/2002.07650v5 | Uncertainty estimation is important for ensuring safety and robustness of AI
systems. While most research in the area has focused on un-structured
prediction tasks, limited work has investigated general uncertainty estimation
approaches for structured prediction. Thus, this work aims to investigate
uncertainty estimati... |
Is Your Model Fairly Certain? Uncertainty-Aware Fairness Evaluation for
LLMs | 2505.23996v1 | vovk2005algorithmic | \cite{vovk2005algorithmic} | Algorithmic learning in a random world | null | null | true | false | Vovk, Vladimir and Gammerman, Alexander and Shafer, Glenn | 2,005 | null | null | null | null | Algorithmic learning in a random world | Algorithmic Learning in a Random World | https://www.amazon.ca/Algorithmic-Learning-Random-World-Vladimir/dp/0387001522 | Algorithmic Learning in a Random Worlddescribes recent theoretical and experimental developments in building computable approximations to Kolmogorov's |
Is Your Model Fairly Certain? Uncertainty-Aware Fairness Evaluation for
LLMs | 2505.23996v1 | gal2016dropout | \cite{gal2016dropout} | Dropout as a Bayesian Approximation: Representing Model Uncertainty in
Deep Learning | http://arxiv.org/abs/1506.02142v6 | Deep learning tools have gained tremendous attention in applied machine
learning. However such tools for regression and classification do not capture
model uncertainty. In comparison, Bayesian models offer a mathematically
grounded framework to reason about model uncertainty, but usually come with a
prohibitive computa... | true | true | Gal, Yarin and Ghahramani, Zoubin | 2,016 | null | null | null | null | Dropout as a Bayesian Approximation: Representing Model Uncertainty in
Deep Learning | Representing Model Uncertainty in Deep Learning - arXiv | https://arxiv.org/abs/1506.02142 | In this paper we develop a new theoretical framework casting dropout training in deep neural networks (NNs) as approximate Bayesian inference in deep Gaussian |
Is Your Model Fairly Certain? Uncertainty-Aware Fairness Evaluation for
LLMs | 2505.23996v1 | yu2022learning | \cite{yu2022learning} | Learning Uncertainty for Unknown Domains with Zero-Target-Assumption | null | null | true | false | Yu, Yu and Sajjad, Hassan and Xu, Jia | 2,022 | null | null | null | null | Learning Uncertainty for Unknown Domains with Zero-Target-Assumption | Learning Uncertainty for Unknown Domains with Zero-Target ... | https://openreview.net/forum?id=pWVASryOyFw | In this paper, the authors propose to use a Maximum-Entropy Rewarded Reinforcement Learning framework to select training data for NLP tasks, the goal of which is to maximize generalization. Weaknesses: The authors only proved the role of entropy in selecting data, but this paper does not elaborate on the motivation and... |
Is Your Model Fairly Certain? Uncertainty-Aware Fairness Evaluation for
LLMs | 2505.23996v1 | kuhn2023semantic | \cite{kuhn2023semantic} | Semantic Uncertainty: Linguistic Invariances for Uncertainty Estimation
in Natural Language Generation | http://arxiv.org/abs/2302.09664v3 | We introduce a method to measure uncertainty in large language models. For
tasks like question answering, it is essential to know when we can trust the
natural language outputs of foundation models. We show that measuring
uncertainty in natural language is challenging because of "semantic
equivalence" -- different sent... | true | true | Kuhn, Lorenz and Gal, Yarin and Farquhar, Sebastian | 2,023 | null | null | null | null | Semantic Uncertainty: Linguistic Invariances for Uncertainty Estimation
in Natural Language Generation | Semantic Uncertainty: Linguistic Invariances for ... - OpenReview | https://openreview.net/forum?id=VD-AYtP0dve | Summary: The paper proposes an approach called semantic entropy, which incorporates linguistic invariances for uncertainty estimation in NLG. |
Is Your Model Fairly Certain? Uncertainty-Aware Fairness Evaluation for
LLMs | 2505.23996v1 | duan2023shifting | \cite{duan2023shifting} | Shifting Attention to Relevance: Towards the Predictive Uncertainty
Quantification of Free-Form Large Language Models | http://arxiv.org/abs/2307.01379v3 | Large Language Models (LLMs) show promising results in language generation
and instruction following but frequently "hallucinate", making their outputs
less reliable. Despite Uncertainty Quantification's (UQ) potential solutions,
implementing it accurately within LLMs is challenging. Our research introduces
a simple he... | true | true | Duan, Jinhao and Cheng, Hao and Wang, Shiqi and Wang, Chenan and Zavalny, Alex and Xu, Renjing and Kailkhura, Bhavya and Xu, Kaidi | 2,024 | null | null | null | null | Shifting Attention to Relevance: Towards the Predictive Uncertainty
Quantification of Free-Form Large Language Models | Shifting Attention to Relevance: Towards the Predictive ... | https://arxiv.org/abs/2307.01379 | by J Duan · 2023 · Cited by 172 — Shifting Attention to Relevance: Towards the Predictive Uncertainty Quantification of Free-Form Large Language Models. Authors:Jinhao Duan, Hao |
Is Your Model Fairly Certain? Uncertainty-Aware Fairness Evaluation for
LLMs | 2505.23996v1 | kadavath2022language | \cite{kadavath2022language} | Language Models (Mostly) Know What They Know | http://arxiv.org/abs/2207.05221v4 | We study whether language models can evaluate the validity of their own
claims and predict which questions they will be able to answer correctly. We
first show that larger models are well-calibrated on diverse multiple choice
and true/false questions when they are provided in the right format. Thus we
can approach self... | true | true | Kadavath, Saurav and Conerly, Tom and Askell, Amanda and Henighan, Tom and Drain, Dawn and Perez, Ethan and Schiefer, Nicholas and Hatfield-Dodds, Zac and DasSarma, Nova and Tran-Johnson, Eli and others | 2,022 | null | null | null | arXiv preprint arXiv:2207.05221 | Language Models (Mostly) Know What They Know | Language Models (Mostly) Know What They Know | http://arxiv.org/pdf/2207.05221v4 | We study whether language models can evaluate the validity of their own
claims and predict which questions they will be able to answer correctly. We
first show that larger models are well-calibrated on diverse multiple choice
and true/false questions when they are provided in the right format. Thus we
can approach self... |
Is Your Model Fairly Certain? Uncertainty-Aware Fairness Evaluation for
LLMs | 2505.23996v1 | malinin2018predictive | \cite{malinin2018predictive} | Predictive Uncertainty Estimation via Prior Networks | http://arxiv.org/abs/1802.10501v4 | Estimating how uncertain an AI system is in its predictions is important to
improve the safety of such systems. Uncertainty in predictive can result from
uncertainty in model parameters, irreducible data uncertainty and uncertainty
due to distributional mismatch between the test and training data
distributions. Differe... | true | true | Malinin, Andrey and Gales, Mark | 2,018 | null | null | null | null | Predictive Uncertainty Estimation via Prior Networks | Predictive Uncertainty Estimation via Prior Networks | http://arxiv.org/pdf/1802.10501v4 | Estimating how uncertain an AI system is in its predictions is important to
improve the safety of such systems. Uncertainty in predictive can result from
uncertainty in model parameters, irreducible data uncertainty and uncertainty
due to distributional mismatch between the test and training data
distributions. Differe... |
Is Your Model Fairly Certain? Uncertainty-Aware Fairness Evaluation for
LLMs | 2505.23996v1 | darrin2022rainproof | \cite{darrin2022rainproof} | Rainproof: An Umbrella To Shield Text Generators From
Out-Of-Distribution Data | http://arxiv.org/abs/2212.09171v2 | Implementing effective control mechanisms to ensure the proper functioning
and security of deployed NLP models, from translation to chatbots, is
essential. A key ingredient to ensure safe system behaviour is
Out-Of-Distribution (OOD) detection, which aims to detect whether an input
sample is statistically far from the ... | true | true | Darrin, Maxime and Piantanida, Pablo and Colombo, Pierre | 2,023 | null | null | null | null | Rainproof: An Umbrella To Shield Text Generators From
Out-Of-Distribution Data | RAINPROOF: An umbrella to shield text generators from ... | https://aclanthology.org/2023.emnlp-main.357.pdf | by M Darrin · 2023 · Cited by 39 — RAINPROOF is a Relative informAItioN Projection OOD detection framework that shields text generators from out-of-distribution data, using soft- |
Is Your Model Fairly Certain? Uncertainty-Aware Fairness Evaluation for
LLMs | 2505.23996v1 | vashurin2025benchmarking | \cite{vashurin2025benchmarking} | Benchmarking uncertainty quantification methods for large language models with lm-polygraph | null | null | true | false | Vashurin, Roman and Fadeeva, Ekaterina and Vazhentsev, Artem and Rvanova, Lyudmila and Vasilev, Daniil and Tsvigun, Akim and Petrakov, Sergey and Xing, Rui and Sadallah, Abdelrahman and Grishchenkov, Kirill and others | 2,025 | null | null | null | Transactions of the Association for Computational Linguistics | Benchmarking uncertainty quantification methods for large language models with lm-polygraph | Benchmarking Uncertainty Quantification Methods for Large ... | https://direct.mit.edu/tacl/article/doi/10.1162/tacl_a_00737/128713/Benchmarking-Uncertainty-Quantification-Methods | Benchmarking Uncertainty Quantification Methods for Large Language Models with LM-Polygraph | Transactions of the Association for Computational Linguistics | MIT Press Roman Vashurin, Ekaterina Fadeeva, Artem Vazhentsev, Lyudmila Rvanova, Daniil Vasilev, Akim Tsvigun, Sergey Petrakov, Rui Xing, Abdelrahman Sadallah, Ki... |
Is Your Model Fairly Certain? Uncertainty-Aware Fairness Evaluation for
LLMs | 2505.23996v1 | santilli2024spurious | \cite{santilli2024spurious} | On a spurious interaction between uncertainty scores and answer evaluation metrics in generative qa tasks | null | null | true | false | Santilli, Andrea and Xiong, Miao and Kirchhof, Michael and Rodriguez, Pau and Danieli, Federico and Suau, Xavier and Zappella, Luca and Williamson, Sinead and Golinski, Adam | 2,024 | null | null | null | null | On a spurious interaction between uncertainty scores and answer evaluation metrics in generative qa tasks | On a Spurious Interaction between Uncertainty Scores & ... | https://openreview.net/pdf?id=jGtL0JFdeD | by A Santilli · Cited by 3 — In this paper, we highlight that some UQ methods and answer evaluation metrics are spuriously correlated via the response length, which leads to falsely |
Is Your Model Fairly Certain? Uncertainty-Aware Fairness Evaluation for
LLMs | 2505.23996v1 | santilli2025revisiting | \cite{santilli2025revisiting} | Revisiting Uncertainty Quantification Evaluation in Language Models:
Spurious Interactions with Response Length Bias Results | http://arxiv.org/abs/2504.13677v2 | Uncertainty Quantification (UQ) in Language Models (LMs) is key to improving
their safety and reliability. Evaluations often use metrics like AUROC to
assess how well UQ methods (e.g., negative sequence probabilities) correlate
with task correctness functions (e.g., ROUGE-L). We show that mutual
biases--when both UQ me... | true | true | Santilli, Andrea and Golinski, Adam and Kirchhof, Michael and Danieli, Federico and Blaas, Arno and Xiong, Miao and Zappella, Luca and Williamson, Sinead | 2,025 | null | null | null | arXiv preprint arXiv:2504.13677 | Revisiting Uncertainty Quantification Evaluation in Language Models:
Spurious Interactions with Response Length Bias Results | Spurious Interactions with Response Length Bias Results | https://arxiv.org/pdf/2504.13677? | by A Santilli · 2025 · Cited by 3 — Uncertainty Quantification (UQ) in Language. Models (LMs) is key to improving their safety and reliability. Evaluations often use metrics. |
Is Your Model Fairly Certain? Uncertainty-Aware Fairness Evaluation for
LLMs | 2505.23996v1 | mehta2024evaluating | \cite{mehta2024evaluating} | Evaluating the Fairness of Deep Learning Uncertainty Estimates in
Medical Image Analysis | http://arxiv.org/abs/2303.03242v1 | Although deep learning (DL) models have shown great success in many medical
image analysis tasks, deployment of the resulting models into real clinical
contexts requires: (1) that they exhibit robustness and fairness across
different sub-populations, and (2) that the confidence in DL model predictions
be accurately exp... | true | true | Mehta, Raghav and Shui, Changjian and Arbel, Tal | 2,024 | null | null | null | null | Evaluating the Fairness of Deep Learning Uncertainty Estimates in
Medical Image Analysis | Evaluating the Fairness of Deep Learning Uncertainty Estimates in ... | https://arxiv.org/abs/2303.03242 | In this work, we present the first exploration of the effect of popular fairness models on overcoming biases across subgroups in medical image analysis. |
Is Your Model Fairly Certain? Uncertainty-Aware Fairness Evaluation for
LLMs | 2505.23996v1 | kuzmin-etal-2023-uncertainty | \cite{kuzmin-etal-2023-uncertainty} | Uncertainty Estimation for Debiased Models: Does Fairness Hurt Reliability? | null | null | true | false | Kuzmin, Gleb and
Vazhentsev, Artem and
Shelmanov, Artem and
Han, Xudong and
Suster, Simon and
Panov, Maxim and
Panchenko, Alexander and
Baldwin, Timothy | 2,023 | null | https://aclanthology.org/2023.ijcnlp-main.48/ | 10.18653/v1/2023.ijcnlp-main.48 | null | Uncertainty Estimation for Debiased Models: Does Fairness Hurt Reliability? | Uncertainty Estimation for Debiased Models: Does Fairness Hurt ... | https://aclanthology.org/2023.ijcnlp-main.48/ | Uncertainty Estimation for Debiased Models: Does Fairness Hurt Reliability?. In Proceedings of the 13th International Joint Conference on Natural Language |
Is Your Model Fairly Certain? Uncertainty-Aware Fairness Evaluation for
LLMs | 2505.23996v1 | kuzucu2023uncertainty | \cite{kuzucu2023uncertainty} | Uncertainty as a Fairness Measure | null | null | true | false | Kuzucu, Selim and Cheong, Jiaee and Gunes, Hatice and Kalkan, Sinan | 2,023 | null | null | null | arXiv preprint arXiv:2312.11299 | Uncertainty as a Fairness Measure | [2312.11299] Uncertainty-based Fairness Measures - arXiv | https://arxiv.org/abs/2312.11299 | We introduce new fairness measures based on different types of uncertainties, namely, aleatoric uncertainty and epistemic uncertainty. |
Is Your Model Fairly Certain? Uncertainty-Aware Fairness Evaluation for
LLMs | 2505.23996v1 | kaiser2022uncertainty | \cite{kaiser2022uncertainty} | Uncertainty-aware predictive modeling for fair data-driven decisions | null | null | true | false | Kaiser, Patrick and Kern, Christoph and R{\"u}gamer, David | 2,022 | null | null | null | arXiv preprint arXiv:2211.02730 | Uncertainty-aware predictive modeling for fair data-driven decisions | Uncertainty-aware predictive modeling for fair data-driven ... | https://openreview.net/forum?id=8DXj-ze0x_s | Uncertainty-aware predictive modeling for fair data-driven decisions | OpenReview Blind Submission by TSRML • Uncertainty-aware predictive modeling for fair data-driven decisions 23 Oct 2022, 01:52 NeurIPS 2022 Workshop TSRML Paper72 Decision Readers: EveryoneShow Revisions The authors highlight the importance of acco... |
Is Your Model Fairly Certain? Uncertainty-Aware Fairness Evaluation for
LLMs | 2505.23996v1 | tahir2023fairness | \cite{tahir2023fairness} | Fairness through Aleatoric Uncertainty | http://arxiv.org/abs/2304.03646v2 | We propose a simple yet effective solution to tackle the often-competing
goals of fairness and utility in classification tasks. While fairness ensures
that the model's predictions are unbiased and do not discriminate against any
particular group or individual, utility focuses on maximizing the model's
predictive perfor... | true | true | Tahir, Anique and Cheng, Lu and Liu, Huan | 2,023 | null | null | null | null | Fairness through Aleatoric Uncertainty | Fairness through Aleatoric Uncertainty | http://arxiv.org/pdf/2304.03646v2 | We propose a simple yet effective solution to tackle the often-competing
goals of fairness and utility in classification tasks. While fairness ensures
that the model's predictions are unbiased and do not discriminate against any
particular group or individual, utility focuses on maximizing the model's
predictive perfor... |
Synthetic Generation and Latent Projection Denoising of Rim Lesions in
Multiple Sclerosis | 2505.23353v1 | Mcal | \cite{Mcal} | Synthetic quantitative MRI through relaxometry modelling | null | null | true | false | Callaghan, Martina F. and Mohammadi, Siawoosh and Weiskopf, Nikolaus | 2,016 | null | https://dx.doi.org/10.1002/nbm.3658 | 10.1002/nbm.3658 | NMR in Biomedicine | Synthetic quantitative MRI through relaxometry modelling | Synthetic quantitative MRI through relaxometry modelling - PMC | https://pmc.ncbi.nlm.nih.gov/articles/PMC5132086/ | The proposed synthetic qMRI approach shows promise for furthering our understanding of the inter‐relation of MRI parameters and for maximizing |
Synthetic Generation and Latent Projection Denoising of Rim Lesions in
Multiple Sclerosis | 2505.23353v1 | Jand | \cite{Jand} | Synthetic MRI for stroke: a qualitative and quantitative pilot study | null | null | true | false | André, Joachim and Barrit, Sami and Jissendi, Patrice | 2,022 | null | null | 10.1038/s41598-022-15204-8 | Scientific Reports | Synthetic MRI for stroke: a qualitative and quantitative pilot study | (PDF) Synthetic MRI for stroke: a qualitative and quantitative pilot study | https://www.researchgate.net/publication/361826097_Synthetic_MRI_for_stroke_a_qualitative_and_quantitative_pilot_study | Synthetic MR provides qualitative and quantitative multi-parametric data about tissue properties. in a single acquisition. Its use in stroke imaging is not |
Synthetic Generation and Latent Projection Denoising of Rim Lesions in
Multiple Sclerosis | 2505.23353v1 | Emoy | \cite{Emoy} | A deep learning approach for synthetic MRI based on two routine sequences and training with synthetic data | null | null | true | false | Moya-Sáez, Elisa and Peña-Nogales, Óscar and Luis-García, Rodrigo de and Alberola-López, Carlos | 2,021 | null | https://www.sciencedirect.com/science/article/pii/S0169260721004454 | https://doi.org/10.1016/j.cmpb.2021.106371 | Computer Methods and Programs in Biomedicine | A deep learning approach for synthetic MRI based on two routine sequences and training with synthetic data | A deep learning approach for synthetic MRI based on two routine ... | https://pubmed.ncbi.nlm.nih.gov/34525411/ | **Conclusions:** These results show that our approach is able to provide realistic parametric maps and weighted images out of a CNN that (a) is trained with a synthetic dataset and (b) needs only two inputs, which are in turn obtained from a common full-brain acquisition that takes less than 8 min of scan time. * Bra... |
Synthetic Generation and Latent Projection Denoising of Rim Lesions in
Multiple Sclerosis | 2505.23353v1 | Kgop | \cite{Kgop} | Synthetic data in generalizable, learning-based neuroimaging | null | null | true | false | Gopinath, Karthik and Hoopes, Andrew and Alexander, Daniel C. and Arnold, Steven E. and Balbastre, Yael and Billot, Benjamin and Casamitjana, Adrià and Cheng, You and Chua, Russ Yue Zhi and Edlow, Brian L. and Fischl, Bruce and Gazula, Harshvardhan and Hoffmann, Malte and Keene, C. Dirk and Kim, Seunghoi and Kimberly, ... | 2,024 | 11 | https://doi.org/10.1162/imag\_a\_00337 | 10.1162/imag_a_00337 | Imaging Neuroscience | Synthetic data in generalizable, learning-based neuroimaging | Synthetic data in generalizable, learning-based ... | https://direct.mit.edu/imag/article/doi/10.1162/imag_a_00337/124867/Synthetic-data-in-generalizable-learning-based | by K Gopinath · 2024 · Cited by 17 — Synthetic data have emerged as an attractive option for developing machine-learning methods in human neuroimaging, particularly in magnetic resonance imaging ( |
Synthetic Generation and Latent Projection Denoising of Rim Lesions in
Multiple Sclerosis | 2505.23353v1 | Jigl | \cite{Jigl} | SynthSR: A public AI tool to turn heterogeneous clinical brain scans into high-resolution T1-weighted images for 3D morphometry | null | null | true | false | Juan E. Iglesias and Benjamin Billot and Yaël Balbastre and Colin Magdamo and Steven E. Arnold and Sudeshna Das and Brian L. Edlow and Daniel C. Alexander and Polina Golland and Bruce Fischl | 2,023 | null | https://www.science.org/doi/abs/10.1126/sciadv.add3607 | 10.1126/sciadv.add3607 | Science Advances | SynthSR: A public AI tool to turn heterogeneous clinical brain scans into high-resolution T1-weighted images for 3D morphometry | SynthSR: A public AI tool to turn heterogeneous clinical brain scans ... | https://pubmed.ncbi.nlm.nih.gov/36724222/ | Missing: 04/08/2025 |
Synthetic Generation and Latent Projection Denoising of Rim Lesions in
Multiple Sclerosis | 2505.23353v1 | jwil | \cite{jwil} | Limits of Transfer Learning | http://arxiv.org/abs/2006.12694v1 | Transfer learning involves taking information and insight from one problem
domain and applying it to a new problem domain. Although widely used in
practice, theory for transfer learning remains less well-developed. To address
this, we prove several novel results related to transfer learning, showing the
need to careful... | true | true | Jake Williams and Abel Tadesse and Tyler Sam and Huey Sun and George D. Montanez | 2,020 | null | https://arxiv.org/abs/2006.12694 | null | null | Limits of Transfer Learning | Limits of Transfer Learning | http://arxiv.org/pdf/2006.12694v1 | Transfer learning involves taking information and insight from one problem
domain and applying it to a new problem domain. Although widely used in
practice, theory for transfer learning remains less well-developed. To address
this, we prove several novel results related to transfer learning, showing the
need to careful... |
Synthetic Generation and Latent Projection Denoising of Rim Lesions in
Multiple Sclerosis | 2505.23353v1 | weli | \cite{weli} | Detecting Alzheimer's Disease on Small Dataset: A Knowledge Transfer Perspective | null | null | true | false | Li, Wei and Zhao, Yifei and Chen, Xi and Xiao, Yang and Qin, Yuanyuan | 2,019 | null | null | 10.1109/JBHI.2018.2839771 | IEEE Journal of Biomedical and Health Informatics | Detecting Alzheimer's Disease on Small Dataset: A Knowledge Transfer Perspective | Detecting Alzheimer's Disease on Small Dataset | http://ieeexplore.ieee.org/document/8362917/ | In addition, we proposed an effective knowledge transfer method to diminish the disparity among different datasets and improve the |
Synthetic Generation and Latent Projection Denoising of Rim Lesions in
Multiple Sclerosis | 2505.23353v1 | jval | \cite{jval} | Transfer Learning in Magnetic Resonance Brain Imaging: a Systematic
Review | http://arxiv.org/abs/2102.01530v2 | Transfer learning refers to machine learning techniques that focus on
acquiring knowledge from related tasks to improve generalization in the tasks
of interest. In MRI, transfer learning is important for developing strategies
that address the variation in MR images. Additionally, transfer learning is
beneficial to re-u... | true | true | Valverde, Juan Miguel and Imani, Vandad and Abdollahzadeh, Ali and De Feo, Riccardo and Prakash, Mithilesh and Ciszek, Robert and Tohka, Jussi | 2,021 | null | http://dx.doi.org/10.3390/jimaging7040066 | 10.3390/jimaging7040066 | Journal of Imaging | Transfer Learning in Magnetic Resonance Brain Imaging: a Systematic
Review | Transfer Learning in Magnetic Resonance Brain Imaging | https://www.researchgate.net/publication/350576269_Transfer_Learning_in_Magnetic_Resonance_Brain_Imaging_A_Systematic_Review | The aim of this review is to identify research directions, gaps in knowledge, applications, and widely used strategies among the transfer learning approaches |
Synthetic Generation and Latent Projection Denoising of Rim Lesions in
Multiple Sclerosis | 2505.23353v1 | smat | \cite{smat} | Employing deep learning and transfer learning for accurate brain tumor detection | null | null | true | false | Mathivanan, Sandeep Kumar and Sonaimuthu, Sridevi and Murugesan, Sankar and Rajadurai, Hariharan and Shivahare, Basu Dev and Shah, Mohd Asif | 2,024 | null | null | 10.1038/s41598-024-57970-7 | Scientific Reports | Employing deep learning and transfer learning for accurate brain tumor detection | (PDF) Employing deep learning and transfer learning for accurate ... | https://www.researchgate.net/publication/379337705_Employing_deep_learning_and_transfer_learning_for_accurate_brain_tumor_detection | This study delves into the potential of deep transfer learning architectures to elevate the accuracy of brain tumor diagnosis. Transfer learning |
Synthetic Generation and Latent Projection Denoising of Rim Lesions in
Multiple Sclerosis | 2505.23353v1 | Vtha | \cite{Vtha} | SinGAN-Seg: Synthetic training data generation for medical image segmentation | null | null | true | false | Thambawita, Vajira AND Salehi, Pegah AND Sheshkal, Sajad Amouei AND Hicks, Steven A. AND Hammer, Hugo L. AND Parasa, Sravanthi AND Lange, Thomas de AND Halvorsen, Pål AND Riegler, Michael A. | 2,022 | 05 | https://doi.org/10.1371/journal.pone.0267976 | 10.1371/journal.pone.0267976 | PLOS ONE | SinGAN-Seg: Synthetic training data generation for medical image segmentation | SinGAN-Seg: Synthetic training data generation for medical image segmentation | http://arxiv.org/pdf/2107.00471v2 | Analyzing medical data to find abnormalities is a time-consuming and costly
task, particularly for rare abnormalities, requiring tremendous efforts from
medical experts. Artificial intelligence has become a popular tool for the
automatic processing of medical data, acting as a supportive tool for doctors.
However, the ... |
Synthetic Generation and Latent Projection Denoising of Rim Lesions in
Multiple Sclerosis | 2505.23353v1 | Awah | \cite{Awah} | CovidGAN: Data Augmentation Using Auxiliary Classifier GAN for Improved
Covid-19 Detection | http://arxiv.org/abs/2103.05094v1 | Coronavirus (COVID-19) is a viral disease caused by severe acute respiratory
syndrome coronavirus 2 (SARS-CoV-2). The spread of COVID-19 seems to have a
detrimental effect on the global economy and health. A positive chest X-ray of
infected patients is a crucial step in the battle against COVID-19. Early
results sugges... | true | true | Waheed, Abdul and Goyal, Muskan and Gupta, Deepak and Khanna, Ashish and Al-Turjman, Fadi and Pinheiro, Plácido Rogerio | 2,020 | null | null | 10.1109/ACCESS.2020.2994762 | IEEE Access | CovidGAN: Data Augmentation Using Auxiliary Classifier GAN for Improved
Covid-19 Detection | (PDF) CovidGAN: Data Augmentation using Auxiliary Classifier GAN ... | https://www.researchgate.net/publication/341401062_CovidGAN_Data_Augmentation_using_Auxiliary_Classifier_GAN_for_Improved_Covid-19_Detection | By adding synthetic images produced by CovidGAN, the accuracy increased to 95%. We hope this method will speed up COVID-19 detection and lead to |
Synthetic Generation and Latent Projection Denoising of Rim Lesions in
Multiple Sclerosis | 2505.23353v1 | Bahm | \cite{Bahm} | Brain Tumor Classification Using a Combination of Variational Autoencoders and Generative Adversarial Networks | null | null | true | false | Ahmad, Bilal and Sun, Jun and You, Qi and Palade, Vasile and Mao, Zhongjie | 2,022 | null | https://www.mdpi.com/2227-9059/10/2/223 | null | Biomedicines | Brain Tumor Classification Using a Combination of Variational Autoencoders and Generative Adversarial Networks | (PDF) Brain Tumor Classification Using a Combination of Variational ... | https://www.researchgate.net/publication/358017457_Brain_Tumor_Classification_Using_a_Combination_of_Variational_Autoencoders_and_Generative_Adversarial_Networks | This paper proposes a framework based on unsupervised deep generative neural networks to solve this limitation. We combine two generative models in the proposed |
Synthetic Generation and Latent Projection Denoising of Rim Lesions in
Multiple Sclerosis | 2505.23353v1 | Hzha | \cite{Hzha} | QSMRim-Net: Imbalance-aware learning for identification of chronic active multiple sclerosis lesions on quantitative susceptibility maps | null | null | true | false | Zhang, Hang and Nguyen, Thanh D. and Zhang, Jinwei and Marcille, Melanie and Spincemaille, Pascal and Wang, Yi and Gauthier, Susan A. and Sweeney, Elizabeth M. | 2,022 | null | https://www.sciencedirect.com/science/article/pii/S2213158222000444 | https://doi.org/10.1016/j.nicl.2022.102979 | NeuroImage: Clinical | QSMRim-Net: Imbalance-aware learning for identification of chronic active multiple sclerosis lesions on quantitative susceptibility maps | QSMRim-Net: Imbalance-aware learning for identification of chronic ... | https://pubmed.ncbi.nlm.nih.gov/35247730/ | QSMRim-Net: Imbalance-aware learning for identification of chronic active multiple sclerosis lesions on quantitative susceptibility maps - PubMed We present QSMRim-Net, a data imbalance-aware deep neural network that fuses lesion-level radiomic and convolutional image features for automated identification of rim + lesi... |
Synthetic Generation and Latent Projection Denoising of Rim Lesions in
Multiple Sclerosis | 2505.23353v1 | Ddab | \cite{Ddab} | DeepSMOTE: Fusing Deep Learning and SMOTE for Imbalanced Data | http://arxiv.org/abs/2105.02340v1 | Despite over two decades of progress, imbalanced data is still considered a
significant challenge for contemporary machine learning models. Modern advances
in deep learning have magnified the importance of the imbalanced data problem.
The two main approaches to address this issue are based on loss function
modification... | true | true | Damien Dablain and Bartosz Krawczyk and Nitesh V. Chawla | 2,021 | null | https://arxiv.org/abs/2105.02340 | null | null | DeepSMOTE: Fusing Deep Learning and SMOTE for Imbalanced Data | DeepSMOTE: Fusing Deep Learning and SMOTE for Imbalanced Data | http://arxiv.org/pdf/2105.02340v1 | Despite over two decades of progress, imbalanced data is still considered a
significant challenge for contemporary machine learning models. Modern advances
in deep learning have magnified the importance of the imbalanced data problem.
The two main approaches to address this issue are based on loss function
modification... |
Synthetic Generation and Latent Projection Denoising of Rim Lesions in
Multiple Sclerosis | 2505.23353v1 | Msal | \cite{Msal} | Multiple Sclerosis Lesion Synthesis in MRI using an encoder-decoder
U-NET | http://arxiv.org/abs/1901.05733v1 | In this paper, we propose generating synthetic multiple sclerosis (MS)
lesions on MRI images with the final aim to improve the performance of
supervised machine learning algorithms, therefore avoiding the problem of the
lack of available ground truth. We propose a two-input two-output fully
convolutional neural network... | true | true | Salem, Mostafa and Valverde, Sergi and Cabezas, Mariano and Pareto, Deborah and Oliver, Arnau and Salvi, Joaquim and Rovira, Àlex and Lladó, Xavier | 2,019 | null | null | 10.1109/ACCESS.2019.2900198 | IEEE Access | Multiple Sclerosis Lesion Synthesis in MRI using an encoder-decoder
U-NET | (PDF) Multiple Sclerosis Lesion Synthesis in MRI using an encoder ... | https://www.researchgate.net/publication/331238531_Multiple_Sclerosis_Lesion_Synthesis_in_MRI_using_an_encoder-decoder_U-NET | In this paper, we propose generating synthetic multiple sclerosis (MS) lesions on MRI images with the final aim to improve the performance of supervised machine |
Synthetic Generation and Latent Projection Denoising of Rim Lesions in
Multiple Sclerosis | 2505.23353v1 | Igoo | \cite{Igoo} | Generative Adversarial Networks | http://arxiv.org/abs/1406.2661v1 | We propose a new framework for estimating generative models via an
adversarial process, in which we simultaneously train two models: a generative
model G that captures the data distribution, and a discriminative model D that
estimates the probability that a sample came from the training data rather than
G. The training... | true | true | Ian J. Goodfellow and Jean Pouget-Abadie and Mehdi Mirza and Bing Xu and David Warde-Farley and Sherjil Ozair and Aaron Courville and Yoshua Bengio | 2,014 | null | https://arxiv.org/abs/1406.2661 | null | null | Generative Adversarial Networks | Generative Adversarial Networks | http://arxiv.org/pdf/1406.2661v1 | We propose a new framework for estimating generative models via an
adversarial process, in which we simultaneously train two models: a generative
model G that captures the data distribution, and a discriminative model D that
estimates the probability that a sample came from the training data rather than
G. The training... |
Synthetic Generation and Latent Projection Denoising of Rim Lesions in
Multiple Sclerosis | 2505.23353v1 | Wxia | \cite{Wxia} | GAN Inversion: A Survey | http://arxiv.org/abs/2101.05278v5 | GAN inversion aims to invert a given image back into the latent space of a
pretrained GAN model, for the image to be faithfully reconstructed from the
inverted code by the generator. As an emerging technique to bridge the real and
fake image domains, GAN inversion plays an essential role in enabling the
pretrained GAN ... | true | true | Weihao Xia and Yulun Zhang and Yujiu Yang and Jing-Hao Xue and Bolei Zhou and Ming-Hsuan Yang | 2,022 | null | https://arxiv.org/abs/2101.05278 | null | null | GAN Inversion: A Survey | GAN Inversion: A Survey | http://arxiv.org/pdf/2101.05278v5 | GAN inversion aims to invert a given image back into the latent space of a
pretrained GAN model, for the image to be faithfully reconstructed from the
inverted code by the generator. As an emerging technique to bridge the real and
fake image domains, GAN inversion plays an essential role in enabling the
pretrained GAN ... |
Synthetic Generation and Latent Projection Denoising of Rim Lesions in
Multiple Sclerosis | 2505.23353v1 | Mmir | \cite{Mmir} | Conditional Generative Adversarial Nets | http://arxiv.org/abs/1411.1784v1 | Generative Adversarial Nets [8] were recently introduced as a novel way to
train generative models. In this work we introduce the conditional version of
generative adversarial nets, which can be constructed by simply feeding the
data, y, we wish to condition on to both the generator and discriminator. We
show that this... | true | true | Mehdi Mirza and
Simon Osindero | 2,014 | null | http://arxiv.org/abs/1411.1784 | null | CoRR | Conditional Generative Adversarial Nets | Conditional Generative Adversarial Nets | http://arxiv.org/pdf/1411.1784v1 | Generative Adversarial Nets [8] were recently introduced as a novel way to
train generative models. In this work we introduce the conditional version of
generative adversarial nets, which can be constructed by simply feeding the
data, y, we wish to condition on to both the generator and discriminator. We
show that this... |
Synthetic Generation and Latent Projection Denoising of Rim Lesions in
Multiple Sclerosis | 2505.23353v1 | Kthe | \cite{Kthe} | Robustness of Conditional GANs to Noisy Labels | http://arxiv.org/abs/1811.03205v1 | We study the problem of learning conditional generators from noisy labeled
samples, where the labels are corrupted by random noise. A standard training of
conditional GANs will not only produce samples with wrong labels, but also
generate poor quality samples. We consider two scenarios, depending on whether
the noise m... | true | true | Kiran Koshy Thekumparampil and Ashish Khetan and Zinan Lin and Sewoong Oh | 2,018 | null | https://arxiv.org/abs/1811.03205 | null | null | Robustness of Conditional GANs to Noisy Labels | Robustness of Conditional GANs to Noisy Labels | http://arxiv.org/pdf/1811.03205v1 | We study the problem of learning conditional generators from noisy labeled
samples, where the labels are corrupted by random noise. A standard training of
conditional GANs will not only produce samples with wrong labels, but also
generate poor quality samples. We consider two scenarios, depending on whether
the noise m... |
Synthetic Generation and Latent Projection Denoising of Rim Lesions in
Multiple Sclerosis | 2505.23353v1 | Wehua | \cite{Wehua} | Correcting Noisy Multilabel Predictions: Modeling Label Noise through
Latent Space Shifts | http://arxiv.org/abs/2502.14281v3 | Noise in data appears to be inevitable in most real-world machine learning
applications and would cause severe overfitting problems. Not only can data
features contain noise, but labels are also prone to be noisy due to human
input. In this paper, rather than noisy label learning in multiclass
classifications, we inste... | true | true | Weipeng Huang and Qin Li and Yang Xiao and Cheng Qiao and Tie Cai and Junwei Liao and Neil J. Hurley and Guangyuan Piao | 2,025 | null | https://arxiv.org/abs/2502.14281 | null | null | Correcting Noisy Multilabel Predictions: Modeling Label Noise through
Latent Space Shifts | [PDF] Correcting Noisy Multilabel Predictions: Modeling Label Noise ... | http://arxiv.org/pdf/2502.14281 | Once the shifted latent variable still locates in the right latent space, the generated label noise will also follow the pattern. (in particular |
Synthetic Generation and Latent Projection Denoising of Rim Lesions in
Multiple Sclerosis | 2505.23353v1 | Hbae | \cite{Hbae} | From Noisy Prediction to True Label: Noisy Prediction Calibration via
Generative Model | http://arxiv.org/abs/2205.00690v3 | Noisy labels are inevitable yet problematic in machine learning society. It
ruins the generalization of a classifier by making the classifier over-fitted
to noisy labels. Existing methods on noisy label have focused on modifying the
classifier during the training procedure. It has two potential problems. First,
these m... | true | true | HeeSun Bae and Seungjae Shin and Byeonghu Na and JoonHo Jang and Kyungwoo Song and Il-Chul Moon | 2,022 | null | https://arxiv.org/abs/2205.00690 | null | null | From Noisy Prediction to True Label: Noisy Prediction Calibration via
Generative Model | [PDF] Noisy Prediction Calibration via Generative Model | https://icml.cc/media/icml-2022/Slides/18350_oZIPQgX.pdf | NPC models the relation between output of a classifier and the true label via generative model. NPC consistently boosts the classification performances of pre- |
Synthetic Generation and Latent Projection Denoising of Rim Lesions in
Multiple Sclerosis | 2505.23353v1 | Vkel | \cite{Vkel} | Prior Image-Constrained Reconstruction using Style-Based Generative
Models | http://arxiv.org/abs/2102.12525v2 | Obtaining a useful estimate of an object from highly incomplete imaging
measurements remains a holy grail of imaging science. Deep learning methods
have shown promise in learning object priors or constraints to improve the
conditioning of an ill-posed imaging inverse problem. In this study, a
framework for estimating a... | true | true | Kelkar, Varun A and Anastasio, Mark | 2,021 | 18--24 Jul | https://proceedings.mlr.press/v139/kelkar21a.html | null | null | Prior Image-Constrained Reconstruction using Style-Based Generative
Models | Prior Image-Constrained Reconstruction using Style-Based ... | http://proceedings.mlr.press/v139/kelkar21a/kelkar21a.pdf | by VA Kelkar · 2021 · Cited by 33 — Style-based generative models have been known to be able to control individual semantic features, or styles, in an image by varying the disentangled. Page 2 |
Pre-Training Curriculum for Multi-Token Prediction in Language Models | 2505.22757v1 | bengio2009curriculum | \cite{bengio2009curriculum} | Curriculum learning | null | null | true | false | Bengio, Yoshua and Louradour, J\'{e}r\^{o}me and Collobert, Ronan and Weston, Jason | 2,009 | null | https://doi.org/10.1145/1553374.1553380 | 10.1145/1553374.1553380 | null | Curriculum learning | Curriculum learning | https://en.wikipedia.org/wiki/Curriculum_learning | Curriculum learning is a technique in machine learning in which a model is trained on examples of increasing difficulty. |
Pre-Training Curriculum for Multi-Token Prediction in Language Models | 2505.22757v1 | cl_survey | \cite{cl_survey} | Curriculum Learning: A Survey | http://arxiv.org/abs/2101.10382v3 | Training machine learning models in a meaningful order, from the easy samples
to the hard ones, using curriculum learning can provide performance
improvements over the standard training approach based on random data
shuffling, without any additional computational costs. Curriculum learning
strategies have been successf... | true | true | Petru Soviany and Radu Tudor Ionescu and Paolo Rota and Nicu Sebe | 2,022 | null | https://arxiv.org/abs/2101.10382 | null | null | Curriculum Learning: A Survey | Curriculum Learning: A Survey | http://arxiv.org/pdf/2101.10382v3 | Training machine learning models in a meaningful order, from the easy samples
to the hard ones, using curriculum learning can provide performance
improvements over the standard training approach based on random data
shuffling, without any additional computational costs. Curriculum learning
strategies have been successf... |
Pre-Training Curriculum for Multi-Token Prediction in Language Models | 2505.22757v1 | cl_nlu | \cite{cl_nlu} | Curriculum Learning for Natural Language Understanding | null | null | true | false | Xu, Benfeng and
Zhang, Licheng and
Mao, Zhendong and
Wang, Quan and
Xie, Hongtao and
Zhang, Yongdong | 2,020 | null | https://aclanthology.org/2020.acl-main.542 | 10.18653/v1/2020.acl-main.542 | null | Curriculum Learning for Natural Language Understanding | [PDF] Curriculum Learning for Natural Language Understanding - Digie | https://api.digie.ai/publications/Curriculum-Learning-for-NLU.pdf | Natural Language Understanding (NLU), which re- quires machines to understand and reason with hu- man language, is a crucial yet challenging problem. Recently, |
Pre-Training Curriculum for Multi-Token Prediction in Language Models | 2505.22757v1 | cl_bert | \cite{cl_bert} | Pre-training a {BERT} with Curriculum Learning by Increasing Block-Size of Input Text | null | null | true | false | Nagatsuka, Koichi and
Broni-Bediako, Clifford and
Atsumi, Masayasu | 2,021 | null | https://aclanthology.org/2021.ranlp-1.112 | null | null | Pre-training a {BERT} with Curriculum Learning by Increasing Block-Size of Input Text | Pre-training a BERT with Curriculum Learning by Increasing Block ... | https://aclanthology.org/2021.ranlp-1.112/ | We propose a new CL method which gradually increases the block-size of input text for training the self-attention mechanism of BERT and its variants. |
Pre-Training Curriculum for Multi-Token Prediction in Language Models | 2505.22757v1 | bert_lrc | \cite{bert_lrc} | Modeling Easiness for Training Transformers with Curriculum Learning | null | null | true | false | Ranaldi, Leonardo and
Pucci, Giulia and
Zanzotto, Fabio Massimo | 2,023 | null | https://aclanthology.org/2023.ranlp-1.101 | null | null | Modeling Easiness for Training Transformers with Curriculum Learning | Modeling Easiness for Training Transformers with Curriculum ... | https://aclanthology.org/2023.ranlp-1.101/ | In this paper, building on Curriculum Learning, we propose a novel, linguistically motivated measure to determine example complexity for organizing examples |
Pre-Training Curriculum for Multi-Token Prediction in Language Models | 2505.22757v1 | orca | \cite{orca} | Orca: Progressive Learning from Complex Explanation Traces of GPT-4 | http://arxiv.org/abs/2306.02707v1 | Recent research has focused on enhancing the capability of smaller models
through imitation learning, drawing on the outputs generated by large
foundation models (LFMs). A number of issues impact the quality of these
models, ranging from limited imitation signals from shallow LFM outputs; small
scale homogeneous traini... | true | true | Subhabrata Mukherjee and Arindam Mitra and Ganesh Jawahar and Sahaj Agarwal and Hamid Palangi and Ahmed Awadallah | 2,023 | null | https://arxiv.org/abs/2306.02707 | null | null | Orca: Progressive Learning from Complex Explanation Traces of GPT-4 | Orca: Progressive Learning from Complex Explanation Traces of GPT-4 | http://arxiv.org/pdf/2306.02707v1 | Recent research has focused on enhancing the capability of smaller models
through imitation learning, drawing on the outputs generated by large
foundation models (LFMs). A number of issues impact the quality of these
models, ranging from limited imitation signals from shallow LFM outputs; small
scale homogeneous traini... |
Pre-Training Curriculum for Multi-Token Prediction in Language Models | 2505.22757v1 | curr_instr | \cite{curr_instr} | Instruction Tuning with Human Curriculum | http://arxiv.org/abs/2310.09518v4 | In this work, we (1) introduce Curriculum Instruction Tuning, (2) explore the
potential advantages of employing diverse curriculum strategies, and (3)
delineate a synthetic instruction-response generation framework that
complements our theoretical approach. Distinct from the existing instruction
tuning dataset, our gen... | true | true | Lee, Bruce W and
Cho, Hyunsoo and
Yoo, Kang Min | 2,024 | null | https://aclanthology.org/2024.findings-naacl.82 | 10.18653/v1/2024.findings-naacl.82 | null | Instruction Tuning with Human Curriculum | Instruction Tuning with Human Curriculum | http://arxiv.org/pdf/2310.09518v4 | In this work, we (1) introduce Curriculum Instruction Tuning, (2) explore the
potential advantages of employing diverse curriculum strategies, and (3)
delineate a synthetic instruction-response generation framework that
complements our theoretical approach. Distinct from the existing instruction
tuning dataset, our gen... |
Pre-Training Curriculum for Multi-Token Prediction in Language Models | 2505.22757v1 | feng2024 | \cite{feng2024} | Maximize Your Data's Potential: Enhancing LLM Accuracy with Two-Phase
Pretraining | http://arxiv.org/abs/2412.15285v1 | Pretraining large language models effectively requires strategic data
selection, blending and ordering. However, key details about data mixtures
especially their scalability to longer token horizons and larger model sizes
remain underexplored due to limited disclosure by model developers. To address
this, we formalize ... | true | true | Steven Feng and Shrimai Prabhumoye and Kezhi Kong and Dan Su and Mostofa Patwary and Mohammad Shoeybi and Bryan Catanzaro | 2,024 | null | https://arxiv.org/abs/2412.15285 | null | null | Maximize Your Data's Potential: Enhancing LLM Accuracy with Two-Phase
Pretraining | Maximize Your Data's Potential: Enhancing LLM Accuracy with Two ... | https://arxiv.org/abs/2412.15285 | A two-phase approach for pretraining outperforms random data ordering and natural distribution of tokens by 3.4% and 17% on average accuracies. |
Pre-Training Curriculum for Multi-Token Prediction in Language Models | 2505.22757v1 | babylm_2023 | \cite{babylm_2023} | Findings of the {B}aby{LM} Challenge: Sample-Efficient Pretraining on Developmentally Plausible Corpora | null | null | true | false | Warstadt, Alex and
Mueller, Aaron and
Choshen, Leshem and
Wilcox, Ethan and
Zhuang, Chengxu and
Ciro, Juan and
Mosquera, Rafael and
Paranjabe, Bhargavi and
Williams, Adina and
Linzen, Tal and
Cotterell, Ryan | 2,023 | null | https://aclanthology.org/2023.conll-babylm.1 | 10.18653/v1/2023.conll-babylm.1 | null | Findings of the {B}aby{LM} Challenge: Sample-Efficient Pretraining on Developmentally Plausible Corpora | Findings of the BabyLM Challenge: Sample-Efficient Pretraining on ... | https://aclanthology.org/2023.conll-babylm.1/ | The BabyLM Challenge findings focus on sample-efficient pretraining on developmentally plausible corpora, presented at the 27th Conference on Computational |
Pre-Training Curriculum for Multi-Token Prediction in Language Models | 2505.22757v1 | babylm_2024 | \cite{babylm_2024} | Findings of the Second BabyLM Challenge: Sample-Efficient Pretraining on
Developmentally Plausible Corpora | http://arxiv.org/abs/2412.05149v1 | The BabyLM Challenge is a community effort to close the data-efficiency gap
between human and computational language learners. Participants compete to
optimize language model training on a fixed language data budget of 100 million
words or less. This year, we released improved text corpora, as well as a
vision-and-lang... | true | true | Michael Y. Hu and Aaron Mueller and Candace Ross and Adina Williams and Tal Linzen and Chengxu Zhuang and Ryan Cotterell and Leshem Choshen and Alex Warstadt and Ethan Gotlieb Wilcox | 2,024 | null | https://arxiv.org/abs/2412.05149 | null | null | Findings of the Second BabyLM Challenge: Sample-Efficient Pretraining on
Developmentally Plausible Corpora | [2504.08165] Findings of the BabyLM Challenge | https://arxiv.org/abs/2504.08165 | View a PDF of the paper titled Findings of the BabyLM Challenge: Sample-Efficient Pretraining on Developmentally Plausible Corpora, by Alex Warstadt and 10 other authors From over 30 submissions, we extract concrete recommendations on how best to train data-efficient language models, and on where future efforts should ... |
Pre-Training Curriculum for Multi-Token Prediction in Language Models | 2505.22757v1 | less_is_more | \cite{less_is_more} | Less is More: Pre-Training Cross-Lingual Small-Scale Language Models
with Cognitively-Plausible Curriculum Learning Strategies | http://arxiv.org/abs/2410.22886v2 | Curriculum Learning has been a popular strategy to improve the cognitive
plausibility of Small-Scale Language Models (SSLMs) in the BabyLM Challenge.
However, it has not led to considerable improvements over non-curriculum
models. We assess whether theoretical linguistic acquisition theories can be
used to specify more... | true | true | Suchir Salhan and Richard Diehl Martinez and Zébulon Goriely and Paula Buttery | 2,024 | null | https://arxiv.org/abs/2410.22886 | null | null | Less is More: Pre-Training Cross-Lingual Small-Scale Language Models
with Cognitively-Plausible Curriculum Learning Strategies | Suchir Salhan - Google Scholar | https://scholar.google.com/citations?user=xOo9sisAAAAJ&hl=en | Less is More: Pre-Training Cross-Lingual Small-Scale Language Models with Cognitively-Plausible Curriculum Learning Strategies. S Salhan, RD Martinez, Z Goriely |
Pre-Training Curriculum for Multi-Token Prediction in Language Models | 2505.22757v1 | prophetnet | \cite{prophetnet} | {P}rophet{N}et: Predicting Future N-gram for Sequence-to-{S}equence{P}re-training | null | null | true | false | Qi, Weizhen and
Yan, Yu and
Gong, Yeyun and
Liu, Dayiheng and
Duan, Nan and
Chen, Jiusheng and
Zhang, Ruofei and
Zhou, Ming | 2,020 | null | https://aclanthology.org/2020.findings-emnlp.217 | 10.18653/v1/2020.findings-emnlp.217 | null | {P}rophet{N}et: Predicting Future N-gram for Sequence-to-{S}equence{P}re-training | ProphetNet: Predicting Future N-gram for Sequence-to- ... | https://arxiv.org/abs/2001.04063 | by W Qi · 2020 · Cited by 542 — This paper presents a new sequence-to-sequence pre-training model called ProphetNet, which introduces a novel self-supervised objective named future n-gram |
Pre-Training Curriculum for Multi-Token Prediction in Language Models | 2505.22757v1 | future_lens | \cite{future_lens} | Future Lens: Anticipating Subsequent Tokens from a Single Hidden State | http://arxiv.org/abs/2311.04897v1 | We conjecture that hidden state vectors corresponding to individual input
tokens encode information sufficient to accurately predict several tokens
ahead. More concretely, in this paper we ask: Given a hidden (internal)
representation of a single token at position $t$ in an input, can we reliably
anticipate the tokens ... | true | true | Pal, Koyena and
Sun, Jiuding and
Yuan, Andrew and
Wallace, Byron and
Bau, David | 2,023 | null | https://aclanthology.org/2023.conll-1.37 | 10.18653/v1/2023.conll-1.37 | null | Future Lens: Anticipating Subsequent Tokens from a Single Hidden State | Future Lens: Anticipating Subsequent Tokens from a Single Hidden State | http://arxiv.org/pdf/2311.04897v1 | We conjecture that hidden state vectors corresponding to individual input
tokens encode information sufficient to accurately predict several tokens
ahead. More concretely, in this paper we ask: Given a hidden (internal)
representation of a single token at position $t$ in an input, can we reliably
anticipate the tokens ... |
Pre-Training Curriculum for Multi-Token Prediction in Language Models | 2505.22757v1 | gloeckle2024mtp | \cite{gloeckle2024mtp} | Better & Faster Large Language Models via Multi-token Prediction | http://arxiv.org/abs/2404.19737v1 | Large language models such as GPT and Llama are trained with a next-token
prediction loss. In this work, we suggest that training language models to
predict multiple future tokens at once results in higher sample efficiency.
More specifically, at each position in the training corpus, we ask the model to
predict the fol... | true | true | Fabian Gloeckle and Badr Youbi Idrissi and Baptiste Rozière and David Lopez-Paz and Gabriel Synnaeve | 2,024 | null | https://arxiv.org/abs/2404.19737 | null | null | Better & Faster Large Language Models via Multi-token Prediction | Better & Faster Large Language Models via Multi-token ... | https://www.reddit.com/r/LocalLLaMA/comments/1dj9xql/better_faster_large_language_models_via/ | In this work, we suggest that training language models to predict multiple future tokens at once results in higher sample efficiency. |
Pre-Training Curriculum for Multi-Token Prediction in Language Models | 2505.22757v1 | blockwise_parallel_decoding | \cite{blockwise_parallel_decoding} | Blockwise Parallel Decoding for Deep Autoregressive Models | http://arxiv.org/abs/1811.03115v1 | Deep autoregressive sequence-to-sequence models have demonstrated impressive
performance across a wide variety of tasks in recent years. While common
architecture classes such as recurrent, convolutional, and self-attention
networks make different trade-offs between the amount of computation needed per
layer and the le... | true | true | Stern, Mitchell and Shazeer, Noam and Uszkoreit, Jakob | 2,018 | null | https://proceedings.neurips.cc/paper_files/paper/2018/file/c4127b9194fe8562c64dc0f5bf2c93bc-Paper.pdf | null | null | Blockwise Parallel Decoding for Deep Autoregressive Models | Blockwise Parallel Decoding for Deep Autoregressive Models | http://arxiv.org/pdf/1811.03115v1 | Deep autoregressive sequence-to-sequence models have demonstrated impressive
performance across a wide variety of tasks in recent years. While common
architecture classes such as recurrent, convolutional, and self-attention
networks make different trade-offs between the amount of computation needed per
layer and the le... |
Pre-Training Curriculum for Multi-Token Prediction in Language Models | 2505.22757v1 | layerskip | \cite{layerskip} | {L}ayer{S}kip: Enabling Early Exit Inference and Self-Speculative Decoding | null | null | true | false | Elhoushi, Mostafa and
Shrivastava, Akshat and
Liskovich, Diana and
Hosmer, Basil and
Wasti, Bram and
Lai, Liangzhen and
Mahmoud, Anas and
Acun, Bilge and
Agarwal, Saurabh and
Roman, Ahmed and
Aly, Ahmed and
Chen, Beidi and
Wu, Carole-Je... | 2,024 | null | https://aclanthology.org/2024.acl-long.681 | 10.18653/v1/2024.acl-long.681 | null | {L}ayer{S}kip: Enabling Early Exit Inference and Self-Speculative Decoding | Enabling Early Exit Inference and Self-Speculative Decoding | https://aclanthology.org/2024.acl-long.681/ | We present LayerSkip, an end-to-end solution to speed-up inference of large language models (LLMs). First, during training we apply layer dropout. |
Pre-Training Curriculum for Multi-Token Prediction in Language Models | 2505.22757v1 | kangaroo | \cite{kangaroo} | Kangaroo: Lossless Self-Speculative Decoding via Double Early Exiting | http://arxiv.org/abs/2404.18911v1 | Speculative decoding has demonstrated its effectiveness in accelerating the
inference of large language models while maintaining a consistent sampling
distribution. However, the conventional approach of training a separate draft
model to achieve a satisfactory token acceptance rate can be costly. Drawing
inspiration fr... | true | true | Fangcheng Liu and Yehui Tang and Zhenhua Liu and Yunsheng Ni and Kai Han and Yunhe Wang | 2,024 | null | https://arxiv.org/abs/2404.18911 | null | null | Kangaroo: Lossless Self-Speculative Decoding via Double Early Exiting | NeurIPS Poster Kangaroo: Lossless Self-Speculative Decoding for ... | https://neurips.cc/virtual/2024/poster/93829 | Kangaroo: Lossless Self-Speculative Decoding for Accelerating LLMs via Double Early Exiting However, the conventional approach of training separate draft model to achieve a satisfactory token acceptance rate can be costly and impractical. In this paper, we propose a novel self-speculative decoding framework \emph{Kanga... |
Pre-Training Curriculum for Multi-Token Prediction in Language Models | 2505.22757v1 | draft_verify | \cite{draft_verify} | Draft & Verify: Lossless Large Language Model Acceleration via
Self-Speculative Decoding | http://arxiv.org/abs/2309.08168v2 | We present a novel inference scheme, self-speculative decoding, for
accelerating Large Language Models (LLMs) without the need for an auxiliary
model. This approach is characterized by a two-stage process: drafting and
verification. The drafting stage generates draft tokens at a slightly lower
quality but more quickly,... | true | true | Zhang, Jun and
Wang, Jue and
Li, Huan and
Shou, Lidan and
Chen, Ke and
Chen, Gang and
Mehrotra, Sharad | 2,024 | null | https://aclanthology.org/2024.acl-long.607 | 10.18653/v1/2024.acl-long.607 | null | Draft & Verify: Lossless Large Language Model Acceleration via
Self-Speculative Decoding | Draft & Verify: Lossless Large Language Model ... | https://aclanthology.org/2024.acl-long.607/ | by J Zhang · 2024 · Cited by 130 — We present a novel inference scheme, self-speculative decoding, for accelerating Large Language Models (LLMs) without the need for an auxiliary model. |
Pre-Training Curriculum for Multi-Token Prediction in Language Models | 2505.22757v1 | swift | \cite{swift} | SWIFT: On-the-Fly Self-Speculative Decoding for LLM Inference
Acceleration | http://arxiv.org/abs/2410.06916v2 | Speculative decoding (SD) has emerged as a widely used paradigm to accelerate
LLM inference without compromising quality. It works by first employing a
compact model to draft multiple tokens efficiently and then using the target
LLM to verify them in parallel. While this technique has achieved notable
speedups, most ex... | true | true | Heming Xia and Yongqi Li and Jun Zhang and Cunxiao Du and Wenjie Li | 2,024 | null | https://arxiv.org/abs/2410.06916 | null | null | SWIFT: On-the-Fly Self-Speculative Decoding for LLM Inference
Acceleration | SWIFT: On-the-Fly Self-Speculative Decoding for LLM ... | https://github.com/hemingkx/SWIFT | SWIFT is an on-the-fly self-speculative decoding algorithm that adaptively selects intermediate layers of LLMs to skip during inference. |
Pre-Training Curriculum for Multi-Token Prediction in Language Models | 2505.22757v1 | koala | \cite{koala} | KOALA: Enhancing Speculative Decoding for LLM via Multi-Layer Draft
Heads with Adversarial Learning | http://arxiv.org/abs/2408.08146v1 | Large Language Models (LLMs) exhibit high inference latency due to their
autoregressive decoding nature. While the draft head in speculative decoding
mitigates this issue, its full potential remains unexplored. In this paper, we
introduce KOALA (K-layer Optimized Adversarial Learning Architecture), an
orthogonal approa... | true | true | Kaiqi Zhang and Jing Zhao and Rui Chen | 2,024 | null | https://arxiv.org/abs/2408.08146 | null | null | KOALA: Enhancing Speculative Decoding for LLM via Multi-Layer Draft
Heads with Adversarial Learning | hemingkx/SpeculativeDecodingPapers: Must-read papers ... - GitHub | https://github.com/hemingkx/SpeculativeDecodingPapers | [pdf], 2024.08. KOALA: Enhancing Speculative Decoding for LLM via Multi-Layer Draft Heads with Adversarial Learning Kaiqi Zhang, Jing Zhao, Rui Chen. [pdf] |
Pre-Training Curriculum for Multi-Token Prediction in Language Models | 2505.22757v1 | medusa | \cite{medusa} | Medusa: Simple LLM Inference Acceleration Framework with Multiple
Decoding Heads | http://arxiv.org/abs/2401.10774v3 | Large Language Models (LLMs) employ auto-regressive decoding that requires
sequential computation, with each step reliant on the previous one's output.
This creates a bottleneck as each step necessitates moving the full model
parameters from High-Bandwidth Memory (HBM) to the accelerator's cache. While
methods such as ... | true | true | Tianle Cai and Yuhong Li and Zhengyang Geng and Hongwu Peng and Jason D. Lee and Deming Chen and Tri Dao | 2,024 | null | https://arxiv.org/abs/2401.10774 | null | null | Medusa: Simple LLM Inference Acceleration Framework with Multiple
Decoding Heads | Medusa: Simple Framework for Accelerating LLM ... | https://github.com/FasterDecoding/Medusa | Medusa is a simple framework that democratizes the acceleration techniques for LLM generation with multiple decoding heads. |
Learning Pyramid-structured Long-range Dependencies for 3D Human Pose
Estimation | 2506.02853v1 | lee1985determination | \cite{lee1985determination} | Determination of {3D} human body postures from a single view | null | null | true | false | Lee, Hsi-Jian and Chen, Zen | 1,985 | null | null | null | Computer Vision, Graphics, and Image Processing | Determination of {3D} human body postures from a single view | Determination of 3D human body postures from a single view | https://www.sciencedirect.com/science/article/abs/pii/0734189X85900945 | In this paper a method is proposed to recover and interpret the 3D body structures of a person from a single view, provided that (1) at least six feature points on the head and a set of body joints are available on the image plane, and (2) the geometry of head and lengths of body segments formed by joints are known. 20... |
Learning Pyramid-structured Long-range Dependencies for 3D Human Pose
Estimation | 2506.02853v1 | mehta2017monocular | \cite{mehta2017monocular} | Monocular {3D} human pose estimation in the wild using improved cnn supervision | null | null | true | false | Mehta, Dushyant and Rhodin, Helge and Casas, Dan and Fua, Pascal and Sotnychenko, Oleksandr and Xu, Weipeng and Theobalt, Christian | 2,017 | null | null | null | null | Monocular {3D} human pose estimation in the wild using improved cnn supervision | Monocular 3D Human Pose Estimation In The Wild Using Improved ... | https://arxiv.org/abs/1611.09813 | Authors:Dushyant Mehta, Helge Rhodin, Dan Casas, Pascal Fua, Oleksandr Sotnychenko, Weipeng Xu, Christian Theobalt View a PDF of the paper titled Monocular 3D Human Pose Estimation In The Wild Using Improved CNN Supervision, by Dushyant Mehta and 6 other authors View a PDF of the paper titled Monocular 3D Human Pose Es... |
Learning Pyramid-structured Long-range Dependencies for 3D Human Pose
Estimation | 2506.02853v1 | pavlakos2017coarse | \cite{pavlakos2017coarse} | Coarse-to-Fine Volumetric Prediction for Single-Image 3D Human Pose | http://arxiv.org/abs/1611.07828v2 | This paper addresses the challenge of 3D human pose estimation from a single
color image. Despite the general success of the end-to-end learning paradigm,
top performing approaches employ a two-step solution consisting of a
Convolutional Network (ConvNet) for 2D joint localization and a subsequent
optimization step to ... | true | true | Pavlakos, Georgios and Zhou, Xiaowei and Derpanis, Konstantinos G and Daniilidis, Kostas | 2,017 | null | null | null | null | Coarse-to-Fine Volumetric Prediction for Single-Image 3D Human Pose | Coarse-to-Fine Volumetric Prediction for Single-Image 3D ... | https://arxiv.org/abs/1611.07828 | Image 2: arxiv logo>cs> arXiv:1611.07828 **arXiv:1611.07828** (cs) View a PDF of the paper titled Coarse-to-Fine Volumetric Prediction for Single-Image 3D Human Pose, by Georgios Pavlakos and 3 other authors View a PDF of the paper titled Coarse-to-Fine Volumetric Prediction for Single-Image 3D Human Pose, by Georgio... |
Learning Pyramid-structured Long-range Dependencies for 3D Human Pose
Estimation | 2506.02853v1 | cai2019exploiting | \cite{cai2019exploiting} | Exploiting spatial-temporal relationships for {3D} pose estimation via graph convolutional networks | null | null | true | false | Cai, Yujun and Ge, Liuhao and Liu, Jun and Cai, Jianfei and Cham, Tat-Jen and Yuan, Junsong and Thalmann, Nadia Magnenat | 2,019 | null | null | null | null | Exploiting spatial-temporal relationships for {3D} pose estimation via graph convolutional networks | vanoracai/Exploiting-Spatial-temporal-Relationships-for- ... | https://github.com/vanoracai/Exploiting-Spatial-temporal-Relationships-for-3D-Pose-Estimation-via-Graph-Convolutional-Networks | This is the code for the paper ICCV 2019 Exploiting Spatial-temporal Relationships for 3D Pose Estimation via Graph Convolutional Networks in Pytorch. |
Learning Pyramid-structured Long-range Dependencies for 3D Human Pose
Estimation | 2506.02853v1 | martinez2017simple | \cite{martinez2017simple} | A simple yet effective baseline for 3d human pose estimation | http://arxiv.org/abs/1705.03098v2 | Following the success of deep convolutional networks, state-of-the-art
methods for 3d human pose estimation have focused on deep end-to-end systems
that predict 3d joint locations given raw image pixels. Despite their excellent
performance, it is often not easy to understand whether their remaining error
stems from a l... | true | true | Martinez, Julieta and Hossain, Rayat and Romero, Javier and Little, James J | 2,017 | null | null | null | null | A simple yet effective baseline for 3d human pose estimation | A simple yet effective baseline for 3d human pose estimation | http://arxiv.org/pdf/1705.03098v2 | Following the success of deep convolutional networks, state-of-the-art
methods for 3d human pose estimation have focused on deep end-to-end systems
that predict 3d joint locations given raw image pixels. Despite their excellent
performance, it is often not easy to understand whether their remaining error
stems from a l... |
Learning Pyramid-structured Long-range Dependencies for 3D Human Pose
Estimation | 2506.02853v1 | zhao2019semantic | \cite{zhao2019semantic} | {Semantic Graph Convolutional Networks for 3D Human Pose Regression} | null | null | true | false | Zhao, Long and Peng, Xi and Tian, Yu and Kapadia, Mubbasir and Metaxas, Dimitris N | 2,019 | null | null | null | null | {Semantic Graph Convolutional Networks for 3D Human Pose Regression} | Semantic Graph Convolutional Networks for 3D Human ... | https://openaccess.thecvf.com/content_CVPR_2019/papers/Zhao_Semantic_Graph_Convolutional_Networks_for_3D_Human_Pose_Regression_CVPR_2019_paper.pdf | by L Zhao · 2019 · Cited by 714 — SemGCN is a novel network for regression tasks with graph data, capturing semantic information, and applied to 3D human pose regression. |
Learning Pyramid-structured Long-range Dependencies for 3D Human Pose
Estimation | 2506.02853v1 | zou2021modulated | \cite{zou2021modulated} | Modulated graph convolutional network for {3D} human pose estimation | null | null | true | false | Zou, Zhiming and Tang, Wei | 2,021 | null | null | null | null | Modulated graph convolutional network for {3D} human pose estimation | Modulated Graph Convolutional Network for 3D Human Pose ... | https://ieeexplore.ieee.org/document/9710217/ | The graph convolutional network (GCN) has recently achieved promising performance of 3D human pose estimation (HPE) by modeling the relationship among body |
Learning Pyramid-structured Long-range Dependencies for 3D Human Pose
Estimation | 2506.02853v1 | zhao2022graformer | \cite{zhao2022graformer} | {GraFormer: Graph-oriented Transformer for {3D} Pose Estimation} | null | null | true | false | Zhao, Weixi and Wang, Weiqiang and Tian, Yunjie | 2,022 | null | null | null | null | {GraFormer: Graph-oriented Transformer for {3D} Pose Estimation} | [PDF] GraFormer: Graph-Oriented Transformer for 3D Pose Estimation | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhao_GraFormer_Graph-Oriented_Transformer_for_3D_Pose_Estimation_CVPR_2022_paper.pdf | In this paper, we use a new transformer architecture by embedding graph convolution operations to improve the. 3D pose estimation. 3. Method. As shown in Figure |
Learning Pyramid-structured Long-range Dependencies for 3D Human Pose
Estimation | 2506.02853v1 | ZhongTMM2024 | \cite{ZhongTMM2024} | {Frame-Padded Multiscale Transformer for Monocular {3D} Human Pose Estimation} | null | null | true | false | Zhong, Yuanhong and Yang, Guangxia and Zhong, Daidi and Yang, Xun and Wang, Shanshan | 2,024 | null | null | 10.1109/TMM.2023.3347095 | IEEE Transactions on Multimedia | {Frame-Padded Multiscale Transformer for Monocular {3D} Human Pose Estimation} | Frame-Padded Multiscale Transformer for Monocular 3D Human ... | https://dl.acm.org/doi/10.1109/TMM.2023.3347095 | Abstract. Monocular 3D human pose estimation is an ill-posed problem in computer vision due to its depth ambiguity. Most existing works supplement the depth |
Learning Pyramid-structured Long-range Dependencies for 3D Human Pose
Estimation | 2506.02853v1 | WangTMM2024 | \cite{WangTMM2024} | {Exploiting Temporal Correlations for {3D} Human Pose Estimation} | null | null | true | false | Wang, Ruibin and Ying, Xianghua and Xing, Bowei | 2,024 | null | null | 10.1109/TMM.2023.3323874 | IEEE Transactions on Multimedia | {Exploiting Temporal Correlations for {3D} Human Pose Estimation} | Exploiting Temporal Correlations for 3D Human Pose ... | http://ieeexplore.ieee.org/document/10278485/ | Exploiting the rich temporal information in human pose sequences to facilitate 3D pose estimation has garnered particular attention. |
Learning Pyramid-structured Long-range Dependencies for 3D Human Pose
Estimation | 2506.02853v1 | tang20233d | \cite{tang20233d} | {3D} human pose estimation with spatio-temporal criss-cross attention | null | null | true | false | Tang, Zhenhua and Qiu, Zhaofan and Hao, Yanbin and Hong, Richang and Yao, Ting | 2,023 | null | null | null | null | {3D} human pose estimation with spatio-temporal criss-cross attention | zhenhuat/STCFormer: (CVPR2023)3D Human Pose ... | https://github.com/zhenhuat/STCFormer | This is the readme file for the code release of 3D Human Pose Estimation with Spatio-Temporal Criss-cross Attention on PyTorch platform. |
Learning Pyramid-structured Long-range Dependencies for 3D Human Pose
Estimation | 2506.02853v1 | li2022mhformer | \cite{li2022mhformer} | MHFormer: Multi-Hypothesis Transformer for 3D Human Pose Estimation | http://arxiv.org/abs/2111.12707v4 | Estimating 3D human poses from monocular videos is a challenging task due to
depth ambiguity and self-occlusion. Most existing works attempt to solve both
issues by exploiting spatial and temporal relationships. However, those works
ignore the fact that it is an inverse problem where multiple feasible solutions
(i.e., ... | true | true | Li, Wenhao and Liu, Hong and Tang, Hao and Wang, Pichao and Van Gool, Luc | 2,022 | null | null | null | null | MHFormer: Multi-Hypothesis Transformer for 3D Human Pose Estimation | Multi-Hypothesis Transformer for 3D Human Pose Estimation - arXiv | https://arxiv.org/abs/2111.12707 | We propose a Multi-Hypothesis Transformer (MHFormer) that learns spatio-temporal representations of multiple plausible pose hypotheses. |
Learning Pyramid-structured Long-range Dependencies for 3D Human Pose
Estimation | 2506.02853v1 | liu2023posynda | \cite{liu2023posynda} | PoSynDA: Multi-Hypothesis Pose Synthesis Domain Adaptation for Robust 3D
Human Pose Estimation | http://arxiv.org/abs/2308.09678v2 | Existing 3D human pose estimators face challenges in adapting to new datasets
due to the lack of 2D-3D pose pairs in training sets. To overcome this issue,
we propose \textit{Multi-Hypothesis \textbf{P}ose \textbf{Syn}thesis
\textbf{D}omain \textbf{A}daptation} (\textbf{PoSynDA}) framework to bridge
this data disparity... | true | true | Liu, Hanbing and He, Jun-Yan and Cheng, Zhi-Qi and Xiang, Wangmeng and Yang, Qize and Chai, Wenhao and Wang, Gaoang and Bao, Xu and Luo, Bin and Geng, Yifeng and others | 2,023 | null | null | null | null | PoSynDA: Multi-Hypothesis Pose Synthesis Domain Adaptation for Robust 3D
Human Pose Estimation | PoSynDA: Multi-Hypothesis Pose Synthesis Domain ... | https://github.com/hbing-l/PoSynDA | PoSynDA is a novel framework for 3D Human Pose Estimation (3D HPE) that addresses the challenges of adapting to new datasets due to the scarcity of 2D-3D |
Learning Pyramid-structured Long-range Dependencies for 3D Human Pose
Estimation | 2506.02853v1 | chen2023hdformer | \cite{chen2023hdformer} | HDFormer: High-order Directed Transformer for 3D Human Pose Estimation | http://arxiv.org/abs/2302.01825v2 | Human pose estimation is a challenging task due to its structured data
sequence nature. Existing methods primarily focus on pair-wise interaction of
body joints, which is insufficient for scenarios involving overlapping joints
and rapidly changing poses. To overcome these issues, we introduce a novel
approach, the High... | true | true | Chen, Hanyuan and He, Jun-Yan and Xiang, Wangmeng and Cheng, Zhi-Qi and Liu, Wei and Liu, Hanbing and Luo, Bin and Geng, Yifeng and Xie, Xuansong | 2,023 | null | null | null | null | HDFormer: High-order Directed Transformer for 3D Human Pose Estimation | High-order Directed Transformer for 3D Human Pose Estimation | https://arxiv.org/abs/2302.01825 | HDFormer is a novel approach for 3D human pose estimation using high-order bone and joint relationships, addressing issues with overlapping |
Learning Pyramid-structured Long-range Dependencies for 3D Human Pose
Estimation | 2506.02853v1 | hu2021conditional | \cite{hu2021conditional} | Conditional Directed Graph Convolution for 3D Human Pose Estimation | http://arxiv.org/abs/2107.07797v2 | Graph convolutional networks have significantly improved 3D human pose
estimation by representing the human skeleton as an undirected graph. However,
this representation fails to reflect the articulated characteristic of human
skeletons as the hierarchical orders among the joints are not explicitly
presented. In this p... | true | true | Hu, Wenbo and Zhang, Changgong and Zhan, Fangneng and Zhang, Lei and Wong, Tien-Tsin | 2,021 | null | null | null | null | Conditional Directed Graph Convolution for 3D Human Pose Estimation | Conditional Directed Graph Convolution for 3D Human Pose Estimation | http://arxiv.org/pdf/2107.07797v2 | Graph convolutional networks have significantly improved 3D human pose
estimation by representing the human skeleton as an undirected graph. However,
this representation fails to reflect the articulated characteristic of human
skeletons as the hierarchical orders among the joints are not explicitly
presented. In this p... |
Learning Pyramid-structured Long-range Dependencies for 3D Human Pose
Estimation | 2506.02853v1 | ci2019optimizing | \cite{ci2019optimizing} | Optimizing network structure for {3D} human pose estimation | null | null | true | false | Ci, Hai and Wang, Chunyu and Ma, Xiaoxuan and Wang, Yizhou | 2,019 | null | null | null | null | Optimizing network structure for {3D} human pose estimation | Optimizing Network Structure for 3D Human Pose Estimation | https://openaccess.thecvf.com/content_ICCV_2019/papers/Ci_Optimizing_Network_Structure_for_3D_Human_Pose_Estimation_ICCV_2019_paper.pdf | by H Ci · 2019 · Cited by 312 — A 3D human pose is naturally represented by a skele- tal graph parameterized by the 3D locations of the body joints such as elbows and knees. See Figure 1. When |
Learning Pyramid-structured Long-range Dependencies for 3D Human Pose
Estimation | 2506.02853v1 | liu2020comprehensive | \cite{liu2020comprehensive} | A comprehensive study of weight sharing in graph networks for {3D} human pose estimation | null | null | true | false | Liu, Kenkun and Ding, Rongqi and Zou, Zhiming and Wang, Le and Tang, Wei | 2,020 | null | null | null | null | A comprehensive study of weight sharing in graph networks for {3D} human pose estimation | A Comprehensive Study of Weight Sharing in Graph ... | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123550324.pdf | by K Liu · Cited by 182 — Graph convolutional networks (GCNs) have been applied to. 3D human pose estimation (HPE) from 2D body joint detections and have shown encouraging performance. |
Learning Pyramid-structured Long-range Dependencies for 3D Human Pose
Estimation | 2506.02853v1 | wang2018non | \cite{wang2018non} | Non-local Neural Networks | http://arxiv.org/abs/1711.07971v3 | Both convolutional and recurrent operations are building blocks that process
one local neighborhood at a time. In this paper, we present non-local
operations as a generic family of building blocks for capturing long-range
dependencies. Inspired by the classical non-local means method in computer
vision, our non-local o... | true | true | Wang, Xiaolong and Girshick, Ross and Gupta, Abhinav and He, Kaiming | 2,018 | null | null | null | null | Non-local Neural Networks | [PDF] Non-Local Neural Networks - CVF Open Access | https://openaccess.thecvf.com/content_cvpr_2018/papers/Wang_Non-Local_Neural_Networks_CVPR_2018_paper.pdf | Non-local operations capture long-range dependencies by computing a weighted sum of features at all positions, unlike local operations. They are efficient and |
Learning Pyramid-structured Long-range Dependencies for 3D Human Pose
Estimation | 2506.02853v1 | gong2023diffpose | \cite{gong2023diffpose} | DiffPose: Toward More Reliable 3D Pose Estimation | http://arxiv.org/abs/2211.16940v3 | Monocular 3D human pose estimation is quite challenging due to the inherent
ambiguity and occlusion, which often lead to high uncertainty and
indeterminacy. On the other hand, diffusion models have recently emerged as an
effective tool for generating high-quality images from noise. Inspired by their
capability, we expl... | true | true | Gong, Jia and Foo, Lin Geng and Fan, Zhipeng and Ke, Qiuhong and Rahmani, Hossein and Liu, Jun | 2,023 | null | null | null | null | DiffPose: Toward More Reliable 3D Pose Estimation | DiffPose: Toward More Reliable 3D Pose Estimation | http://arxiv.org/pdf/2211.16940v3 | Monocular 3D human pose estimation is quite challenging due to the inherent
ambiguity and occlusion, which often lead to high uncertainty and
indeterminacy. On the other hand, diffusion models have recently emerged as an
effective tool for generating high-quality images from noise. Inspired by their
capability, we expl... |
Learning Pyramid-structured Long-range Dependencies for 3D Human Pose
Estimation | 2506.02853v1 | holmquist2023diffpose | \cite{holmquist2023diffpose} | DiffPose: Multi-hypothesis Human Pose Estimation using Diffusion models | http://arxiv.org/abs/2211.16487v1 | Traditionally, monocular 3D human pose estimation employs a machine learning
model to predict the most likely 3D pose for a given input image. However, a
single image can be highly ambiguous and induces multiple plausible solutions
for the 2D-3D lifting step which results in overly confident 3D pose
predictors. To this... | true | true | Holmquist, Karl and Wandt, Bastian | 2,023 | null | null | null | null | DiffPose: Multi-hypothesis Human Pose Estimation using Diffusion models | Multi-hypothesis Human Pose Estimation using Diffusion models | https://arxiv.org/abs/2211.16487 | We propose \emph{DiffPose}, a conditional diffusion model, that predicts multiple hypotheses for a given input image. |
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