Marktechpost

AUGUST 2, 2024

A major issue in RL is the data scarcity in embodied AI, where agents must interact with physical environments. This problem is exacerbated by the need for substantial reward-labeled data to train agents effectively.

Machine Learning 130

Machine Learning Data Scarcity Large Language Models Robotics 130

Marktechpost

MARCH 1, 2024

However, the scarcity and limited annotation of 3D data present significant challenges for the development and impact of 3D pretraining. One straightforward solution to address the data scarcity issue is to merge multiple existing 3D datasets and employ the combined data for universal 3D backbone pretraining.

Data Scarcity 109

Data Scarcity Natural Language Processing Deep Learning Artificial Intelligence 109

Unite.AI

JULY 5, 2024

As the technology continues to evolve, it promises to unlock new possibilities in AI research and application development, while addressing critical challenges related to data scarcity and privacy.

LLM 259

LLM Prompt Engineer Prompt Engineering Data Scarcity 259

Marktechpost

JULY 22, 2024

However, acquiring such datasets presents significant challenges, including data scarcity, privacy concerns, and high data collection and annotation costs. Artificial (synthetic) data has emerged as a promising solution to these challenges, offering a way to generate data that mimics real-world patterns and characteristics.

AI Research 120

AI Research AI Researcher Data Scarcity Prompt Engineer 120

Marktechpost

MARCH 26, 2024

In conclusion, the LLM2LLM framework offers a robust solution to the critical challenge of data scarcity. By harnessing the power of one LLM to improve another, it demonstrates a novel, efficient pathway to fine-tune models for specific tasks with limited initial data. Similarly, on the CaseHOLD dataset, there was a 32.6%

Large Language Models 132

Large Language Models Data Scarcity Natural Language Processing LLM 132

Marktechpost

OCTOBER 22, 2024

The dataset was designed to address the major challenges of multilingual multimodal learning: data scarcity, cultural nuances, catastrophic forgetting, and evaluation complexity. Moreover, PANGEA matches or even outperforms proprietary models like Gemini-1.5-Pro

Large Language Models 103

Large Language Models Data Scarcity Inference Engine LLM 103

Marktechpost

AUGUST 3, 2023

The post Meet LP-MusicCaps: A Tag-to-Pseudo Caption Generation Approach with Large Language Models to Address the Data Scarcity Issue in Automatic Music Captioning appeared first on MarkTechPost.

Data Scarcity 52

Data Scarcity Large Language Models BERT Natural Language Processing 52

Marktechpost

FEBRUARY 29, 2024

Data scarcity in low-resource languages can be mitigated using word-to-word translations from high-resource languages. However, bilingual lexicons typically need more overlap with task data, leading to inadequate translation coverage. Check out the Paper.

Artificial Intelligence 96

Artificial Intelligence Artificial Intelligence Data Scarcity NLP 96

Marktechpost

APRIL 27, 2024

A few-shot evaluation further confirms FLORA’s proficiency in managing data scarcity and distribution variability, showcasing its robust performance even with limited training examples. In conclusion, FLORA presents a promising solution to the challenge of training vision-language models in federated learning settings.

Machine Learning 133

Machine Learning Data Scarcity Data Mining AI 133

Marktechpost

JANUARY 29, 2024

Other effective strategies to address data scarcity include vocabulary extension and ongoing pretraining. An important milestone was reached when the XLM-R auto-encoding model was introduced with 278M parameters with language coverage from 100 languages to 534 languages.

Large Language Models 132

Large Language Models Data Scarcity Artificial Intelligence Artificial Intelligence 132

Marktechpost

JANUARY 15, 2024

Developed by researchers from Apple, aiming to enhance machine translation, AlignInstruct represents a paradigm shift in tackling data scarcity. This is where the novel concept of contrastive alignment instructions, or AlignInstruct, comes into play.

Large Language Models 129

Large Language Models Data Scarcity Computational Linguistics Natural Language Processing 129

Marktechpost

APRIL 17, 2024

Synthetic data has been identified as a pivotal solution to this challenge, promising to bridge the gap caused by data scarcity, privacy issues, and the high costs associated with data acquisition.

Data Scarcity 120

Data Scarcity Artificial Intelligence Artificial Intelligence AI Modeling 120

Marktechpost

MAY 11, 2024

Low-resource settings: Linguistic knowledge is essential for addressing issues with data scarcity and linguistic variance in linguistically varied or low-resource languages. Proficiency in language ensures that NLP assessments encompass not just performance at the surface level but also more profound linguistic issues.

Large Language Models 128

Large Language Models NLP Data Scarcity Computational Linguistics 128

Marktechpost

APRIL 5, 2024

However, there’s potential to significantly improve models for smaller languages through multilingual training, which could mitigate the data scarcity issue.

Data Scarcity 130

Data Scarcity AI Modeling AI AI 130

Marktechpost

DECEMBER 28, 2024

These challenges are compounded by data scarcity in advanced mathematics and the inherent difficulty of verifying intricate logical reasoning. By grounding reasoning in formal logic, these methods create a robust framework for tackling abstract mathematical challenges while addressing data scarcity and correctness verification issues.

Data Scarcity 66

Data Scarcity Natural Language Processing Artificial Intelligence Artificial Intelligence 66

Marktechpost

DECEMBER 31, 2023

He highlighted the necessity for effective data use by stressing the significant amount of data many AI systems consume. Another researcher highlighted the challenge of considering AI model-free due to market data scarcity for training, particularly in realistic derivative markets.

Neural Network 120

Neural Network Data Scarcity Artificial Intelligence Artificial Intelligence 120

Marktechpost

JULY 8, 2024

In conclusion, the research conducted by Cohere For AI demonstrates the critical importance of high-quality, diverse, multilingual data in training effective multilingual language models.

Data Scarcity 131

Data Scarcity Large Language Models Natural Language Processing NLP 131

Unite.AI

JANUARY 22, 2024

However, generating synthetic data for NLP is non-trivial, demanding high linguistic knowledge, creativity, and diversity. Different methods, such as rule-based and data-driven approaches, have been proposed to generate synthetic data.

NLP 173

NLP BERT Data Scarcity Large Language Models 173

Marktechpost

OCTOBER 11, 2024

Using GANs to generate high-quality synthetic data, Distilabel addresses key issues such as data scarcity, bias, and privacy concerns. Overall, the study presents Distilabel as a robust solution to the challenges of dataset creation.

Data Scarcity 109

Data Scarcity Neural Network Natural Language Processing Machine Learning 109

Marktechpost

JULY 23, 2023

Data scarcity and data imbalance are two of these challenges. Despite the growing interest in developing ML models for medical imaging, significant challenges can limit such models’ practical applications or even predispose them to substantial bias.

Machine Learning 97

Machine Learning Data Scarcity Algorithm AI Research 97

Marktechpost

MARCH 16, 2024

This method leverages pre-trained generative text and image models to create synthetic paired data for VLMs, addressing data scarcity, cost, and noise challenges. It generates both text and images synthetically, avoiding reliance on real-world data. The researchers from Google DeepMind have proposed Synth2.

Data Scarcity 104

Data Scarcity Computer Vision ML Artificial Intelligence 104

Marktechpost

DECEMBER 30, 2023

link] To conclude, the TF-T2V framework offers several key advantages: It innovatively utilizes text-free videos, addressing the data scarcity issue prevalent in the field. The dual-branch structure, focusing on spatial appearance and motion dynamics, generates high-quality, coherent video.

Data Scarcity 117

Data Scarcity Computer Vision Artificial Intelligence Artificial Intelligence 117

Marktechpost

AUGUST 12, 2024

The success of VulScribeR highlights the importance of large-scale data augmentation in the field of vulnerability detection. By generating diverse and realistic vulnerable code samples, this approach provides a practical solution to the data scarcity problem that has long hindered the development of effective DLVD models.

Large Language Models 109

Large Language Models Data Scarcity Software Engineer LLM 109

Marktechpost

JULY 18, 2024

In conclusion, YouTube-SL-25 is a pivotal advancement in sign language research, addressing the longstanding data scarcity issue. The dataset’s open-domain nature allows for broad applications, from general sign language pretraining to medium-quality finetuning for specific tasks such as translation and caption alignment.

Data Scarcity 120

Data Scarcity Machine Learning ML Large Language Models 120

Marktechpost

FEBRUARY 28, 2024

By aligning the embedding space of unimodal FMs through cross-modal transformation models utilizing KG triplets, BioBRIDGE maintains data sufficiency and efficiency and navigates the challenges posed by computational costs and data scarcity that hinder the scalability of multimodal approaches.

Machine Learning 101

Machine Learning AI Research AI Researcher Data Scarcity 101

Marktechpost

MAY 8, 2024

Availability of training data: Deep learning’s efficacy relies heavily on data quality, with simulation environments bridging the gap between real-world data scarcity and training requirements.

Deep Learning 122

Deep Learning Neural Network Data Scarcity Natural Language Processing 122

Marktechpost

NOVEMBER 9, 2023

They also make available a sizable collection of artificially photorealistic photos matched with ground truth labels for these kinds of signals to overcome data scarcity. Despite relying just on silhouettes, which are devoid of geometric information, they use surface normals and key points as supplementary clues.

Data Scarcity 127

Data Scarcity Computer Vision AI AI 127

Marktechpost

MAY 28, 2024

The DLM’s innovative use of synthetic data addresses the data scarcity issue that has hampered the performance of earlier error correction models. This approach significantly exceeds previous attempts and achieves state-of-the-art performance in ASR systems.

Data Scarcity 109

Data Scarcity Large Language Models Machine Learning Algorithm 109

Unite.AI

SEPTEMBER 12, 2024

Data scarcity is another significant issue. Gathering large volumes of labeled data in many fields is complicated, time-consuming, and costly. This is particularly true in diverse real-world situations. For example, a facial recognition system trained mainly on one demographic may struggle with others, leading to biased results.

Data Quality 162

Data Quality Data Scarcity AI AI 162

Marktechpost

AUGUST 2, 2024

With its extensive language training and romanization technique, the MMS Zero-shot method offers a promising solution to the data scarcity challenge, advancing the field towards more inclusive and universal speech recognition systems.

Data Scarcity 108

Data Scarcity AI Modeling AI AI 108

Marktechpost

DECEMBER 1, 2024

By enabling straightforward generation of synthetic datasets, it allows organizations to experiment and train models without being hindered by data scarcity or privacy restrictions. Synthetic data is particularly useful in situations where collecting real data is too costly, ethically challenging, or impractical.

Python 90

Python LLM Data Scarcity Data Scientist 90

Marktechpost

SEPTEMBER 2, 2023

They optimize the LVLM using synthesized anomalous visual-textual data and incorporating IAD expertise. Direct training using IAD data, however, needs to be improved. Data scarcity is the first. With just a few normal samples, AnomalyGPT can also learn in context, allowing for quick adjustment to new objects.

Data Scarcity 123

Data Scarcity Large Language Models Natural Language Processing LLM 123

Marktechpost

FEBRUARY 27, 2024

For instance, BloomberGPT excels in finance with private financial data spanning 40 years. Collaborative training on decentralized personal data, without direct sharing, emerges as a critical approach to support the development of modern LLMs amid data scarcity and privacy concerns.

Large Language Models 100

Large Language Models Machine Learning Data Scarcity Algorithm 100

Machine Learning Research at Apple

MAY 10, 2023

Self-training has been shown to be helpful in addressing data scarcity for many domains, including vision, speech, and language. Specifically, self-training, or pseudo-labeling, labels unsupervised data and adds that to the training pool.

Data Scarcity 64

Data Scarcity 64

Marktechpost

APRIL 23, 2024

Unlike conventional methods, this approach utilizes Bayesian inference and Monte Carlo techniques to effectively manage uncertainty and data scarcity.

Deep Learning 99

Deep Learning Data Scarcity Artificial Intelligence Artificial Intelligence 99

Marktechpost

MARCH 11, 2024

The developed CAT-SD scheme effectively mitigates catastrophic forgetting, addresses data scarcity, and ensures privacy in medical datasets. In conclusion, this study introduces a novel privacy-preserving synthetic continual semantic segmentation approach for robotic instrument segmentation.

Neural Network 105

Neural Network Continuous Learning Robotics Data Scarcity 105

Marktechpost

JUNE 19, 2024

Transfer learning and ensemble methods address challenges like overfitting, underfitting, and data scarcity. ML facilitates the exploration of vast unanalyzed datasets, promising new strategies in bioprocess development.

Machine Learning 104

Machine Learning Neural Network Data Scarcity ML 104