Sure! I'd be happy to provide you with an introduction to machine learning basics for beginners. Machine learning is a subfield of artificial intelligence (AI) that focuses on enabling computers to learn and make predictions or decisions without being explicitly programmed. Here are some key concepts and terms to help you get started:

1. Supervised Learning: In supervised learning, the machine learning algorithm learns from labeled training data. The training data consists of input examples and their corresponding correct output or target values. The algorithm learns to generalize from this data and make predictions or classify new, unseen examples.

2. Unsupervised Learning: Unsupervised learning involves learning patterns and relationships from unlabeled data. Unlike supervised learning, there are no target values provided. Instead, the algorithm aims to discover inherent structures or clusters in the data.

3. Training Data and Test Data: Machine learning models require a dataset to learn from. The dataset is typically split into two parts: the training data and the test data. The model learns from the training data, and the test data is used to evaluate its performance and generalization ability.

4. Features and Labels: In supervised learning, the input examples are often represented by features or attributes. For example, in a spam email classification task, features might include the presence of certain keywords or the length of the email. The corresponding output or target values are called labels, indicating the class or category to which the example belongs (e.g., spam or not spam).

5. Model Evaluation Metrics: To assess the performance of a machine learning model, various evaluation metrics are used. Common metrics include accuracy (the proportion of correctly predicted examples), precision (the proportion of true positives among all positive predictions), recall (the proportion of true positives predicted correctly), and F1 score (a combination of precision and recall).

6. Overfitting and Underfitting: Overfitting occurs when a model becomes too complex and learns to memorize the training data instead of generalizing well to unseen examples. On the other hand, underfitting happens when a model is too simple and fails to capture the underlying patterns in the data. Balancing the complexity of the model is crucial to achieve good generalization.

7. Feature Engineering: Feature engineering involves selecting or creating relevant features that can help improve the performance of a machine learning model. It often requires domain knowledge and creativity to transform raw data into a suitable representation that captures the important information.

8. Bias and Variance Trade-off: The bias-variance trade-off is a fundamental concept in machine learning. Bias refers to the errors introduced by the model's assumptions and simplifications, while variance refers to the model's sensitivity to small fluctuations in the training data. Reducing bias may increase variance and vice versa. Finding the right balance is important for building a well-performing model.

9. Supervised Learning Algorithms: There are various supervised learning algorithms, including linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), and neural networks. Each algorithm has its own strengths, weaknesses, and specific use cases.

10. Unsupervised Learning Algorithms: Unsupervised learning algorithms include clustering algorithms like k-means clustering and hierarchical clustering, dimensionality reduction techniques like principal component analysis (PCA) and t-SNE, and anomaly detection algorithms, among others.

These concepts provide a starting point for understanding the basics of machine learning. As you delve deeper, you can explore more advanced topics such as deep learning, reinforcement learning, and natural language processing. Remember to practice hands-on with real-world datasets to gain practical experience and further refine your skills.

The Unlabeled AnuraSet (U-AnuraSet) is an extension of the original AnuraSet dataset. It consists of soundscape recordings from passive acoustic monitoring conducted in Brazil. The recording sites are identical to those in the original AnuraSet. Each site comprises 2,666 one-minute raw audio files of unlabeled data. The U-AnuraSet is publicly available to encourage machine learning researchers to explore innovative methods for leveraging unlabeled data in the training of models aimed at solving problems such as anuran call identification.

If you find the Unlabeled AnuraSet useful for your research, please consider citing it as follows:

Cañas, J.S., Toro-Gómez, M.P., Sugai, L.S.M., et al. A dataset for benchmarking Neotropical anuran calls identification in passive acoustic monitoring. Sci Data 10, 771 (2023). https://doi.org/10.1038/s41597-023-02666-2

Clustering is an unsupervised machine learning technique whose goal is to cluster unlabeled data. But traditional clustering methods only output a set of results and do not provide any explanations of the results. Although in the literature a number of methods based on decision tree have been proposed to explain the clustering results, most of them have some disadvantages, such as too many branches and too deep leaves, which lead to complex explanations and make it difficult for users to understand. In this paper, a hypercube overlay model based on multi-objective optimization is proposed to achieve succinct explanations of clustering results. The model designs two objective functions based on the number of hypercubes and the compactness of instances and then uses multi-objective optimization to find a set of nondominated solutions. Finally, an Utopia point is defined to determine the most suitable solution, in which each cluster can be covered by as few hypercubes as possible. Based on these hypercubes, an explanations of each cluster is provided. Upon verification on synthetic and real datasets respectively, it shows that the model can provide a concise and understandable explanations to users.

The Dataset

These datasets were used while writing the following work:

Polo, F. M., Ciochetti, I., and Bertolo, E. (2021). Predicting legal proceedings status: approaches based on sequential text data. In Proceedings of the Eighteenth International Conference on Artificial Intelligence and Law, pages 264–265.

Please cite us if you use our datasets in your academic work:

@inproceedings{polo2021predicting,
 title={Predicting legal proceedings status: approaches based on sequential text data},
 author={Polo, Felipe Maia and Ciochetti, Itamar and Bertolo, Emerson},
 booktitle={Proceedings of the Eighteenth International Conference on Artificial Intelligence and Law},
 pages={264--265},
 year={2021}
}

More details below!

Context

Every legal proceeding in Brazil is one of three possible classes of status: (i) archived proceedings, (ii) active proceedings, and (iii) suspended proceedings. The three possible classes are given in a specific instant in time, which may be temporary or permanent. Moreover, they are decided by the courts to organize their workflow, which in Brazil may reach thousands of simultaneous cases per judge. Developing machine learning models to classify legal proceedings according to their status can assist public and private institutions in managing large portfolios of legal proceedings, providing gains in scale and efficiency.

In this dataset, each proceeding is made up of a sequence of short texts called “motions” written in Portuguese by the courts’ administrative staff. The motions relate to the proceedings, but not necessarily to their legal status.

Content

Our data is composed of two datasets: a dataset of ~3*10^6 unlabeled motions and a dataset containing 6449 legal proceedings, each with an individual and a variable number of motions, but which have been labeled by lawyers. Among the labeled data, 47.14% is classified as archived (class 1), 45.23% is classified as active (class 2), and 7.63% is classified as suspended (class 3).

The datasets we use are representative samples from the first (São Paulo) and third (Rio de Janeiro) most significant state courts. State courts handle the most variable types of cases throughout Brazil and are responsible for 80% of the total amount of lawsuits. Therefore, these datasets are a good representation of a very significant portion of the use of language and expressions in Brazilian legal vocabulary.

Regarding the labels dataset, the key "-1" denotes the most recent text while "-2" the second most recent and so on.

Acknowledgements

We would like to thank Ana Carolina Domingues Borges, Andrews Adriani Angeli, and Nathália Caroline Juarez Delgado from Tikal Tech for helping us to obtain the datasets. This work would not be possible without their efforts.

Inspiration

Can you develop good machine learning classifiers for text sequences? :)

These four labeled data sets are targeted at ordinal quantification. The goal of quantification is not to predict the label of each individual instance, but the distribution of labels in unlabeled sets of data.

With the scripts provided, you can extract CSV files from the UCI machine learning repository and from OpenML. The ordinal class labels stem from a binning of a continuous regression label.

We complement this data set with the indices of data items that appear in each sample of our evaluation. Hence, you can precisely replicate our samples by drawing the specified data items. The indices stem from two evaluation protocols that are well suited for ordinal quantification. To this end, each row in the files app_val_indices.csv, app_tst_indices.csv, app-oq_val_indices.csv, and app-oq_tst_indices.csv represents one sample.

Our first protocol is the artificial prevalence protocol (APP), where all possible distributions of labels are drawn with an equal probability. The second protocol, APP-OQ, is a variant thereof, where only the smoothest 20% of all APP samples are considered. This variant is targeted at ordinal quantification tasks, where classes are ordered and a similarity of neighboring classes can be assumed.

Usage

You can extract four CSV files through the provided script extract-oq.jl, which is conveniently wrapped in a Makefile. The Project.toml and Manifest.toml specify the Julia package dependencies, similar to a requirements file in Python.

Preliminaries: You have to have a working Julia installation. We have used Julia v1.6.5 in our experiments.

Data Extraction: In your terminal, you can call either

make

(recommended), or

julia --project="." --eval "using Pkg; Pkg.instantiate()"
julia --project="." extract-oq.jl

Outcome: The first row in each CSV file is the header. The first column, named "class_label", is the ordinal class.

Further Reading

Implementation of our experiments: https://github.com/mirkobunse/regularized-oq

This dataset, titled "Network Anomaly Dataset," is designed for the development and evaluation of machine learning models focused on network anomaly detection. The dataset is available in two versions: a labeled version where each instance is marked as "Anomaly" or "Normal," and an unlabeled version that can be used for unsupervised learning techniques.

Dataset Features: - Throughput: The amount of data successfully transmitted over a network in a given period. - Congestion: The degree of network traffic load, potentially leading to delays or packet loss. - Packet Loss: The percentage of packets that fail to reach their destination, indicative of network issues. - Latency: The time taken for data to travel from the source to the destination, crucial for time-sensitive applications. - Jitter: The variation in packet arrival times, affecting the quality of real-time communications.

Applications: - Supervised Learning: Use the labeled dataset to train and evaluate models such as Random Forest, SVM, and Logistic Regression for anomaly detection. - Unsupervised Learning: Apply techniques like clustering and change point detection on the unlabeled dataset to discover hidden patterns and anomalies.

This dataset is ideal for practitioners and researchers aiming to explore network security, develop robust anomaly detection models, or conduct comparative analysis between supervised and unsupervised learning methods.

Machine learning‐based behaviour classification using acceleration data is a powerful tool in bio‐logging research. Deep learning architectures such as convolutional neural networks (CNN), long short‐term memory (LSTM) and self‐attention mechanisms as well as related training techniques have been extensively studied in human activity recognition. However, they have rarely been used in wild animal studies. The main challenges of acceleration‐based wild animal behaviour classification include data shortages, class imbalance problems, various types of noise in data due to differences in individual behaviour and where the loggers were attached and complexity in data due to complex animal‐specific behaviours, which may have limited the application of deep learning techniques in this area. To overcome these challenges, we explored the effectiveness of techniques for efficient model training: data augmentation, manifold mixup and pre‐training of deep learning models with unlabelled data, using datasets from two species of wild seabirds and state‐of‐the‐art deep learning model architectures. Data augmentation improved the overall model performance when one of the various techniques (none, scaling, jittering, permutation, time‐warping and rotation) was randomly applied to each data during mini‐batch training. Manifold mixup also improved model performance, but not as much as random data augmentation. Pre‐training with unlabelled data did not improve model performance. The state‐of‐the‐art deep learning models, including a model consisting of four CNN layers, an LSTM layer and a multi‐head attention layer, as well as its modified version with shortcut connection, showed better performance among other comparative models. Using only raw acceleration data as inputs, these models outperformed classic machine learning approaches that used 119 handcrafted features. Our experiments showed that deep learning techniques are promising for acceleration‐based behaviour classification of wild animals and highlighted some challenges (e.g. effective use of unlabelled data). There is scope for greater exploration of deep learning techniques in wild animal studies (e.g. advanced data augmentation, multimodal sensor data use, transfer learning and self‐supervised learning). We hope that this study will stimulate the development of deep learning techniques for wild animal behaviour classification using time‐series sensor data.

This abstract is cited from the original article "Exploring deep learning techniques for wild animal behaviour classification using animal-borne accelerometers" in Methods in Ecology and Evolution (Otsuka et al., 2024).Please see README for the details of the datasets.

Market Size and Growth: The global market for Machine Learning (ML) in Chip Design is projected to reach USD 19.7 billion by 2033, registering a CAGR of 25.2% from 2025 to 2033. This growth is attributed to the increasing demand for faster, more power-efficient chips and the ability of ML to automate and optimize the chip design process. Key drivers include the need to reduce design time and cost, improve performance, and address emerging technologies such as AI and IoT. Market Segmentation and Trends: Based on type, supervised learning is expected to dominate the market due to its wide applications in chip design, including design rule checking, yield prediction, and fault diagnosis. Semi-supervised learning is gaining traction as it combines labeled and unlabeled data for training, offering improved accuracy. Unsupervised learning and reinforcement learning are also finding use in chip design, particularly in areas such as auto layout and routing. Major chipmakers such as Intel, NVIDIA, and Cadence Design Systems are investing heavily in ML technologies to enhance their chip design capabilities. Additionally, the adoption of ML in foundries is growing as they seek to improve yield and efficiency for their customers. This comprehensive report provides an in-depth analysis of the Machine Learning in Chip Design market, offering insights into key market dynamics, regional trends, growth drivers, and competitive landscapes. Covering the period from 2023 to 2029, the report forecasts market size and growth to assist businesses in making strategic decisions and capturing untapped opportunities.

The main task of clustering is to identify natural groups within an unlabeled dataset. This means that clustering is an unsupervised machine learning task, which is important in many scientific, engineering, and business domains and this dataset is suitable for this task.

Dataset belonging to the paper: Data-Driven Machine Learning-Informed Framework for Model Predictive Control in Vehicles

labeled_seed.csv: Processed and labeled data of all maneuvers combined into a single file, sorted by label

raw_track_session.csv: Untouched CSV file from Racebox track session

unlabeled_exemplar.csv: Processed but unlabeled data of street and track data

Weed Detection (Unsupervised + Supervised Learning)

Overview

This dataset is designed to support both supervised and unsupervised learning for the task of weed detection in crop fields. It provides labeled data in YOLO format suitable for training object detection models, unlabeled data for semi-supervised or unsupervised learning, and a separate test set for evaluation. The objective is to detect and distinguish between weed and crop instances using deep learning models like YOLOv5 or YOLOv8.

Dataset Structure

│ ├── labeled/ │ ├── images/ # Labeled images for training │ └── labels/ # YOLO-format annotations │ ├── unlabeled/ # Unlabeled images for unsupervised or semi-supervised learning │ └── test/ ├── images/ # Test images └── labels/ # Ground truth annotations in YOLO format

Clustering is an unsupervised machine learning technique whose goal is to cluster unlabeled data. But traditional clustering methods only output a set of results and do not provide any explanations of the results. Although in the literature a number of methods based on decision tree have been proposed to explain the clustering results, most of them have some disadvantages, such as too many branches and too deep leaves, which lead to complex explanations and make it difficult for users to understand. In this paper, a hypercube overlay model based on multi-objective optimization is proposed to achieve succinct explanations of clustering results. The model designs two objective functions based on the number of hypercubes and the compactness of instances and then uses multi-objective optimization to find a set of nondominated solutions. Finally, an Utopia point is defined to determine the most suitable solution, in which each cluster can be covered by as few hypercubes as possible. Based on these hypercubes, an explanations of each cluster is provided. Upon verification on synthetic and real datasets respectively, it shows that the model can provide a concise and understandable explanations to users.

AI in Unsupervised Learning Market Outlook

According to our latest research, the AI in Unsupervised Learning market size reached USD 3.8 billion globally in 2024, demonstrating robust expansion as organizations increasingly leverage unsupervised techniques for extracting actionable insights from unlabelled data. The market is forecasted to grow at a CAGR of 28.2% from 2025 to 2033, propelling the industry to an estimated USD 36.7 billion by 2033. This remarkable growth trajectory is primarily fueled by the escalating adoption of artificial intelligence across diverse sectors, an exponential surge in data generation, and the pressing need for advanced analytics that can operate without manual data labeling.

One of the key growth factors driving the AI in Unsupervised Learning market is the rising complexity and volume of data generated by enterprises in the digital era. Organizations are inundated with unstructured and unlabelled data from sources such as social media, IoT devices, and transactional systems. Traditional supervised learning methods are often impractical due to the time and cost associated with manual labeling. Unsupervised learning algorithms, such as clustering and dimensionality reduction, offer a scalable solution by autonomously identifying patterns, anomalies, and hidden structures within vast datasets. This capability is increasingly vital for industries aiming to enhance decision-making, streamline operations, and gain a competitive edge through advanced analytics.

Another significant driver is the rapid advancement in computational power and AI infrastructure, which has made it feasible to implement sophisticated unsupervised learning models at scale. The proliferation of cloud computing and specialized AI hardware has reduced barriers to entry, enabling even small and medium enterprises to deploy unsupervised learning solutions. Additionally, the evolution of neural networks and deep learning architectures has expanded the scope of unsupervised algorithms, allowing for more complex tasks such as image recognition, natural language processing, and anomaly detection. These technological advancements are not only accelerating adoption but also fostering innovation across sectors including healthcare, finance, manufacturing, and retail.

Furthermore, regulatory compliance and the growing emphasis on data privacy are pushing organizations to adopt unsupervised learning methods. Unlike supervised approaches that require sensitive data labeling, unsupervised algorithms can process data without explicit human intervention, thereby reducing the risk of privacy breaches. This is particularly relevant in sectors such as healthcare and BFSI, where stringent data protection regulations are in place. The ability to derive insights from unlabelled data while maintaining compliance is a compelling value proposition, further propelling the market forward.

Regionally, North America continues to dominate the AI in Unsupervised Learning market owing to its advanced technological ecosystem, significant investments in AI research, and strong presence of leading market players. Europe follows closely, driven by robust regulatory frameworks and a focus on ethical AI deployment. The Asia Pacific region is exhibiting the fastest growth, fueled by rapid digital transformation, government initiatives, and increasing adoption of AI across industries. Latin America and the Middle East & Africa are also witnessing steady growth, albeit at a slower pace, as awareness and infrastructure continue to develop.

Component Analysis

The Component segment of the AI in Unsupervised Learning market is categorized into Software, Hardware, and Services, each playing a pivotal role in the overall ecosystem. The software segment, comprising machine learning frameworks, data analytics platforms, and AI development tools, holds the largest market share. This dominance is attributed to the continuous evolution of AI algorithms and the increasing availability of open-source and proprietary solutions tailored for unsupervised learning. Enterprises are investing heavily in software that can facilitate the seamless integration of unsupervised learning capabilities into existing workflows, enabling automation, predictive analytics, and pattern recognition without the need for labeled data.

The hardware segment, while smaller in comparison to software, is experiencing significant growth due to the escalating demand for high-perf

Self-Supervised Learning Market Outlook

According to our latest research, the global self-supervised learning market size reached USD 10.2 billion in 2024, demonstrating rapid adoption across multiple sectors. The market is set to expand at a strong CAGR of 33.1% from 2025 to 2033, propelled by the growing need for advanced artificial intelligence solutions that minimize dependency on labeled data. By 2033, the market is forecasted to achieve an impressive size of USD 117.2 billion, underscoring the transformative potential of self-supervised learning in revolutionizing data-driven decision-making and automation across industries. This growth trajectory is supported by increasing investments in AI research, the proliferation of big data, and the urgent demand for scalable machine learning models.

The primary growth driver for the self-supervised learning market is the exponential surge in data generation across industries and the corresponding need for efficient data labeling techniques. Traditional supervised learning requires vast amounts of labeled data, which is both time-consuming and expensive to annotate. Self-supervised learning, by contrast, leverages unlabeled data to train models, significantly reducing operational costs and accelerating the deployment of AI systems. This paradigm shift is particularly critical in sectors like healthcare, finance, and autonomous vehicles, where large datasets are abundant but labeled examples are scarce. As organizations seek to unlock value from their data assets, self-supervised learning is emerging as a cornerstone technology, enabling more robust, scalable, and generalizable AI applications.

Another significant factor fueling market expansion is the rapid advancement in computing infrastructure and algorithmic innovation. The availability of high-performance hardware, such as GPUs and TPUs, coupled with breakthroughs in neural network architectures, has made it feasible to train complex self-supervised models on massive datasets. Additionally, the open-source movement and collaborative research have democratized access to state-of-the-art self-supervised learning frameworks, fostering innovation and lowering barriers to entry for enterprises of all sizes. These technological advancements are empowering organizations to experiment with self-supervised learning at scale, driving adoption across a wide range of applications, from natural language processing to computer vision and robotics.

The market is also benefiting from the growing emphasis on ethical AI and data privacy. Self-supervised learning methods, which minimize the need for sensitive labeled data, are increasingly being adopted to address privacy concerns and regulatory compliance requirements. This is particularly relevant in regions with stringent data protection regulations, such as the European Union. Furthermore, the ability of self-supervised learning to generalize across domains and tasks is enabling businesses to build more resilient and adaptable AI systems, further accelerating market growth. The convergence of these factors is positioning self-supervised learning as a key enabler of next-generation AI solutions.

Transfer Learning is emerging as a pivotal technique in the realm of self-supervised learning, offering a bridge between different domains and tasks. By leveraging knowledge from pre-trained models, transfer learning allows for the adaptation of AI systems to new, related tasks with minimal additional data. This approach is particularly beneficial in scenarios where labeled data is scarce, enabling models to generalize better and learn more efficiently. The integration of transfer learning into self-supervised frameworks is enhancing the ability of AI systems to tackle complex problems across various industries, from healthcare diagnostics to autonomous driving. As the demand for versatile and efficient AI solutions grows, transfer learning is set to play a crucial role in the evolution of self-supervised learning technologies.

From a regional perspective, North America currently leads the self-supervised learning market, accounting for the largest share due to its robust AI research ecosystem, significant investments from technology giants, and early adoption across verticals. However, Asia Pacific is projected to witness the fastest growth over the forecast period, driven by the rapid digital tran

This data repository contains the OCT images and binary annotations for segmentation of retinal tissue using deep learning. To use, please refer to the Github repository https://github.com/theislab/DeepRT.

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Access to large, annotated samples represents a considerable challenge for training accurate deep-learning models in medical imaging. While current leading-edge transfer learning from pre-trained models can help with cases lacking data, it limits design choices, and generally results in the use of unnecessarily large models. We propose a novel, self-supervised training scheme for obtaining high-quality, pre-trained networks from unlabeled, cross-modal medical imaging data, which will allow for creating accurate and efficient models. We demonstrate this by accurately predicting optical coherence tomography (OCT)-based retinal thickness measurements from simple infrared (IR) fundus images. Subsequently, learned representations outperformed advanced classifiers on a separate diabetic retinopathy classification task in a scenario of scarce training data. Our cross-modal, three-staged scheme effectively replaced 26,343 diabetic retinopathy annotations with 1,009 semantic segmentations on OCT and reached the same classification accuracy using only 25% of fundus images, without any drawbacks, since OCT is not required for predictions. We expect this concept will also apply to other multimodal clinical data-imaging, health records, and genomics data, and be applicable to corresponding sample-starved learning problems.

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Dataset Source: https://www.aicrowd.com/challenges/data-purchasing-challenge-2022

🕵️ Introduction Data for machine learning tasks usually does not come for free but has to be purchased. The costs and benefits of data have to be weighed against each other. This is challenging. First, data usually has combinatorial value. For instance, different observations might complement or substitute each other for a given machine learning task. In such cases, the decision to purchase one group of observations has to be made conditional on the decision to purchase another group of observations. If these relationships are high-dimensional, finding the optimal bundle becomes computationally hard. Second, data comes at different quality, for instance, with different levels of noise. Third, data has to be acquired under the assumption of being valuable out-of-sample. Distribution shifts have to be anticipated.

In this competition, you face these data purchasing challenges in the context of an multi-label image classification task in a quality control setting.

📑 Problem Statement

In short: You have to classify images. Some images in your training set are labelled but most of them aren't. How do you decide which images to label if you have a limited budget to do so?

In more detail: You face a multi-label image classification task. The dataset consists of synthetically generated images of painted metal sheets. A classifier is meant to predict whether the sheets have production damages and if so which ones. You have access to a set of images, a subset of which are labelled with respect to production damages. Because labeling is costly and your budget is limited, you have to decide for which of the unlabelled images labels should be purchased in order to maximize prediction accuracy.

Each of the images have a 4 dimensional label representing the presence or the absence of ['scratch_small', 'scratch_large', 'dent_small', 'dent_large'] in the images.

You are required to submit code, which can be run in three different phases:

Pre-Training Phase

In the Pre-Training Phase, your code will have access to 5,000 labelled images on a multi-label image classification task with 4 classes. It is up to you, how you wish to use this data. For instance, you might want to pre-train a classification model. Purchase Phase

In the Purchase Phase, your code, after going through the Pre-Training Phase will have access to an unlabelled dataset of 10,000 images. You will have a budget of 3,000 label purchases, that you can freely use across any of the images in the unlabelled dataset to obtain their labels. You are tasked with designing your own approach on how to select the optimal subset of 3,000 images in the unlabelled dataset, which would help you optimize your model's performance on the prediction task. You can then continue training your model (which has been pre-trained in the pre-training phase) using the newly purchased labels. Prediction Phase

In the Prediction Phase, your code will have access to a test set of 3,000 unlabelled images, for which you have to generate and submit predictions. Your submission will be evaluated based on the performance of your predictions on this test set. Your code will have access to a node with 4 CPUS, 16 GB RAM, 1 NVIDIA T4 GPU and 3 hours of runtime per submission. In the final round of this challenge, your code will be evaluated across multiple budget-runtime constraints.

💾 Dataset

The datasets for this challenge can be accessed in the Resources Section.

training.tar.gz: The training set containing 5,000 images with their associated labels. During your local experiments you are allowed to use the data as you please. unlabelled.tar.gz: The unlabelled set containing 10,000 images, and their associated labels. During your local experiments you are only allowed to access the labels through the provided purchase_label function. validation.tar.gz: The validation set containing 3,000 images, and their associated labels. During your local experiments you are only allowed to use the labels of the validation set to measure the performance of your models and experiments. debug.tar.gz.: A small set of 100 images with their associated labels, that you can use for integration testing, and for trying out the provided starter kit. NOTE While you run your local experiments on this dataset, your submissions will be evaluated on a dataset which might be sampled from a different distribution, and is not the same as this publicly released version.

👥 Participation

🖊 Evaluation Criteria The challenge will use the Accuracy Score, Hamming Loss and the Exact Match Ratio during evaluation. The primary score will be the Accuracy Score.

📅 Timeline This challenge has two Rounds.

Round 1 : Feb 4th – Feb 28th, 2022

The first round submissions will be evaluated based on one budget-compute constraint pair (max. of 3,00...

In the field of polymer informatics, utilizing machine learning (ML) techniques to evaluate the glass transition temperature Tg and other properties of polymers has attracted extensive attention. This data-centric approach is much more efficient and practical than the laborious experimental measurements when encountered a daunting number of polymer structures. Various ML models are demonstrated to perform well for Tg prediction. Nevertheless, they are trained on different data sets, using different structure representations, and based on different feature engineering methods. Thus, the critical question arises on selecting a proper ML model to better handle the Tg prediction with generalization ability. To provide a fair comparison of different ML techniques and examine the key factors that affect the model performance, we carry out a systematic benchmark study by compiling 79 different ML models and training them on a large and diverse data set. The three major components in setting up an ML model are structure representations, feature representations, and ML algorithms. In terms of polymer structure representation, we consider the polymer monomer, repeat unit, and oligomer with longer chain structure. Based on that feature, representation is calculated, including Morgan fingerprinting with or without substructure frequency, RDKit descriptors, molecular embedding, molecular graph, etc. Afterward, the obtained feature input is trained using different ML algorithms, such as deep neural networks, convolutional neural networks, random forest, support vector machine, LASSO regression, and Gaussian process regression. We evaluate the performance of these ML models using a holdout test set and an extra unlabeled data set from high-throughput molecular dynamics simulation. The ML model’s generalization ability on an unlabeled data set is especially focused, and the model’s sensitivity to topology and the molecular weight of polymers is also taken into consideration. This benchmark study provides not only a guideline for the Tg prediction task but also a useful reference for other polymer informatics tasks.

Self-Supervised Learning for Robotic Grasping Market Outlook

According to our latest research, the global market size for Self-Supervised Learning for Robotic Grasping stood at USD 1.38 billion in 2024, with a robust CAGR of 32.8% expected over the forecast period. The market is projected to reach USD 16.35 billion by 2033, driven by rapid advancements in artificial intelligence, increasing automation across industries, and the growing demand for intelligent robotic systems capable of complex manipulation tasks. As per the latest research, significant growth factors include the integration of advanced machine learning models, the expansion of collaborative robotics, and the rising adoption of cloud-based deployment for scalable robotic solutions.

One of the primary growth drivers for the Self-Supervised Learning for Robotic Grasping market is the increasing sophistication of deep learning algorithms, particularly those enabling robots to learn from unlabeled data. Industries such as manufacturing, logistics, and healthcare are increasingly relying on robots that can autonomously improve their grasping capabilities without extensive human intervention. This trend is further accelerated by the need for flexible automation in highly dynamic environments, where traditional supervised learning methods prove costly and time-consuming. The ability of self-supervised learning to reduce dependency on large labeled datasets not only cuts operational costs but also accelerates deployment timelines, making it highly attractive for organizations aiming to maintain a competitive edge.

Another significant factor fueling market growth is the rapid expansion of collaborative and service robots in sectors beyond traditional manufacturing. As e-commerce, food and beverage, and healthcare sectors experience surging demand for automation, there is a rising emphasis on robots that can interact safely and effectively with humans. Self-supervised learning enables these robots to adapt to new objects, environments, and tasks with minimal reprogramming, thereby enhancing their utility across diverse applications. This adaptability is crucial for sectors dealing with highly variable product mixes and unpredictable operational conditions, further solidifying the role of self-supervised learning as a transformative technology in robotic grasping.

The proliferation of cloud-based solutions constitutes another pivotal growth factor in the Self-Supervised Learning for Robotic Grasping market. Cloud-based deployment models offer unparalleled scalability, allowing organizations to leverage vast computational resources for training and updating robotic models. This, in turn, facilitates continuous learning and improvement of robotic systems deployed across geographically dispersed locations. Additionally, the integration of edge computing with cloud platforms ensures real-time responsiveness and data privacy, which are critical for applications in sensitive environments such as healthcare and automotive manufacturing. As a result, cloud-based self-supervised learning solutions are witnessing rapid adoption, especially among enterprises seeking to future-proof their automation strategies.

From a regional perspective, Asia Pacific dominates the Self-Supervised Learning for Robotic Grasping market, accounting for the largest revenue share in 2024. This leadership is attributed to the region’s robust manufacturing ecosystem, aggressive investments in smart factories, and the presence of leading robotics innovators. North America and Europe follow closely, driven by technological advancements, strong R&D infrastructure, and high adoption rates in industries such as automotive and electronics. The Middle East & Africa and Latin America are also emerging as promising markets, fueled by increasing automation initiatives and supportive government policies. The regional landscape is characterized by intense competition, rapid technological adoption, and a growing focus on developing indigenous robotic capabilities.

Technology Analysis

The Technology segment of the Self-Supervised Learning for Robotic Grasping market is characterized by rapid innovation and diversification, with several distinct approaches driving advancements in robotic manipulation. Convolutional Neural Networks (CNNs) are at the forefront, enabling robots to interpret complex visual data and recognize objects with high accuracy. CNNs

Image segmentation holds broad applications in medical image analysis, providing crucial support to doctors in both automatic diagnosis and computer-assisted interventions. The heterogeneity observed across various medical image datasets necessitates the training of task-specific segmentation models. However, effectively supervising the training of deep learning segmentation models typically demands dense label masks, a requirement that becomes challenging due to the constraints posed by privacy and cost issues in collecting large-scale medical datasets. These challenges collectively give rise to the limited annotations problems in medical image segmentation.

In this dissertation, we address the challenges posed by annotation deficiencies through a comprehensive exploration of various strategies. Firstly, we employ self-supervised learning to extract information from unlabeled data, presenting a tailored self-supervised method designed specifically for convolutional neural networks and 3D Vision Transformers. Secondly, our attention shifts to domain adaptation problems, leveraging images with similar content but in different modalities. We introduce the use of contrastive loss as a shape constraint in our image translation framework, resulting in both improved performance and enhanced training robustness. Thirdly, we incorporate diffusion models for data augmentation, expanding datasets with generated image-label pairs. Lastly, we explore to extract segmentation masks from image-level annotations alone. We propose a multi-task training framework for ECG abnormal beats localization and a conditional diffusion-based algorithm for tumor detection.

To analyse large numbers of texts, social science researchers are increasingly confronting the challenge of text classification. When manual labeling is not possible and researchers have to find automatized ways to classify texts, computer science provides a useful toolbox of machine-learning methods whose performance remains understudied in the social sciences. In this article, we compare the performance of the most widely used text classifiers by applying them to a typical research scenario in social science research: a relatively small labeled dataset with infrequent occurrence of categories of interest, which is a part of a large unlabeled dataset. As an example case, we look at Twitter communication regarding climate change, a topic of increasing scholarly interest in interdisciplinary social science research. Using a novel dataset including 5,750 tweets from various international organizations regarding the highly ambiguous concept of climate change, we evaluate the performance of methods in automatically classifying tweets based on whether they are about climate change or not. In this context, we highlight two main findings. First, supervised machine-learning methods perform better than state-of-the-art lexicons, in particular as class balance increases. Second, traditional machine-learning methods, such as logistic regression and random forest, perform similarly to sophisticated deep-learning methods, whilst requiring much less training time and computational resources. The results have important implications for the analysis of short texts in social science research.