J0nasW/paperswithcode huggingface dataset:
Task: Transfer Learning
Area: miscellaneous

**Transfer Learning** is a machine learning technique where a model trained on one task is re-purposed and fine-tuned for a related, but different task. The idea behind transfer learning is to leverage the knowledge learned from a pre-trained model to solve a new, but related problem. This can be useful in situations where there is limited data available to train a new model from scratch, or when the new task is similar enough to the original task that the pre-trained model can be adapted to the new problem with only minor modifications.

<span style="color:grey; opacity: 0.6">( Image credit: [Subodh Malgonde](https://medium.com/@subodh.malgonde/transfer-learning-using-tensorflow-52a4f6bcde3e) )</span>
Task: Intrusion Detection
Area: miscellaneous

**Intrusion Detection** is the process of dynamically monitoring events occurring in a computer system or network, analyzing them for signs of possible incidents and often interdicting the unauthorized access. This is typically accomplished by automatically collecting information from a variety of systems and network sources, and then analyzing the information for possible security problems.


<span class="description-source">Source: [Machine Learning Techniques for Intrusion Detection ](https://arxiv.org/abs/1312.2177)</span>
Task: Classification with Costly Features
Area: miscellaneous

The task is to classify the dataset with costly features with different budget settings. The final metric is the normalized area under the cost-accuracy curve.
Task: Sentence Ambiguity
Area: miscellaneous

Asks models to identify the truth or falsehood of purposely ambiguous sentences.

Source: [BIG-bench](https://github.com/google/BIG-bench/tree/main/bigbench/benchmark_tasks/sentence_ambiguity)
Task: Network Intrusion Detection
Area: miscellaneous

**Network intrusion detection** is the task of monitoring network traffic to and from all devices on a network in order to detect computer attacks.
Task: Vector Quantization (k-means problem)
Area: miscellaneous

Given a data set $X$ of d-dimensional numeric vectors and a number $k$ find a codebook $C$ of $k$ d-dimensional vectors such that the sum of square distances of each $x \in X$ to the respective nearest $c   \in C$ is as small as possible. This is also known as the k-means problem and is known to be NP-hard.
Task: Cross-Modal Retrieval
Area: miscellaneous

**Cross-Modal Retrieval** is used for implementing a retrieval task across different modalities. such as image-text, video-text, and audio-text Cross-Modal Retrieval. The main challenge of Cross-Modal Retrieval is the modality gap and the key solution of Cross-Modal Retrieval is to generate new representations from different modalities in the shared subspace, such that new generated features can be applied in the computation of distance metrics, such as cosine distance and Euclidean distance.

<span class="description-source">Source: [Deep Triplet Neural Networks with Cluster-CCA for Audio-Visual Cross-modal Retrieval ](https://arxiv.org/abs/1908.03737)</span>
Task: Protein Interface Prediction
Area: miscellaneous

How and where proteins interface with one another can ultimately impact the proteins' functions along with a range of other biological processes. As such, precise computational methods for protein interface prediction (PIP) come highly sought after as they could yield significant advances in drug discovery and design as well as protein function analysis.
Task: Table Detection
Area: miscellaneous

Image credit:[Table Detection in the Wild: A Novel Diverse Table Detection Dataset and Method](https://paperswithcode.com/paper/table-detection-in-the-wild-a-novel-diverse)
Task: Malware Classification
Area: miscellaneous

**Malware Classification** is the process of assigning a malware sample to a specific malware family. Malware within a family shares similar properties that can be used to create signatures for detection and classification. Signatures can be categorized as static or dynamic based on how they are extracted. A static signature can be based on a byte-code sequence, binary assembly instruction, or an imported Dynamic Link Library (DLL). Dynamic signatures can be based on file system activities, terminal commands, network communications, or function and system call sequences.


<span class="description-source">Source: [Behavioral Malware Classification using Convolutional Recurrent Neural Networks ](https://arxiv.org/abs/1811.07842)</span>
Task: Cross-Modal Information Retrieval
Area: miscellaneous

**Cross-Modal Information Retrieval** (CMIR) is the task of finding relevant items across different modalities. For example, given an image, find a text or vice versa. The main challenge in CMIR is known as the *heterogeneity gap*: since items from different modalities have different data types, the similarity between them cannot be measured directly. Therefore, the majority of CMIR methods published to date attempt to bridge this gap by learning a latent representation space, where the similarity between items from different modalities can be measured.

<span class="description-source">Source: [Scene-centric vs. Object-centric Image-Text Cross-modal Retrieval: A Reproducibility Study](https://arxiv.org/abs/2301.05174)</span>
Task: X-Ray Diffraction (XRD)
Area: miscellaneous

Diffraction of X-ray patterns and images, with common applications for materials and images.
Task: Grasp Contact Prediction
Area: miscellaneous

Predict contact between object and hand (human or robot).
Task: Pseudo Label
Area: miscellaneous

A lightweight but very power technique for semi supervised learning
Task: Two-sample testing
Area: miscellaneous

In statistical hypothesis testing, a two-sample test is a test performed on the data of two random samples, each independently obtained from a different given population. The purpose of the test is to determine whether the difference between these two populations is statistically significant. The statistics used in two-sample tests can be used to solve many machine learning problems, such as domain adaptation, covariate shift and generative adversarial networks.
Task: Knowledge Tracing
Area: miscellaneous

**Knowledge Tracing** is the task of modelling student knowledge over time so that we can accurately predict how students will perform on future interactions. Improvement on this task means that resources can be suggested to students based on their individual needs, and content which is predicted to be too easy or too hard can be skipped or delayed.


<span class="description-source">Source: [Deep Knowledge Tracing ](https://arxiv.org/abs/1506.05908)</span>
Task: Counterfactual Explanation
Area: miscellaneous

Returns a contrastive argument that permits to achieve the desired class, e.g.,  “to obtain this loan, you need XXX of annual
revenue instead of the current YYY”
Task: Numerical Integration
Area: miscellaneous

Numerical integration is the task to calculate the numerical value of a definite integral or the numerical solution of differential equations.
Task: Stress-Strain Relation
Area: miscellaneous

Data-driven techniques for finding stress-strain relation in non-linearly elastic bodies.

<span style="color:grey; opacity: 0.6">( Image credit: [Data-driven Computing in Elasticity
via Chebyshev Approximation](https://arxiv.org/pdf/1904.10434.pdf) )</span>
Task: Multimodal Intent Recognition
Area: miscellaneous

Intent recognition on multimodal content.

Image source: [MIntRec: A New Dataset for Multimodal Intent Recognition](https://paperswithcode.com/dataset/mintrec)
Task: Penn Machine Learning Benchmark (Real-World)
Area: miscellaneous

Real-World Datasets in Penn Machine Learning Benchmark
Task: Social Media Popularity Prediction
Area: miscellaneous

Social Media Popularity Prediction (SMPP) aims to predict the future popularity (e.g., clicks, views, likes, etc.) of online posts automatically via plenty of social media data from public platforms. It is a crucial problem for social media learning and forecasting and one of the most challenging problems in the field. With the ever-changing user interests and public attention on social media platforms, how to predict popularity accurately becomes more challenging than before. This task is valuable to content providers, marketers, or consumers in a range of real-world applications, including multimedia advertising, recommendation system, or trend analysis.
Task: Community Question Answering
Area: miscellaneous

Community question answering is the task of answering questions on a Q&A forum or board, such as Stack Overflow or Quora.
Task: Click-Through Rate Prediction
Area: miscellaneous

Click-through rate prediction is the task of predicting the likelihood that something on a website (such as an advertisement) will be clicked.

<span style="color:grey; opacity: 0.6">( Image credit: [Deep Spatio-Temporal Neural Networks for Click-Through Rate Prediction](https://arxiv.org/pdf/1906.03776v2.pdf) )</span>
Task: Seismic Detection
Area: miscellaneous

When recording seismic ground motion in multiple sites using independent recording stations one needs to recognize the presence of the same parts of seismic waves arriving at these stations. This problem is known in seismology as seismic phase picking or, more generally, seismic detection.
Task: imbalanced classification
Area: miscellaneous

learning classifier from class-imbalanced data
Task: BIG-bench Machine Learning
Area: miscellaneous

This branch include most common machine learning fundamental algorithms.
Task: Operator learning
Area: miscellaneous

Learn an operator between infinite dimensional Hilbert spaces or Banach spaces
Task: Twitter Bot Detection
Area: miscellaneous

Academic studies estimate that up to 15% of Twitter users are automated bot accounts [1]. The prevalence of Twitter bots coupled with the ability of some bots to give seemingly human responses has enabled these non-human accounts to garner widespread influence. Hence, detecting non-human Twitter users or automated bot accounts using machine learning techniques has become an area of interest to researchers in the last few years.  

[1] https://aaai.org/ocs/index.php/ICWSM/ICWSM17/paper/view/15587
Task: Formation Energy
Area: miscellaneous

On the QM9 dataset the numbers reported in the table are the mean absolute error in eV on the target variable U0 divided by U0's chemical accuracy, which is equal to 0.043.
Task: Variational Monte Carlo
Area: miscellaneous

Variational methods for quantum physics
Task: EEG Emotion Recognition
Area: miscellaneous

Emotion Recognition using EEG signals
Task: Image Retrieval with Multi-Modal Query
Area: miscellaneous

The problem of retrieving images from a database based on a multi-modal (image- text) query. Specifically, the query text prompts some modification in the query image and the task is to retrieve images with the desired modifications.
Task: Brain Decoding
Area: miscellaneous

**Motor Brain Decoding** is fundamental task for building motor brain computer interfaces (BCI).

Progress in predicting finger movements based on brain activity allows us to restore motor functions and improve rehabilitation process of patients.
Task: Misconceptions
Area: miscellaneous

Measures whether a model can discern popular misconceptions from the truth.

Example:

```
        input: The daddy longlegs spider is the most venomous spider in the world.
        choice: T
        choice: F
        answer: F

        input: Karl Benz is correctly credited with the invention of the first modern automobile.
        choice: T
        choice: F
        answer: T
```

Source: [BIG-bench](https://github.com/google/BIG-bench/tree/main/bigbench/benchmark_tasks/misconceptions)
Task: General Knowledge
Area: miscellaneous

This task aims to evaluate the ability of a model to answer general-knowledge questions.

Source: [BIG-bench](https://github.com/google/BIG-bench/tree/main/bigbench/benchmark_tasks/general_knowledge)
Task: Learning-To-Rank
Area: miscellaneous

Learning to rank is the application of machine learning to build ranking models. Some common use cases for ranking models are information retrieval (e.g., web search) and news feeds application (think Twitter, Facebook, Instagram).
Task: Prediction Intervals
Area: miscellaneous

A prediction interval is an estimate of an interval in which a future observation will fall, with a certain probability, given what has already been observed. Prediction intervals are often used in regression analysis.
Task: Synthetic Data Generation
Area: miscellaneous

The generation of tabular data by any means possible.
Task: Recommendation Systems
Area: miscellaneous

The **Recommendation Systems** task is to produce a list of recommendations for a user. The most common methods used in recommender systems are factor models (Koren et al., 2009; Weimer et al., 2007; Hidasi & Tikk, 2012) and neighborhood methods (Sarwar et al., 2001; Koren, 2008). 
Factor models work by decomposing the sparse user-item interactions matrix to a set of d dimensional vectors one for each item and user in the dataset. Factor models are hard to apply in session-based recommendations due to the absence of a user profile. On the other hand, neighborhood methods, which rely on computing similarities between items (or users) are based on co-occurrences of items in sessions (or user profiles). Neighborhood methods have been used extensively in session-based recommendations.

<span style="color:grey; opacity: 0.6">( Image credit: [CuMF_SGD](https://arxiv.org/pdf/1610.05838v3.pdf) )</span>
Task: Session-Based Recommendations
Area: miscellaneous

Recommendation based on a sequence of events. e.g. next item prediction
Task: Unsupervised Contextual Anomaly Detection
Area: miscellaneous

The objective of Unsupervised Anomaly Detection is to detect previously unseen rare objects or events. Unsupervised Contextual Anomaly Detection is formulated such that the data contains two types of attributes, behavioral and contextual attributes with no pre-existing information which observations are anomalous. Behavioral attributes are attributes that relate directly to the process of interest whereas contextual attributes relate to exogenous but highly affecting factors in relation to the process.  Generally the behavioral attributes are conditional on the contextual attributes. 
Source: [Unsupervised Contextual Anomaly Detection using Joint Deep Variational Generative Models](https://arxiv.org/pdf/1904.00548.pdf)
Task: Traffic Classification
Area: miscellaneous

**Traffic Classification** is a task of categorizing traffic flows into application-aware classes such as chats, streaming, VoIP, etc. Classification can be used for several purposes including policy enforcement and control or QoS management.


<span class="description-source">Source: [Classification of Traffic Using Neural Networks by Rejecting: a Novel Approach in Classifying VPN Traffic ](https://arxiv.org/abs/2001.03665)</span>
Task: Service Composition
Area: miscellaneous

Let T be the task that the service composition needs to accomplish. The task T can be granulated to T 1 , T 2 , T 3 , T 4 , … , T n . i.e. T =
{T 1 , T 2 , T 3 , T 4 , … , T n } . For each task T i , a set of service S i = S i 1 , S i 2 , S i 3 , … , S i m is discovered during the service discovery process such that all
services in a set S i perform the same function and have the same input and output parameters (See Figure 2). S 1 = {S 11 , S 12 , S 13 , … , S 1m } , S 2 =
{S 21 , S 22 , S 23 , … , S 2m } , S 3 = {S 31 , S 32 , S 33 , … , S 3m } , … , S n = {S n 1 , S n 2 , S n 3 , … , S n m }
We need to select one service from each set S i in order to compose the big service such that the overall QoS attributes of the big service
are optimal. The total number of the possible distinct service composition is n m . Let k be the the number of QoS attributes. Then the total num-
ber of comparisons required are kn m . We need at least kn m comparisons to find whether the solution is optimal, thus making the problem as
NP-Hard.
Task: Age Estimation
Area: miscellaneous

Age Estimation is the task of estimating the age of a person from an image some other kind of data.

<span style="color:grey; opacity: 0.6">( Image credit: [BridgeNet](https://arxiv.org/pdf/1904.03358v1.pdf) )</span>
Task: Movie Recommendation
Area: miscellaneous

Evaluates the ability of language models to propose relevant movie recommendations with collaborative filtering data.

Source: [BIG-bench](https://github.com/google/BIG-bench/tree/main/bigbench/benchmark_tasks/movie_recommendation)
Task: Human Detection of Deepfakes
Area: miscellaneous

The task of detecting deepfake stimuli, as given to human participants in a statistical study. Methodologies should ideally include a-priori power analysis (e.g. using the GPower software) to calculate the sample size of human participants that would be sufficient to detect the presence of a main effect of a specified effect size.
Task: Protein Structure Prediction
Area: miscellaneous

Image credit: [FastFold: Reducing AlphaFold Training Time from 11 Days to 67 Hours](https://arxiv.org/pdf/2203.00854v1.pdf)
Task: Image-to-Image Translation
Area: miscellaneous

**Image-to-Image Translation** is a task in computer vision and machine learning where the goal is to learn a mapping between an input image and an output image, such that the output image can be used to perform a specific task, such as style transfer, data augmentation, or image restoration.

<span style="color:grey; opacity: 0.6">( Image credit: [Unpaired Image-to-Image Translation
using Cycle-Consistent Adversarial Networks](https://arxiv.org/pdf/1703.10593v6.pdf) )</span>
Task: Imputation
Area: miscellaneous

Substituting missing data with values according to some criteria.
Task: Context Aware Product Recommendation
Area: miscellaneous

Context-aware recommender systems (CARS) generate more relevant recommendations by adapting them to the specific contextual situation of the user. This article explores how contextual information can be used to create more intelligent and useful recommender systems.
Task: Malware Detection
Area: miscellaneous

**Malware Detection** is a significant part of endpoint security including workstations, servers, cloud instances, and mobile devices. Malware Detection is used to detect and identify malicious activities caused by malware. With the increase in the variety of malware activities on CMS based websites such as [malicious malware redirects on WordPress site](https://secure.wphackedhelp.com/blog/wordpress-malware-redirect-hack-cleanup/) (Aka, WordPress Malware Redirect Hack) where the site redirects to spam, being the most widespread, the need for automatic detection and classifier amplifies as well. The signature-based Malware Detection system is commonly used for existing malware that has a signature but it is not suitable for unknown malware or zero-day malware


<span class="description-source">Source: [The Threat of Adversarial Attacks on Machine Learning in Network Security - A Survey ](https://arxiv.org/abs/1911.02621)</span>
Task: Fine-Grained Urban Flow Inference
Area: miscellaneous

Fine-grained urban flow inference (FUFI) aims to infer the fine-grained urban flow map from the coarse-grained one.
Task: Survival Analysis
Area: miscellaneous

**Survival Analysis** is a branch of statistics focused on the study of time-to-event data, usually called survival times. This type of data appears in a wide range of applications such as failure times in mechanical systems, death times of patients in a clinical trial or duration of unemployment in a population. One of the main objectives of Survival Analysis is the estimation of the so-called survival function and the hazard function. If a random variable has density function $f$ and cumulative distribution function $F$, then its survival function $S$ is $1-F$, and its hazard $λ$ is $f/S$.


<span class="description-source">Source: [Gaussian Processes for Survival Analysis ](https://arxiv.org/abs/1611.00817)</span>

Image: [Kvamme et al.](https://arxiv.org/pdf/1910.06724v1.pdf)
Task: Fraud Detection
Area: miscellaneous

**Fraud Detection** is a vital topic that applies to many industries including the financial sectors, banking, government agencies, insurance, and law enforcement, and more. Fraud endeavors have detected a radical rise in current years, creating this topic more critical than ever. Despite struggles on the part of the troubled organizations, hundreds of millions of dollars are wasted to fraud each year. Because nearly a few samples confirm fraud in a vast community, locating these can be complex. Data mining and statistics help to predict and immediately distinguish fraud and take immediate action to minimize costs.


<span class="description-source">Source: [Applying support vector data description for fraud detection ](https://arxiv.org/abs/2006.00618)</span>
Task: Multi-Armed Bandits
Area: miscellaneous

Multi-armed bandits refer to a task where a fixed amount of resources must be allocated between competing resources that maximizes expected gain. Typically these problems involve an exploration/exploitation trade-off.

<span style="color:grey; opacity: 0.6">( Image credit: [Microsoft Research](http://research.microsoft.com/en-us/projects/bandits/) )</span>
Task: Molecular Property Prediction
Area: miscellaneous

Molecular property prediction is the task of predicting the properties of a molecule from its structure.
Task: Zero-Shot Cross-Modal Retrieval
Area: miscellaneous

**Zero-Shot Cross-Modal Retrieval** is the task of finding relevant items across different modalities without having received any training examples. For example, given an image, find a text or vice versa. The main challenge in the task is known as the *heterogeneity gap*: since items from different modalities have different data types, the similarity between them cannot be measured directly. Therefore, the majority of methods published to date attempt to bridge this gap by learning a latent representation space, where the similarity between items from different modalities can be measured.

<span class="description-source">Source: [Scene-centric vs. Object-centric Image-Text Cross-modal Retrieval: A Reproducibility Study](https://arxiv.org/abs/2301.05174)</span>
Task: Contextual Anomaly Detection
Area: miscellaneous

The objective of Unsupervised Anomaly Detection is to detect previously unseen rare objects or events. Contextual Anomaly Detection is formulated such that the data contains two types of attributes, behavioral and contextual attributes. Behavioral attributes are attributes that relate directly to the process of interest whereas contextual attributes relate to exogenous but highly affecting factors in relation to the process.  Generally the behavioral attributes are conditional on the contextual attributes. 
Source: [Unsupervised Contextual Anomaly Detection using Joint Deep Variational Generative Models](https://arxiv.org/pdf/1904.00548.pdf)
Task: Known Unknowns
Area: miscellaneous

Language models have a tendency to generate text containing false statements that are often referred to as "Hallucinations." The primary purpose of this task is to test for this failure case by probing whether a model can correctly identify that the answer to a question is unknown. A common failure mode would be to prefer a prediction of false on unknown truth over a prediction that the answer is unknown.

Source: [BIG-bench](https://github.com/google/BIG-bench/tree/main/bigbench/benchmark_tasks/known_unknowns)
Task: Table Extraction
Area: miscellaneous

Table extraction involves detecting and recognizing a table's logical structure and content from its unstructured presentation within a document