J0nasW/paperswithcode huggingface dataset:
Task: Dictionary Learning
Area: methodology

**Dictionary Learning** is an important problem in multiple areas, ranging from computational neuroscience, machine learning, to computer vision and image processing. The general goal is to find a good basis for given data. More formally, in the Dictionary Learning problem, also known as sparse coding, we are given samples of a random vector $y\in\mathbb{R}^n$, of the form $y=Ax$ where $A$ is some unknown matrix in $\mathbb{R}^{n×m}$, called dictionary, and $x$ is sampled from an unknown distribution over sparse vectors. The goal is to approximately recover the dictionary $A$.


<span class="description-source">Source: [Polynomial-time tensor decompositions with sum-of-squares ](https://arxiv.org/abs/1610.01980)</span>
Task: Novel Class Discovery
Area: methodology

The goal of Novel Class Discovery (NCD) is to identify new classes in unlabeled data, by exploiting prior knowledge from known classes. In this specific setup, the data is split in two sets. The first is a labeled set containing known classes and the second is an unlabeled set containing unknown classes that must be discovered.
Task: Imitation Learning
Area: methodology

**Imitation Learning** is a framework for learning a behavior policy from demonstrations. Usually, demonstrations are presented in the form of state-action trajectories, with each pair indicating the action to take at the state being visited. In order to learn the behavior policy, the demonstrated actions are usually utilized in two ways. The first, known as Behavior Cloning (BC), treats the action as the target label for each state, and then learns a generalized mapping from states to actions in a supervised manner. Another way, known as Inverse Reinforcement Learning (IRL), views the demonstrated actions as a sequence of decisions, and aims at finding a reward/cost function under which the demonstrated decisions are optimal. 

Finally, a newer methodology, Inverse Q-Learning aims at directly learning Q-functions from expert data, implicitly representing rewards, under which the optimal policy can be given as a Boltzmann distribution similar to soft Q-learning

<span class="description-source">Source: [Learning to Imitate ](https://ai.stanford.edu/blog/learning-to-imitate)</span>
Task: Multimodal Text and Image Classification
Area: methodology

Classification with both source Image and Text
Task: One-Shot Learning
Area: methodology

One-shot learning is the task of learning information about object categories from a single training example.

<span style="color:grey; opacity: 0.6">( Image credit: [Siamese Neural Networks for One-shot Image Recognition](https://www.cs.cmu.edu/~rsalakhu/papers/oneshot1.pdf) )</span>
Task: Unsupervised Pre-training
Area: methodology

Pre-training a neural network using unsupervised (self-supervised) auxiliary tasks on unlabeled data.
Task: Gaussian Processes
Area: methodology

**Gaussian Processes** is a powerful framework for several machine learning tasks such as regression, classification and inference. Given a finite set of input output training data that is generated out of a fixed (but possibly unknown) function, the framework models the unknown function as a stochastic process such that the training outputs are a finite number of jointly Gaussian random variables, whose properties can then be used to infer the statistics (the mean and variance) of the function at test values of input.


<span class="description-source">Source: [Sequential Randomized Matrix Factorization for Gaussian Processes: Efficient Predictions and Hyper-parameter Optimization ](https://arxiv.org/abs/1711.06989)</span>
Task: Long-tail learning with class descriptors
Area: methodology

Long-tail learning by using class descriptors (like attributes, class embedding, etc) to learn tail classes as well as head classes.
Task: Personalized Federated Learning
Area: methodology

The federated learning setup presents numerous challenges including data heterogeneity (differences in data distribution), device heterogeneity (in terms of computation capabilities, network connection, etc.), and communication efficiency.
Especially data heterogeneity makes it hard to learn a single shared global model that applies to all clients. To overcome these issues, Personalized Federated Learning (PFL) aims to personalize the global model for each client in the federation.
Task: Few-Shot Learning
Area: methodology

**Few-Shot Learning** is an example of meta-learning, where a learner is trained on several related tasks, during the meta-training phase, so that it can generalize well to unseen (but related) tasks with just few examples, during the meta-testing phase. An effective approach to the Few-Shot Learning problem is to learn a common representation for various tasks and train task specific classifiers on top of this representation.


<span class="description-source">Source: [Penalty Method for Inversion-Free Deep Bilevel Optimization ](https://arxiv.org/abs/1911.03432)</span>
Task: Classification
Area: methodology

**Classification** is the task of categorizing a set of data into predefined classes or groups. The aim of classification is to train a model to correctly predict the class or group of new, unseen data. The model is trained on a labeled dataset where each instance is assigned a class label. The learning algorithm then builds a mapping between the features of the data and the class labels. This mapping is then used to predict the class label of new, unseen data points. The quality of the prediction is usually evaluated using metrics such as accuracy, precision, and recall.
Task: Entity Embeddings
Area: methodology

Entity Embeddings is a technique for applying deep learning to tabular data. It involves representing the categorical data of an information systems entity with multiple dimensions.
Task: Multiobjective Optimization
Area: methodology

Multi-objective optimization (also known as multi-objective programming, vector optimization, multicriteria optimization, multiattribute optimization or Pareto optimization) is an area of multiple criteria decision making that is concerned with mathematical optimization problems involving more than one objective function to be optimized simultaneously. Multi-objective optimization has been applied in many fields of science, including engineering, economics and logistics where optimal decisions need to be taken in the presence of trade-offs between two or more conflicting objectives. Minimizing cost while maximizing comfort while buying a car, and maximizing performance whilst minimizing fuel consumption and emission of pollutants of a vehicle are examples of multi-objective optimization problems involving two and three objectives, respectively. In practical problems, there can be more than three objectives.
Task: Word Embeddings
Area: methodology

Word embedding is the collective name for a set of language modeling and feature learning techniques in natural language processing (NLP) where words or phrases from the vocabulary are mapped to vectors of real numbers.

Techniques for learning word embeddings can include  Word2Vec, GloVe, and other neural network-based approaches that train on an NLP task such as language modeling or document classification.

<span style="color:grey; opacity: 0.6">( Image credit: [Dynamic Word Embedding for Evolving Semantic Discovery](https://arxiv.org/pdf/1703.00607v2.pdf) )</span>
Task: Unsupervised MNIST
Area: methodology

Accuracy on MNIST when training without any labels
Task: Unsupervised Domain Adaptation
Area: methodology

**Unsupervised Domain Adaptation** is a learning framework to transfer knowledge learned from source domains with a large number of annotated training examples to target domains with unlabeled data only.


<span class="description-source">Source: [Domain-Specific Batch Normalization for Unsupervised Domain Adaptation ](https://arxiv.org/abs/1906.03950)</span>
Task: Depth Anomaly Segmentation
Area: methodology

Anomaly Segmentation using depth information only
Task: NONPARAMETRIC DEEP CLUSTERING
Area: methodology

Deep Nonparametric clustering are methods which utilize deep clustering when the number of clusters is not known apriorly and needs to be inferred.
Task: Interpretable Machine Learning
Area: methodology

The goal of **Interpretable Machine Learning** is to allow oversight and understanding of machine-learned decisions. Much of the work in Interpretable Machine Learning has come in the form of devising methods to better explain the predictions of machine learning models.


<span class="description-source">Source: [Assessing the Local Interpretability of Machine Learning Models ](https://arxiv.org/abs/1902.03501)</span>
Task: Partial Domain Adaptation
Area: methodology

**Partial Domain Adaptation** is a transfer learning paradigm, which manages to transfer relevant knowledge from a large-scale source domain to a small-scale target domain.


<span class="description-source">Source: [Deep Residual Correction Network for Partial Domain Adaptation ](https://arxiv.org/abs/2004.04914)</span>
Task: Privacy Preserving Deep Learning
Area: methodology

The goal of privacy-preserving (deep) learning is to train a model while preserving privacy of the training dataset. Typically, it is understood that the trained model should be privacy-preserving (e.g., due to the training algorithm being differentially private).
Task: 3D Anomaly Segmentation
Area: methodology

Anomaly Segmentation using 3D information only
Task: Multi-Label Classification
Area: methodology

**Multi-Label Classification** is the supervised learning problem where an instance may be associated with multiple labels. This is an extension of single-label classification (i.e., multi-class, or binary) where each instance is only associated with a single class label.


<span class="description-source">Source: [Deep Learning for Multi-label Classification ](https://arxiv.org/abs/1502.05988)</span>
Task: Feature Engineering
Area: methodology

Feature engineering is the process of taking a dataset and constructing explanatory variables — features — that can be used to train a machine learning model for a prediction problem. Often, data is spread across multiple tables and must be gathered into a single table with rows containing the observations and features in the columns.

The traditional approach to feature engineering is to build features one at a time using domain knowledge, a tedious, time-consuming, and error-prone process known as manual feature engineering. The code for manual feature engineering is problem-dependent and must be re-written for each new dataset.
Task: Few-Shot Relation Classification
Area: methodology

**Few-Shot Relation Classification** is a particular relation classification task under minimum annotated data, where a model is required to classify a new incoming query instance given only few support instances (e.g., 1 or 5) during testing.


<span class="description-source">Source: [MICK: A Meta-Learning Framework for Few-shot Relation Classification with Little Training Data ](https://arxiv.org/abs/2004.14164)</span>
Task: Bayesian Optimisation
Area: methodology

Expensive black-box functions are a common problem in many disciplines, including tuning the parameters of machine learning algorithms, robotics, and other engineering design problems. **Bayesian Optimisation** is a principled and efficient technique for the global optimisation of these functions. The idea behind Bayesian Optimisation is to place a prior distribution over the target function and then update that prior with a set of “true” observations of the target function by expensively evaluating it in order to produce a posterior predictive distribution. The posterior then informs where to make the next observation of the target function through the use of an acquisition function, which balances the exploitation of regions known to have good performance with the exploration of regions where there is little information about the function’s response.


<span class="description-source">Source: [A Bayesian Approach for the Robust Optimisation of Expensive-to-Evaluate Functions ](https://arxiv.org/abs/1904.11416)</span>
Task: Graph Representation Learning
Area: methodology

The goal of **Graph Representation Learning** is to construct a set of features (‘embeddings’) representing the structure of the graph and the data thereon. We can distinguish among Node-wise embeddings, representing each node of the graph, Edge-wise embeddings, representing each edge in the graph, and Graph-wise embeddings representing the graph as a whole.


<span class="description-source">Source: [SIGN: Scalable Inception Graph Neural Networks ](https://arxiv.org/abs/2004.11198)</span>
Task: Abnormal Event Detection In Video
Area: methodology

**Abnormal Event Detection In Video** is a challenging task in computer vision, as the definition of what an abnormal event looks like depends very much on the context. For instance, a car driving by on the street is regarded as a normal event, but if the car enters a pedestrian area, this is regarded as an abnormal event. A person running on a sports court (normal event) versus running outside from a bank (abnormal event) is another example. Although what is considered abnormal depends on the context, we can generally agree that abnormal events should be unexpected events that occur less often than familiar (normal) events


<span class="description-source">Source: [Unmasking the abnormal events in video ](https://arxiv.org/abs/1705.08182)</span>

Image: [Ravanbakhsh et al](https://arxiv.org/pdf/1708.09644v1.pdf)
Task: Bilevel Optimization
Area: methodology

**Bilevel Optimization** is a branch of optimization, which contains a nested optimization problem within the constraints of the outer optimization problem. The outer optimization task is usually referred as the upper level task, and the nested inner optimization task is referred as the lower level task. The lower level problem appears as a constraint, such that only an optimal solution to the lower level optimization problem is a possible feasible candidate to the upper level optimization problem.


<span class="description-source">Source: [Efficient Evolutionary Algorithm for Single-Objective Bilevel Optimization ](https://arxiv.org/abs/1303.3901)</span>
Task: Data Free Quantization
Area: methodology

**Data Free Quantization** is a technique to achieve a highly accurate quantized model without accessing any training data.

<span class="description-source">Source: [Qimera: Data-free Quantization with Synthetic Boundary Supporting Samples](https://arxiv.org/abs/2111.02625)</span>
Task: AutoML
Area: methodology

Automated Machine Learning (**AutoML**) is a general concept which covers diverse techniques for automated model learning including automatic data preprocessing, architecture search, and model selection.
Source: Evaluating recommender systems for AI-driven data science (1905.09205)


<span class="description-source">Source: [CHOPT : Automated Hyperparameter Optimization Framework for Cloud-Based Machine Learning Platforms ](https://arxiv.org/abs/1810.03527)</span>
Task: Metaheuristic Optimization
Area: methodology

In computer science and mathematical optimization, a metaheuristic is a higher-level procedure or heuristic designed to find, generate, or select a heuristic (partial search algorithm) that may provide a sufficiently good solution to an optimization problem. For some examples, you can visit https://aliasgharheidari.com/Publications.html
Task: Distributed Optimization
Area: methodology

The goal of **Distributed Optimization** is to optimize a certain objective defined over millions of billions of data that is distributed over many machines by utilizing the computational power of these machines.


<span class="description-source">Source: [Analysis of Distributed StochasticDual Coordinate Ascent ](https://arxiv.org/abs/1312.1031)</span>
Task: Federated Unsupervised Learning
Area: methodology

Federated unsupervised learning trains models from decentralized data that have no labels.
Task: Outlier Detection
Area: methodology

**Outlier Detection** is a task of identifying a subset of a given data set which are considered anomalous in that they are unusual from other instances. It is one of the core data mining tasks and is central to many applications. In the security field, it can be used to identify potentially threatening users, in the manufacturing field it can be used to identify parts that are likely to fail.


<span class="description-source">Source: [Coverage-based Outlier Explanation ](https://arxiv.org/abs/1911.02617)</span>
Task: Information Plane
Area: methodology

To obtain the Information Plane (IP) of deep neural networks, which shows the trajectories of the hidden layers during training in a 2D plane using as coordinate axes the mutual information between the input and the hidden layer, and the mutual information between the output and the hidden layer.
Task: Disentanglement
Area: methodology

This is an approach to solve a diverse set of tasks in a data efficient manner  by disentangling (or isolating ) the underlying structure of the main problem into disjoint parts of its representations.  This disentanglement can be done by focussing on the "transformation" properties of the world(main problem)
Task: Chatbot
Area: methodology

**Chatbot** or conversational AI is a language model designed and implemented to have conversations with humans. 


<span class="description-source">Source: [Open Data Chatbot ](https://arxiv.org/abs/1909.03653)</span>

[Image source](https://arxiv.org/pdf/2006.16779v3.pdf)
Task: Hyperparameter Optimization
Area: methodology

**Hyperparameter Optimization** is the problem of choosing a set of optimal hyperparameters for a learning algorithm. Whether the algorithm is suitable for the data directly depends on hyperparameters, which directly influence overfitting or underfitting. Each model requires different assumptions, weights or training speeds for different types of data under the conditions of a given loss function.


<span class="description-source">Source: [Data-driven model for fracturing design optimization: focus on building digital database and production forecast ](https://arxiv.org/abs/1910.14499)</span>
Task: Model Compression
Area: methodology

**Model Compression** is an actively pursued area of research over the last few years with the goal of deploying state-of-the-art deep networks in low-power and resource limited devices without significant drop in accuracy. Parameter pruning, low-rank factorization and weight quantization are some of the proposed methods to compress the size of deep networks.


<span class="description-source">Source: [KD-MRI: A knowledge distillation framework for image reconstruction and image restoration in MRI workflow ](https://arxiv.org/abs/2004.05319)</span>
Task: Density Estimation
Area: methodology

The goal of **Density Estimation** is to give an accurate description of the underlying probabilistic density distribution of an observable data set with unknown density.


<span class="description-source">Source: [Contrastive Predictive Coding Based Feature for Automatic Speaker Verification ](https://arxiv.org/abs/1904.01575)</span>
Task: Multi-Task Learning
Area: methodology

Multi-task learning aims to learn multiple different tasks simultaneously while maximizing
performance on one or all of the tasks.

<span style="color:grey; opacity: 0.6">( Image credit: [Cross-stitch Networks for Multi-task Learning](https://arxiv.org/pdf/1604.03539v1.pdf) )</span>
Task: Neural Architecture Search
Area: methodology

**Neural architecture search (NAS)** is a technique for automating the design of artificial neural networks (ANN), a widely used model in the field of machine learning. NAS essentially takes the process of a human manually tweaking a neural network and learning what works well, and automates this task to discover more complex architectures.

Image Credit : [NAS with Reinforcement Learning](https://arxiv.org/abs/1611.01578)
Task: Long-tail Learning
Area: methodology

Long-tailed learning, one of the most challenging problems in visual recognition, aims to train well-performing models from a large number of images that follow a long-tailed class distribution.
Task: RGB+Depth Anomaly Detection and Segmentation
Area: methodology

RGB+Depth Anomaly Detection and Segmentation
Task: Model extraction
Area: methodology

Model extraction attacks, aka model stealing attacks, are used to extract the parameters from the target model. Ideally, the adversary will be able to steal and replicate a model that will have a very similar performance to the target model.
Task: Depth + RGB Anomaly Segmentation
Area: methodology

Depth + RGB Anomaly Segmentation
Task: Multi-task Language Understanding
Area: methodology

The test covers 57 tasks including elementary mathematics, US history, computer science, law, and more. https://arxiv.org/pdf/2009.03300.pdf
Task: Multi-agent Reinforcement Learning
Area: methodology

The target of **Multi-agent Reinforcement Learning** is to solve complex problems by integrating multiple agents that focus on different sub-tasks. In general, there are two types of multi-agent systems: independent and cooperative systems.


<span class="description-source">Source: [Show, Describe and Conclude: On Exploiting the Structure Information of Chest X-Ray Reports ](https://arxiv.org/abs/2004.12274)</span>
Task: Reinforcement Learning (RL)
Area: methodology

**Reinforcement Learning (RL)** involves training an agent to take actions in an environment to maximize a cumulative reward signal. The agent interacts with the environment and learns by receiving feedback in the form of rewards or punishments for its actions. The goal of reinforcement learning is to find the optimal policy or decision-making strategy that maximizes the long-term reward.
Task: Multiple Instance Learning
Area: methodology

**Multiple Instance Learning** is a type of weakly supervised learning algorithm where training data is arranged in bags, where each bag contains a set of instances $X=\\{x_1,x_2, \ldots,x_M\\}$, and there is one single label $Y$ per bag, $Y\in\\{0, 1\\}$ in the case of a binary classification problem. It is assumed that individual labels $y_1, y_2,\ldots, y_M$ exist for the instances within a bag, but they are unknown during training. In the standard Multiple Instance assumption, a bag is considered negative if all its instances are negative. On the other hand, a bag is positive, if at least one instance in the bag is positive.


<span class="description-source">Source: [Monte-Carlo Sampling applied to Multiple Instance Learning for Histological Image Classification ](https://arxiv.org/abs/1812.11560)</span>
Task: Science Question Answering
Area: miscellaneous

Image credit: [Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering](https://paperswithcode.com/paper/learn-to-explain-multimodal-reasoning-via)
Task: Penn Machine Learning Benchmark
Area: miscellaneous

Penn Machine Learning Benchmarks (PMLB) is a large collection of curated benchmark datasets for evaluating and comparing supervised machine learning algorithms.
Task: Weather Forecasting
Area: miscellaneous

**Weather Forecasting** is the prediction of future weather conditions such as precipitation, temperature, pressure and wind.


<span class="description-source">Source: [MetNet: A Neural Weather Model for Precipitation Forecasting ](https://arxiv.org/abs/2003.12140)</span>
Task: Classifier calibration
Area: miscellaneous

Confidence calibration – the problem of predicting probability estimates representative of the true correctness likelihood – is important for classification models in many applications. The two common calibration metrics are Expected Calibration Error (ECE) and Maximum Calibration Error (MCE).