Machine learning: Trends, perspectives, and prospects

By Michael I. Jordan, Tom Michael Mitchell. 2015

8,448 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Large Language Models, Scaling & Learning Theory

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Tie-Yan Liu
2. Tao Qin
3. Yoshua Bengio
4. Xu Tan
5. Sergey Levine
6. Quoc V. Le
7. Mohit Bansal
8. Yang Liu

In Large Language Models

1. Tao Qin
2. Tie-Yan Liu
3. Andreas Holzinger
4. Xu Tan
5. Yang Liu
6. Li Yang
7. Abdallah Shami
8. H. Hodson

In Scaling & Learning Theory

1. Andreas Holzinger
2. Li Yang
3. Abdallah Shami
4. H. Hodson
5. Julia E. Powles
6. Alexey Ignatiev
7. Yuxi Li
8. Jun Shu

Citations in the corpus

• Theory-Guided Data Science: A New Paradigm for Scientific Discovery from Data (2016, background): “The problem of parameter selection has recently received considerable attention in the machine learning community in the context of multi-armed bandit problems [76], [77], [78].”
• Computing Optimal Decision Sets with SAT (2020, background): “Decision tasks that seemed well beyond the capabilities of artificial intelligence have now become commonly solved using machine learning [32, 35, 41].”
• Discovering governing equations from data by sparse identification of nonlinear dynamical systems (2015, background): “Advances in machine learning [19] and data science [29, 21] have promised a renaissance in the analysis and understanding of complex data, extracting patterns in vast multimodal data that is beyond the ability of humans to grasp.”
• A Survey on Neural Speech Synthesis (2021, background): “Developing a TTS system requires knowledge about languages and human speech production, and involves multiple disciplines including linguistics [61], acoustics [166], digital signal processing [314], and machine learning [25, 143].”
• Ray: A Distributed Framework for Emerging AI Applications (2017, background): “an emerging class of reinforcement learning applications and algorithms. 1 Introduction Artificial intelligence is currently emerging as the workhorse technology for a range of real-world applications [27]. To date, however, these applications have largely been based on a fairly restricted supervised”
• Federated Collaborative Filtering for Privacy-Preserving Personalized Recommendation System (2019, background): “Personalized recommendation, a classical application of machine learning models [1] suffers badly from unreliable predictions with diverse consequences in many different domains.”
• Deep Patient: An Unsupervised Representation to Predict the Future of Patients from the Electronic Health Records (2016, neutral): “The success of predictive algorithms largely depends on feature selection and data representation 12,13 .”
• Byzantine-Tolerant Machine Learning (2017, background): “Its applications range all the way from images classification, financial trend prediction, disease diagnosis, to gaming and driving [8].”
• Interactive machine learning for health informatics: when do we need the human-in-the-loop? (2016, background): “Today, machine learning (ML) is the fastest growing technical field, at the intersection of informatics and statistics, tightly connected with data science and knowledge discovery, and health is among the greatest challenges [2, 3].”
• Machine Learning Testing: Survey, Landscapes and Horizons (2019, methodology): “It is the most widely used type of machine learning [34].”
• All-optical neural network with nonlinear activation functions (2019, background): “ANNs also show great potential in scientific researches [3–6], especially in discovering new materials [7], classifying phases of matter [8, 9], representing variational wave functions [10], accelerating Monte Carlo simulations [11, 12], etc.”
• Small Sample Learning in Big Data Era (2018, methodology): “Firstly, a performance measure is generally required to be defined, and then useful information are exploited from the pre-collected historical experience (training data) and pre-known prior knowledge under the criterion of maximizing the performance measure to train a good learner to help analyze f”
• A Review of Convolutional Neural Network Applied to Fruit Image Processing (2020, background): “Additionally, pooling operations contribute to obtaining an invariant representation to small translations of the input [9,11,47].”
• Shredder: Learning Noise to Protect Privacy with Partial DNN Inference on the Edge (2019, background): “The limited computation capability of edge devices [6] and the increasing processing demand of learning models [7, 8] has naturally pushed most of the computation to the cloud [9].”
• Revolutionizing healthcare: the role of artificial intelligence in clinical practice (2023, background): “As AI continues to evolve, it is crucial to ensure that it is developed responsibly and for the benefit of all [5–8].”