A quick glance into any of the top-rated research papers on Machine Learning shows us how Machine Learning and digital technologies are becoming an integral part of every industry.
According to recent research by Gartner, “Smart machines will enter mainstream adoption by 2021.” Adopting Machine Learning help your organization gain a major competitive edge.
Why is Machine Learning so Hot Today?
With over 250 million active customers and tens of millions of products, Amazon’s machine learning makes accurate product recommendations. These recommendations are an outcome of the customer’s browsing and purchasing behavior almost instantly. No humans could do that.
Google is using driverless cars with the help of machine learning to make our roads safer. IBM’s Watson is already a big name in healthcare with its machine learning and cognitive computing power.
If you have an interest in a career in Machine Learning or Deep Learning, you must develop a habit of reading Research Papers on Machine Learning regularly. Reading research papers in Machine Learning keeps you abreast of the latest trends and thoughts.
The course books define the basic premises of your learning Research papers on Machine Learning give you a deeper understanding of the implementation models in every industry.
Being an ML professional your primary task is to think about problems that are difficult to identify. Solve them through innovative means, rather than memorize what has already been found.
Another advantage of browsing through research papers on machine learning is that you can learn Machine Learning algorithms better. Students or ML professionals who read research papers on machine learning algorithms have a better understanding of programming and coding.
Want to Know How Machine Learning Is Impacting our Lives?
The food or grocery segment is one area where Machine Learning has left an indelible mark. Up to 40% of a grocer’s revenue comes from sales of fresh produce. Therefore, maintaining product quality is very important. But that is easier said than done.
Grocers are dependent on their supply chains and consumers. Keeping their shelves stocked and their products fresh is a difficult situation for them.
But with machine learning grocers already know the secret to smarter fresh-food replenishment. They can train ML programs on historical datasets and input data about promotions and store hours as well. Then use the analyses to gauge how much of each product to order and display.
ML systems can also collect information about weather forecasts, public holidays, order quantity parameters, and other contextual information.
Grocers or store-owners can then issue a recommended order every 24 hours so that the grocer always has the appropriate products in the appropriate amounts in stock.
Research Papers on Machine Learning Algorithms
Research Papers on Machine Learning have questioned which machine learning algorithm and what underlying model structure to use has been based on time-consuming investigations and research by human experts.
It has been found out that the right way to select the best algorithms and the most appropriate model architecture, with the correct hyper-parameters, is through trial and error.
Meta-Learning, as it has evolved through the latest research papers on machine learning. It is a concept where exploration of algorithms and model structures take place using machine learning methods.
For us, learning happens at multiple scales. Our brains are born with the ability to learn new concepts and tasks. Similarly, research papers in Machine Learning show that in Meta-Learning or Learning to Learn, there is a hierarchical application of AI algorithms.
This includes first learning which is the best network architecture, and what optimization algorithms and hyper-parameters are most appropriate for the model that has been selected.
The model that has been selected through this process refines the most mundane of tasks. The research has already achieved remarkable results and with the use of different optimization techniques. Evolutionary Strategies is perhaps the best example of this.
However, with a Meta- Reinforcement Learning Algorithm, the objective is to learn the working behind Reinforcement Learning agent that includes both the Reinforcement Learning algorithm and the policy.
Pieter Abbeel gave an explanation for this at the Meta-Learning Symposium held during NIPS 2017. This was also one of the highest rated research papers on Machine Learning.
Research Papers on Machine Learning: One-Shot Learning
In one of the several research papers in Machine Learning, Oriol Vinyals states that humans are capable of learning new concepts with minimal supervision. In a Deep Learning network, there is a requirement of huge amount of labelled training data because neural networks are still not able to recognize a new object that they have only seen once or twice.
However, more recent researches on machine learning have shown that the application of model-based, or metric-based, or optimization-based Meta-Learning approaches to define network architectures that can learn from just a few data examples.
Moreover, the latest research papers on machine learning, i.e., on One-Shot Learning by Vinyals shows significant improvements have taken place over previous baseline one-shot accuracy for video and language tasks.
This approach uses a model that learns a classifier based on an attention kernel to map a small labelled support set and an unlabelled example to its corresponding label
Again, for Reinforcement Learning applications, One-Shot Imitation Learning brings out the possibility of learning from just a few demonstrations of a given task. It is possible to generalize to new instances of the same task by applying a Meta-Learning approach to train robust policies.
Research Papers on Machine Learning: Simulation-Based Learning
Several existing Reinforcement Learning (RL) systems, today rely on simulations to explore the solution space and solve complex problems. These include systems based on Self-Play for gaming applications.
Self-Play is an essential part of the algorithms used by Google\DeepMind in AlphaGo. In the more recent AlphaGo Zero reinforcement learning systems. These are some of the breakthrough approaches that have defeated the world champion at the ancient Chinese game of Go.
Thus, it is interesting to note that the newer AlphaGo Zero system has achieved a significant step forward. The training of AlphaGo Zero system was entirely by Self-Play RL starting from a completely random play. It received no human data or supervision input. The system is effectively self-learning.
Therefore, simulation for Reinforcement Learning training has also been used in Imagination Augmented RL algorithms – the recent Imagination-Augmented Agents (I2A) approach improves on the original model-based RL algorithms by combining both model-free and model-based policy rollouts.
Thus, this approach allows the policy improvement & has resulted in a significant improvement in performance.
Research Papers on Machine Learning: The Wasserstein Auto-Encoder
Wasserstein research paper on Auto-Encoders shows how Autoencoders, which are neural networks, are used for dimensionality reduction. Autoencoders are more popularly used for generative learning models. Variational autoencoder (VAE) is largely used in applications in image and text recognition space.
Moreover, researchers from Max Planck Institute for Intelligent Systems, Germany, in collaboration with scientists from Google Brain have come up with the Wasserstein Auto encoder (WAE). It is capable of utilizing Wasserstein distance in any generative model.
Their aim was to reduce optimal transport cost function in the model distribution.
Thus, after testing, WAE proved to be more functional. It provided a more stable solution than other auto encoders such as VAE with lesser architectural complexity.
Research Papers on Machine Learning: Ultra-strong Machine Learning Comprehensibility of Programs Learned with ILP
Authors of the paper on Ultra-strong machine learning comprehensibility of programs learned with ILP are among the most widely read research papers on machine learning algorithms. They introduced an operational definition for comprehensibility of logic programs. They conducted human trials to determine how properties of a program affect its ease of comprehension.
As a matter of fact, Scholars have used two sets of experiments testing human comprehensibility of logic programs. In the first experiment, they have tested human comprehensibility with and without predicate invention.
Thus, in the second experiment, researchers have directly tested whether any state-of-the-art ILP systems are ultra-strong learners in Michie’s sense, and select the Metagol system for use in human trials.
The results show that participants were not able to learn the relational concept on their own from a set of examples. They were able to apply the relational definition provided by the ILP system correctly.
Moreover, this implies the existence of a class of relational concepts which are hard to acquire for humans, though easy to understand given an abstract explanation. The scholars are of opinion that improved understanding of this class could have potential relevance to contexts involving human learning, teaching, and verbal interaction.
Develop your Own Thoughts
While all of the aforementioned papers present a unique perspective in the advancements in machine learning, you must develop your own thoughts on a hot topic and publish it.
The novel methods mentioned in these research papers in machine learning provide diverse avenues for ML research. As a Machine Learning and artificial intelligence enthusiasts, you can gain a lot when it comes to the latest techniques developed in research.
Thus, as a researcher, Machine Learning looks promising as a career option. You may go for a course in MOOC or take up online courses like the John Hopkins Data Science specialization.
Thus, participating in Kaggle or other online machine learning competitions will also help you gain experience. Attending local meetups or academic conferences is always a fruitful way to learn.
Career in Data Science
You may also enroll in a Data Analytics course for more lucrative career options in Data Science. Moreover, Industry-relevant curriculums, pragmatic market-ready approach, hands-on Capstone Project are some of the best reasons for choosing Digital Vidya. Need experts for creating a killer resume that stands out in the crowd?
Thus, for a rewarding career in Machine Learning, one must stay up to date with any up and coming changes. This also means staying abreast of the latest developments for tools, theory and algorithms.
Furthermore, online communities are great places to know of these changes. Also, read a lot. Read articles on Google Map-Reduce, Google File System, Google Big Table, and The Unreasonable Effectiveness of Data. You will get plenty of free Machine Learning books online. Practice problems, coding competitions, and hackathons are a great way to hone your skills.
Conclusion
Moreover, try finding answers to questions at the end of every research paper on Machine Learning. In addition to research papers in machine learning, subscribe to Machine Learning newsletters or join Machine Learning communities. The latter is better as it helps you gain knowledge through practical implementation of Machine Learning.
Therefore, to build a promising career in Machine Learning, join the Machine Learning Course.
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