This chapter illustrates how suitable neuro-symbolic models for language understanding can enable domain generalizability and robustness in downstream tasks. Different methods for integrating neural language models and knowledge graphs are discussed. The situations in which this combination is most appropriate are characterized, including quantitative evaluation and qualitative error analysis on a variety of commonsense question answering benchmark datasets.

Training deep neural networks (DNNs) is time-consuming. While most existing solutions try to overlap/schedule computation and communication for efficient training, this paper goes one step further by skipping computing and communication through DNN layer freezing. Our key insight is that the training progress of internal DNN layers differs significantly, and front layers often become well-trained much earlier than deep layers. To explore this, we first introduce the notion of training plasticity to quantify the training progress of internal DNN layers. Then we design KGT, a knowledge-guided DNN training system that employs semantic knowledge from a reference model to accurately evaluate individual layers' training plasticity and safely freeze the converged ones, saving their corresponding backward computation and communication. Our reference model is generated on the fly using quantization techniques and runs forward operations asynchronously on available CPUs to minimize the overhead. In addition, KGT caches the intermediate outputs of the frozen layers with prefetching to further skip the forward computation. Our implementation and testbed experiments with popular vision and language models show that KGT achieves 19%-43% training speedup w.r.t. the state-of-the-art without sacrificing accuracy.

Machine learning algorithms are often referred to as "black boxes." Once the data is put into the algorithm, it is not always possible to know exactly how the algorithm will reach the prediction. This can be especially frustrating when problems occur. MIT's new Mechanical Engineering (MechE) course teaches students how to combine data science and physics-based engineering to tackle the "black box" problem. In Class 2.C161 (Modeling and Designing Physical Systems Using Machine Learning), Professor George Barbastathis teaches how mechanical engineers use their unique knowledge of physical systems to check algorithms and create more accurate predictions.

Machine Learning is the learning in which a machine can learn on its own without being explicitly programmed. It is an application of Al that provide the system with the ability to automatically learn and improve from experience. Here we can generate a program by integrating the input and output of that program. One of the simple definitions of Machine Learning is "Machine Learning is said to learn from experience w.r.t some class of task T and a performance measure P if learners performance at the task in the class as measured by P improves with experiences." Sometimes Artificial Intelligence and Machine Learning are considered as same but they are somehow different from each other.

Celebrated academic and thought leader in machine learning and AI research, UC Berkeley Distinguished Professor Michael I. Jordan, has been named laureate professor at Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI). He was also named honorary director of a new Laureate Faculty Programme, which he will help build with MBZUAI President, Professor Eric Xing. Jordan's appointment brings a wealth of experience to the university, and to the country, in artificial intelligence; in the interface of computer science and statistics; and in computational biology, natural language processing, and signal processing. He is a member of the US National Academy of Sciences, National Academy of Engineering, and Academy of Arts and Sciences, as well as a foreign member of the Royal Society. In 2016, he was cited by Science Magazine as the most influential author in computer science.

By the emergence of deep learning in the large size of data, the true usage of the data has been boosted and deep learning does this by traversing those data on millions of parameters. But this has made an additional requirement of the computation resources such as GPU and these resources are not available at the cutting edge devices such as mobile phones. To counter this issue researchers have introduced many compression techniques such as Knowledge Distillation which is the process of converting complex model behaviour to smaller one in terms of a number of parameters. So in this article, we will take a look at knowledge distillation and will discuss its context briefly. Below are the major points listed that are to be discussed in this article.

This dissertation addresses visual scene understanding and enhances segmentation performance and generalization, training efficiency of networks, and holistic understanding. First, we investigate semantic segmentation in the context of street scenes and train semantic segmentation networks on combinations of various datasets. In Chapter 2 we design a framework of hierarchical classifiers over a single convolutional backbone, and train it end-to-end on a combination of pixel-labeled datasets, improving generalizability and the number of recognizable semantic concepts. Chapter 3 focuses on enriching semantic segmentation with weak supervision and proposes a weakly-supervised algorithm for training with bounding box-level and image-level supervision instead of only with per-pixel supervision. The memory and computational load challenges that arise from simultaneous training on multiple datasets are addressed in Chapter 4. We propose two methodologies for selecting informative and diverse samples from datasets with weak supervision to reduce our networks' ecological footprint without sacrificing performance. Motivated by memory and computation efficiency requirements, in Chapter 5, we rethink simultaneous training on heterogeneous datasets and propose a universal semantic segmentation framework. This framework achieves consistent increases in performance metrics and semantic knowledgeability by exploiting various scene understanding datasets. Chapter 6 introduces the novel task of part-aware panoptic segmentation, which extends our reasoning towards holistic scene understanding. This task combines scene and parts-level semantics with instance-level object detection. In conclusion, our contributions span over convolutional network architectures, weakly-supervised learning, part and panoptic segmentation, paving the way towards a holistic, rich, and sustainable visual scene understanding.

Measuring machine creativity is one of the most fascinating challenges in Artificial Intelligence. This paper explores the possibility of using generative learning techniques for automatic assessment of creativity. The proposed solution does not involve human judgement, it is modular and of general applicability. We introduce a new measure, namely DeepCreativity, based on Margaret Boden's definition of creativity as composed by value, novelty and surprise. We evaluate our methodology (and related measure) considering a case study, i.e., the generation of 19th century American poetry, showing its effectiveness and expressiveness.

An overarching goal in machine learning is to build a generalizable model with few samples. To this end, overparameterization has been the subject of immense interest to explain the generalization ability of deep nets even when the size of the dataset is smaller than that of the model. While the prior literature focuses on the classical supervised setting, this paper aims to demystify overparameterization for meta-learning. Here we have a sequence of linear-regression tasks and we ask: (1) Given earlier tasks, what is the optimal linear representation of features for a new downstream task? and (2) How many samples do we need to build this representation? This work shows that surprisingly, overparameterization arises as a natural answer to these fundamental meta-learning questions. Specifically, for (1), we first show that learning the optimal representation coincides with the problem of designing a task-aware regularization to promote inductive bias. We leverage this inductive bias to explain how the downstream task actually benefits from overparameterization, in contrast to prior works on few-shot learning. For (2), we develop a theory to explain how feature covariance can implicitly help reduce the sample complexity well below the degrees of freedom and lead to small estimation error. We then integrate these findings to obtain an overall performance guarantee for our meta-learning algorithm. Numerical experiments on real and synthetic data verify our insights on overparameterized meta-learning.

Hello guys, first of all, I wish you a very Happy New Year 2022. I have been sharing a lot of roadmaps to become a Web developer, DevOps engineer, and recently React.js One of the requests I received from many of my readers was for creating a Java Developer Roadmap. Since Java is my expertise, It wasn't a problem to create a Java Developer Roadmap, but it took slightly longer for me to create one because of the limited time I get. Anyway, I am finally ready to share my Java developer RoadMap with you. This Roadmap contains my years of experience and the unobstructed path of how to become a Java expert.