What is learning? 20$^{st}$ century formalizations of learning theory -- which precipitated revolutions in artificial intelligence -- focus primarily on $\mathit{in-distribution}$ learning, that is, learning under the assumption that the training data are sampled from the same distribution as the evaluation distribution. This assumption renders these theories inadequate for characterizing 21$^{st}$ century real world data problems, which are typically characterized by evaluation distributions that differ from the training data distributions (referred to as out-of-distribution learning). We therefore make a small change to existing formal definitions of learnability by relaxing that assumption. We then introduce $\mathbf{learning\ efficiency}$ (LE) to quantify the amount a learner is able to leverage data for a given problem, regardless of whether it is an in- or out-of-distribution problem. We then define and prove the relationship between generalized notions of learnability, and show how this framework is sufficiently general to characterize transfer, multitask, meta, continual, and lifelong learning. We hope this unification helps bridge the gap between empirical practice and theoretical guidance in real world problems. Finally, because biological learning continues to outperform machine learning algorithms on certain OOD challenges, we discuss the limitations of this framework vis-\'a-vis its ability to formalize biological learning, suggesting multiple avenues for future research.

HRNet is a state-of-the-art algorithm in the field of semantic segmentation, facial landmark detection, and human pose estimation. It has shown superior results in semantic segmentation on datasets like PASCAL Context, LIP, Cityscapes, AFLW, COFW, and 300W. But first, let's understand what the fields mean and what kind of algorithm hides behind HRNet. Semantic Segmentation is used to categorize structures of an image into certain classes. This is done by labeling each pixel with a certain class [3].

Gradsflow can automatically train Deep Learning Models for different tasks on your laptop or to a remote cluster directly from your laptop. It provides a powerful and easy-to-extend Model Training API that can be used to train almost any PyTorch model. You can use the AutoTask feature to automatically build and train models without writing any Machine Learning code across various areas including Vision and Text. Currently, it supports Image Classification, Text Classification, Sentiment Analysis and Text Summarization. Just dump the data and you are ready to train the model.

The neural network is the intuitive and positional side of the hybrid algorithm. It is trained on thousands of master chess games. This game on the left is a game that is played between two neural networks. When looking at the moves that the engine play, it is very clear that the network has learnt some basic positional concepts. For example, you can see that the engines push knights to the center, fianchetto bishops,and push pawns to gain space.

Now, imagine the same cat, but with coal-black fur. Now, imagine the cat strutting along the Great Wall of China. Doing this, a quick series of neuron activations in your brain will come up with variations of the picture presented, based on your previous knowledge of the world. As humans, it's easy to envision an object with different attributes. But, despite advances in deep neural networks that match or surpass human performance in certain tasks, computers still struggle with the very human skill of "imagination."

Natural language processing also known as NLP is a deep learning technique that helps computers extract meaning from unstructured texts. NLP is widely used in data-intensive tasks such as automatic translation and text summarization. There is also a field called NLP-art that uses NLP to create art such as text paintings, poetry, fiction, music and other forms of visual arts. The idea of NLP is that a computer can understand natural language the same way a human brain interprets it. The only difference is that the computer uses math rather than biology to understand.

Convolutional Neural Network (CNN) has been most widely used in the field of computer vision and visual perception to solve multiple tasks such as image classification, semantic segmentation and many more. However, there is a need for approaches that can further improve its performance. One such approach is to add some attention mechanism to an already existing CNN architecture for further improvements. Squeeze and Excitation Network (SENet) is one such attention mechanism that is most widely used for performance improvements. In the article, we are going to learn more about the Squeeze and Excitation Networks, how they work and how they help to improve performance. The squeeze and excitation attention mechanism was introduced in the year 2018 by Hu et al. in their paper " Squeeze-and-Excitation Networks " at CVPR 2018 with a journal version in TPAMI.

Iron ore feed load control is one of the most critical settings in a mineral grinding process, directly impacting the quality of final products. The setting of the feed load is mainly determined by the characteristics of the ore pellets. However, the characterisation of ore is challenging to acquire in many production environments, leading to poor feed load settings and inefficient production processes. This paper presents our work using deep learning models for direct ore feed load estimation from ore pellet images. To address the challenges caused by the large size of a full ore pellets image and the shortage of accurately annotated data, we treat the whole modelling process as a weakly supervised learning problem. A two-stage model training algorithm and two neural network architectures are proposed. The experiment results show competitive model performance, and the trained models can be used for real-time feed load estimation for grind process optimisation.

This paper introduces, for the first time to our knowledge, physics-informed neural networks to accurately estimate the AC-OPF result and delivers rigorous guarantees about their performance. Power system operators, along with several other actors, are increasingly using Optimal Power Flow (OPF) algorithms for a wide number of applications, including planning and real-time operations. However, in its original form, the AC Optimal Power Flow problem is often challenging to solve as it is non-linear and non-convex. Besides the large number of approximations and relaxations, recent efforts have also been focusing on Machine Learning approaches, especially neural networks. So far, however, these approaches have only partially considered the wide number of physical models available during training. And, more importantly, they have offered no guarantees about potential constraint violations of their output. Our approach (i) introduces the AC power flow equations inside neural network training and (ii) integrates methods that rigorously determine and reduce the worst-case constraint violations across the entire input domain, while maintaining the optimality of the prediction. We demonstrate how physics-informed neural networks achieve higher accuracy and lower constraint violations than standard neural networks, and show how we can further reduce the worst-case violations for all neural networks.

Deep neural networks have a clear degradation when applying to the unseen environment due to the covariate shift. Conventional approaches like domain adaptation requires the pre-collected target data for iterative training, which is impractical in real-world applications. In this paper, we propose to adapt the deep models to the novel environment during inference. An previous solution is test time normalization, which substitutes the source statistics in BN layers with the target batch statistics. However, we show that test time normalization may potentially deteriorate the discriminative structures due to the mismatch between target batch statistics and source parameters. To this end, we present a general formulation α-BN to calibrate the batch statistics by mixing up the source and target statistics for both alleviating the domain shift and preserving the discriminative structures. Based on α-BN, we further present a novel loss function to form a unified test time adaptation framework Core, which performs the pairwise class correlation online optimization. Extensive experiments show that our approaches achieve the state-of-the-art performance on total twelve datasets from three topics, including model robustness to corruptions, domain generalization on image classification and semantic segmentation. Particularly, our α-BN improves 28.4% to 43.9% on GTA5 Cityscapes without any training, even outperforms the latest source-free domain adaptation method. Deep neural networks (DNNs) achieve impressive success across various applications, but heavily rely on the independent and identical distribution (i.i.d.) assumption. However, in real-world applications, the model is prone to encounter the novel instances. For examples, an automatic pilot should have robust performance under different weather conditions.