Goto

Collaborating Authors

r/MachineLearning - [Research] Help relating to a theorem in machine learning

#artificialintelligence

This is related to a theorem that I have proved and its relation (or not) to an existing result. Essentially, I have shown that PAC-learning is undecidable in the Turing sense. The arxiv link to the paper is https://arxiv.org/abs/1808.06324 I am told that this is provable as a corollary of existing results. I was hinted that the fundamental theorem of statistical machine learning that relates the VC dimension and PAC-learning could be used to prove the undecidability of PAC-learning.


Deep Learning Papers Reading Roadmap

@machinelearnbot

If you are a newcomer to the Deep Learning area, the first question you may have is "Which paper should I start reading from?" Here is a reading roadmap of Deep Learning papers! You will find many papers that are quite new but really worth reading. I would continue adding papers to this roadmap. Editor: What follows is a portion of the papers from this list.


On Machine Learning and Structure for Mobile Robots

arXiv.org Artificial Intelligence

Due to recent advances - compute, data, models - the role of learning in autonomous systems has expanded significantly, rendering new applications possible for the first time. While some of the most significant benefits are obtained in the perception modules of the software stack, other aspects continue to rely on known manual procedures based on prior knowledge on geometry, dynamics, kinematics etc. Nonetheless, learning gains relevance in these modules when data collection and curation become easier than manual rule design. Building on this coarse and broad survey of current research, the final sections aim to provide insights into future potentials and challenges as well as the necessity of structure in current practical applications.


Theoretical Analysis of Adversarial Learning: A Minimax Approach

arXiv.org Machine Learning

We propose a general theoretical method for analyzing the risk bound in the presence of adversaries. In particular, we try to fit the adversarial learning problem into the minimax framework. We first show that the original adversarial learning problem could be reduced to a minimax statistical learning problem by introducing a transport map between distributions. Then we prove a risk bound for this minimax problem in terms of covering numbers. In contrast to previous minimax bounds in \cite{lee,far}, our bound is informative when the radius of the ambiguity set is small. Our method could be applied to multi-class classification problems and commonly-used loss functions such as hinge loss and ramp loss. As two illustrative examples, we derive the adversarial risk bounds for kernel-SVM and deep neural networks. Our results indicate that a stronger adversary might have a negative impact on the complexity of the hypothesis class and the existence of margin could serve as a defense mechanism to counter adversarial attacks.


How to train your MAML

arXiv.org Machine Learning

The field of few-shot learning has recently seen substantial advancements. Most of these advancements came from casting few-shot learning as a meta-learning problem. Model Agnostic Meta Learning or MAML is currently one of the best approaches for few-shot learning via meta-learning. MAML is simple, elegant and very powerful, however, it has a variety of issues, such as being very sensitive to neural network architectures, often leading to instability during training, requiring arduous hyperparameter searches to stabilize training and achieve high generalization and being very computationally expensive at both training and inference times. In this paper, we propose various modifications to MAML that not only stabilize the system, but also substantially improve the generalization performance, convergence speed and computational overhead of MAML, which we call MAML . The human capacity to learn new concepts using only a handful of samples is immense. In stark contrast, modern deep neural networks need, at a minimum, thousands of samples before they begin to learn representations that can generalize well to unseen data-points (Krizhevsky et al., 2012; Huang et al., 2017), and mostly fail when the data available is scarce. The fact that standard deep neural networks fail in the small data regime can provide hints about some of their potential shortcomings. Solving those shortcomings has the potential to open the door to understanding intelligence and advancing Artificial Intelligence.