paper-with-me

Papers

What Really is Deep Learning Doing?

2017-11-06 · Chuyu Xiong

Deep learning has achieved a great success in many areas, from computer vision to natural language processing, to game playing, and much more. Yet, what deep learning is really doing is still an open question. There are a lot of works in this direction. For example, [5] tried to explain deep learning by group renormalization, and [6] tried to explain deep learning from the view of functional approximation. In order to address this very crucial question, here we see deep learning from perspective of mechanical learning and learning machine (see [1], [2]). From this particular angle, we can see deep learning much better and answer with confidence: What deep learning is really doing? why it works well, how it works, and how much data is necessary for learning. We also will discuss advantages and disadvantages of deep learning at the end of this work.

📄 PDF Abstract BibTeX arXiv:1711.03577

Code (0)

등록된 구현이 없습니다.

Tasks

Deep LearningOpen-Ended Question Answering

Similar Papers 제목 키워드 기반

"What Are You Really Trying to Do?": Co-Creating Life Goals from Everyday Computer Use

2026-05-01 · Shardul Sapkota, Matthew Jörke, Zane Sabbagh, Omar Shaikh 외 arxiv

Recent advances in user modeling make it feasible to conduct open-ended inference over a person's everyday computer use. Despite longstanding visions of systems that deeply understand our actions and the purposes they se…

Moonshots for aging

2019-01-13 · Sandeep Kumar, Timothy R. Peterson

As the global population ages, there is increased interest in living longer and improving one's quality of life in later years. However, studying aging - the decline in body function - is expensive and time-consuming. An…

Philosophy

What Can We Really Learn from Post-editing?

2016-10-01 · AMTA 2016 10 · Marcis Pinnis, Rihards Kalnins, Raivis Skadins, Inguna Skadina

Do Deep Reinforcement Learning Algorithms really Learn to Navigate?

2018-01-01 · ICLR 2018 1 · Shurjo Banerjee, Vikas Dhiman, Brent Griffin, Jason J. Corso

Deep reinforcement learning (DRL) algorithms have demonstrated progress in learning to find a goal in challenging environments. As the title of the paper by Mirowski et al. (2016) suggests, one might assume that DRL-base…

Deep Reinforcement LearningNavigatereinforcement-learningReinforcement Learning+1

What's the Issue Here?: Task-based Evaluation of Reader Comment Summarization Systems

2016-05-01 · LREC 2016 5 · Emma Barker, Monica Paramita, Adam Funk, Emina Kurtic 외

Automatic summarization of reader comments in on-line news is an extremely challenging task and a capability for which there is a clear need. Work to date has focussed on producing extractive summaries using well-known t…

Clustering