A curated list of resources dedicated to Natural Language Processing (NLP)
https://github.com/keon/awesome-nlp
#nlp #deep_learning

A must read document for deep learning & machine learning practitioners

https://www.deeplearningbook.org/contents/guidelines.html
#deep_learning #machine_learning

A fascinating research paper in the intersection of Graph Neural Networks and Reinforcement Learning for tackling Robotics challenges

https://openreview.net/pdf?id=S1sqHMZCb
#robotics #deep_learning #geometric_deep_learning

Noam Chomsky: Language, Cognition, and Deep Learning | Artificial Intelligence

Noam Chomsky is one of the greatest minds of our time and is one of the most cited scholars in history. He is a linguist, philosopher, cognitive scientist, historian, social critic, and political activist. He has spent over 60 years at MIT and recently also joined the University of Arizona. This conversation is part of the Artificial Intelligence podcast.

https://www.youtube.com/watch?v=cMscNuSUy0I
#natural_language_processing #deep_learning

Dive into Deep Learning (D2L Book)

Dive into Deep Learning: an interactive deep learning book with code, math, and discussions, based on the NumPy interface

https://github.com/d2l-ai/d2l-en
#deep_learning

An Overview of Recent State of the Art Deep Learning Algorithms/Architectures

Lecture on most recent research and developments in deep learning, and hopes for 2020. This is not intended to be a list of SOTA benchmark results, but rather a set of highlights of machine learning and AI innovations and progress in academia, industry, and society in general. This lecture is part of the MIT Deep Learning Lecture Series.

https://www.youtube.com/watch?v=0VH1Lim8gL8&t=999s
#deep_learning #artificial_intelligence

Deep Reasoning Papers

A repository which contains recent papers including Neural Symbolic Reasoning, Logical Reasoning, Visual Reasoning, natural language reasoning and any other topics connecting deep learning and reasoning.

https://github.com/floodsung/Deep-Reasoning-Papers
#reasoning #deep_learning #artificial_intelligence

An overview of gradient descent optimization algorithms

Abstract: Gradient descent optimization algorithms, while increasingly popular, are often used as black-box optimizers, as practical explanations of their strengths and weaknesses are hard to come by. This article aims to provide the reader with intuitions with regard to the behaviour of different algorithms that will allow her to put them to use. In the course of this overview, we look at different variants of gradient descent, summarize challenges, introduce the most common optimization algorithms, review architectures in a parallel and distributed setting, and investigate additional strategies for optimizing gradient descent

https://arxiv.org/pdf/1609.04747.pdf
#deep_learning #optimization

A cool 3D representation of the structure of BERT language model

Blogpost: https://peltarion.com/knowledge-center/documentation/modeling-view/build-an-ai-model/blocks/bert-encoder
#NLP #deep_learning

Critique of Honda Prize for Dr. Hinton

Summary: Hinton has made significant contributions to artificial neural networks (NNs) and deep learning, but Honda credits him for fundamental inventions of others whom he did not cite. Science must not allow corporate PR to distort the academic record. Sec. I: Modern backpropagation was created by Linnainmaa (1970), not by Rumelhart & Hinton & Williams (1985). Ivakhnenko's deep feedforward nets (since 1965) learned internal representations long before Hinton's shallower ones (1980s). Sec. II: Hinton's unsupervised pre-training for deep NNs in the 2000s was conceptually a rehash of my unsupervised pre-training for deep NNs in 1991. And it was irrelevant for the deep learning revolution of the early 2010s which was mostly based on supervised learning - twice my lab spearheaded the shift from unsupervised pre-training to pure supervised learning (1991-95 and 2006-11). Sec. III: The first superior end-to-end neural speech recognition was based on two methods from my lab: LSTM (1990s-2005) and CTC (2006). Hinton et al. (2012) still used an old hybrid approach of the 1980s and 90s, and did not compare it to the revolutionary CTC-LSTM (which was soon on most smartphones). Sec. IV: Our group at IDSIA had superior award-winning computer vision through deep learning (2011) before Hinton's (2012). Sec. V: Hanson (1990) had a variant of "dropout" long before Hinton (2012). Sec. VI: In the 2010s, most major AI-based services across the world (speech recognition, language translation, etc.) on billions of devices were mostly based on our deep learning techniques, not on Hinton's. Repeatedly, Hinton omitted references to fundamental prior art (Sec. I & II & III & V). However, as Elvis Presley put it, "Truth is like the sun. You can shut it out for a time, but it ain't goin' away."

http://people.idsia.ch/~juergen/critique-honda-prize-hinton.html
#deep_learning

The Cost of Training NLP Models: A Concise Overview

Abstract: We review the cost of training large-scale language models, and the drivers of these costs. The intended audience includes engineers and scientists budgeting their model-training experiments, as well as non-practitioners trying to make sense of the economics of modern-day Natural Language Processing (NLP).

https://arxiv.org/abs/2004.08900
#nlp #deep_learning

Grad-CAM++: Generalized Gradient-based Visual Explanations for Deep Convolutional Networks

Abstract: Over the last decade, Convolutional Neural Network (CNN) models have been highly successful in solving complex vision based problems. However, deep models are perceived as "black box" methods considering the lack of understanding of their internal functioning. There has been a significant recent interest to develop explainable deep learning models, and this paper is an effort in this direction. Building on a recently proposed method called Grad-CAM, we propose Grad-CAM++ to provide better visual explanations of CNN model predictions (when compared to Grad-CAM), in terms of better localization of objects as well as explaining occurrences of multiple objects of a class in a single image. We provide a mathematical explanation for the proposed method, Grad-CAM++, which uses a weighted combination of the positive partial derivatives of the last convolutional layer feature maps with respect to a specific class score as weights to generate a visual explanation for the class label under consideration. Our extensive experiments and evaluations, both subjective and objective, on standard datasets showed that Grad-CAM++ indeed provides better visual explanations for a given CNN architecture when compared to Grad-CAM.

https://arxiv.org/pdf/1710.11063.pdf
#deep_learning #computer_vision

Towards Biologically Plausible Deep Learning

Abstract: Neuroscientists have long criticized deep learning algorithms as incompatible with current knowledge of neurobiology. We explore more biologically plausible versions of deep representation learning, focusing here mostly on unsupervised learning but developing a learning mechanism that could account for supervised, unsupervised and reinforcement learning. The starting point is that the basic learning rule believed to govern synaptic weight updates (Spike-Timing-Dependent Plasticity) arises out of a simple update rule that makes a lot of sense from a machine learning point of view and can be interpreted as gradient descent on some objective function so long as the neuronal dynamics push firing rates towards better values of the objective function (be it supervised, unsupervised, or reward-driven). The second main idea is that this corresponds to a form of the variational EM algorithm, i.e., with approximate rather than exact posteriors, implemented by neural dynamics. Another contribution of this paper is that the gradients required for updating the hidden states in the above variational interpretation can be estimated using an approximation that only requires propagating activations forward and backward, with pairs of layers learning to form a denoising auto-encoder. Finally, we extend the theory about the probabilistic interpretation of auto-encoders to justify improved sampling schemes based on the generative interpretation of denoising auto-encoders, and we validate all these ideas on generative learning tasks.

https://arxiv.org/abs/1502.04156
#deep_learning #neuroscience