Lee, Ian G. Fuss, Daniel J. Speakers optimize information density through syntactic reduction, Roger Levy, T. Church, Trevor J. Learnability and the doubling dimension, Yi Li, Philip M. Emergence of conjunctive visual features by quadratic independent component analysis, J.
Linear Versus Nonlinear Information Processing: A Look at Neural Networks | SpringerLink
Neal, Sam T. Freeman, Edward H. Attribute-efficient learning of decision lists and linear threshold functions under unconcentrated distributions, Philip M.
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Long, Rocco A. Mandel, Daniel P. Navarro, Thomas L. Pickup, David P. Capel, Stephen J. Rabbat, Mario A. Figueiredo, Robert D. Learning annotated hierarchies from relational data, Daniel M. Roy, Charles Kemp, Vikash K. Mansinghka, Joshua B. Rubinstein, Peter L.
Bartlett, J. NeurIPS was designed as a complementary open interdisciplinary meeting for researchers exploring biological and artificial Neural Networks. Reflecting this multidisciplinary approach, NeurIPS began in with information theorist Ed Posner as the conference president and learning theorist Yaser Abu-Mostafa and computational neurobiologist James Bower as co-program chairman. Research presented in the early NeurIPS meetings including a wide range of topics from efforts to solve purely engineering problems to the use of computer models as a tool for understanding biological nervous systems.
Since then, the biological and artificial systems research streams have diverged, and recent NeurIPS proceedings have been dominated by papers on machine learning , artificial intelligence and statistics.
Conference on Neural Information Processing Systems
Reflecting its origins at Snowbird, Utah , the meeting was accompanied by workshops organized at a nearby ski resort up until , when it outgrew ski resorts. Conference organizers considered abandoning the NIPS abbreviation  because of a slang connotation with the word nipples and as a historical slur against Japanese. After a comment period and survey of 2, conference participants, conference organizers decided to keep the abbreviation. The conference had 5, registered participants in [ citation needed ] and 8, in [ citation needed ] , making it the largest conference in Artificial Intelligence [ citation needed ].
Besides machine learning and neuroscience, other fields represented at NeurIPS include cognitive science , psychology , computer vision , statistical linguistics , and information theory. Although the 'Neural' in the NeurIPS acronym had become something of a historical relic, the resurgence of deep learning in neural networks since , fueled by faster computers and big data, has led to impressive achievement in speech recognition, object recognition in images, image captioning , language translation and world championship performance in the game of Go, based on neural architectures inspired by the hierarchy of areas in the visual cortex ConvNet and reinforcement learning inspired by the basal ganglia Temporal difference learning.
In addition to invited talks and symposia, NeurIPS also organizes two named lectureships to recognize distinguished researchers. From Wikipedia, the free encyclopedia. Dimensionality reduction. Structured prediction. Then that initial guess can be used as a starting point for a gradient descent algorithm which operates on the original complicated nonlinear objective function. To summarize, the key idea which has been empirically supported not only by talks at this conference but prior published research in the area is that we may be able to dramatically improve the efficiency of learning in learning machines if we present our learning machines with carefully crafted curricula.
Training them with simplified statistical environments in order to obtain initial parameter estimates, and then using those initial parameter estimates as a starting point for learning more complicated statistical environments. After the Monday Tutorial day, the main conference was held on Tuesday, Wednesday, and Thursday and consisted of continuous sessions beginning each day at 8am and ending around 5pm with poster sessions from 6pm onwards into the evening. Following the main conference, there were two days of conference workshops.
One workshop in particular discussed issues which I feel have not been sufficiently addressed by the machine learning community.
NIPS 2017: Long Beach, CA, USA
The basic idea, here, is that in many safety-critical applications it is not sufficient to have good predictions from our machine learning algorithms we need the algorithms to also provide us with good explanations of their predictions and we need to be convinced that the way the machines are arriving at their predictions is rational and appropriate.
For example, if a machine learning algorithm decided to deny your health insurance request or financial loan request and therefore consequently condemning your family to excessive hardships for the rest of their lives, you would think that the machine learning algorithm would be obligated to explain the logic behind its decision making process! I definitely want to devote any entire podcast to this topic! Too much material, however, to discuss today! At this point, I would like to introduce a relatively new feature to this podcast which is my review of a recent book in the area of Machine Learning.
I think this will be helpful to listeners because some of the books in this area are very introductory, some are very advanced, some focus on more theoretical issues, while others focus on more practical issues. These book reviews should be helpful because I will try to discuss the general contents of books which I think are especially relevant and additionally will make some comments regarding who might be interested in these books and who might not be interested.
If you are interested in implementing Deep Learning algorithms, then you must get this book!
This is the currently the best documentation on basic principles of Deep Learning Algorithms that exists! It is written by Dr. Ian Goodfellow, Dr. Yoshua Bengio, and Dr.
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Aaron Courville who are three experts in the field with considerably theoretical and empirical experience with Deep Learning algorithms. Chapter 5 is a basic introduction to Machine Learning algorithms which covers topics such as the importance of having a training and test data set, hyperparameters, maximum likelihood estimation, maximum a posteriori MAP estimation, and stochastic gradient descent algorithms.
Chapter 6 introduces feedforward multilayer perceptrons, different types of hidden units as well as different types of output unit representations corresponding to different types of probabilistic modeling assumptions regarding the distribution of the target given the input pattern. Chapter 6 also discusses the concept of differentiation algorithms using computational graphs.
The computational graph methodology is extremely powerful for the derivation of derivatives of objective functions for complicated network architectures. Chapter 7 discusses the all-important concept of regularization.
Adversarial training is also introduced as a regularization technique. Chapter 8 introduces optimization methods for training deep learning network architectures.
NIPS 2017: Long Beach, CA, USA
Such methods include: batch, minibatch, stochastic gradient descent SGD , SGD with momentum, parameter initialization strategies, ADAgrad, RMSprop and their variants, Limited Memory BFGS algorithms, updating groups of parameters at a time block coordinate descent , averaging the last few parameter estimates in an on-line adaptive learning algorithm to improve the estimate Polyak Averaging , supervised pretraining, continuation methods and curriculum learning.
Anyone who is using a deep learning algorithm should be familiar with all of these methods and when they are applicable because these different techniques were specifically designed to commonly encountered problems in specific deep learning applications. Chapter 9 discusses convolutional neural networks. Analogous to the convolution operator in the time domain, a convolutional neural network implements a convolution operator in the spatial domain.
The basic idea of the convolutional neural network is that one applies a collection of spatial convolution operators which have free parameters to the image which process very small regions of an image. The output of this collection of spatial convolution operators then forms the input pattern for the next layer of spatial convolution operators which are looking for statistical regularities in the outputs of the first layer. Chapter 10 discusses the concept of recurrent and recursive neural networks and how to derive learning algorithms for them using computational graphs.