A Deep Reinforcement Learning Neural Network Folding Proteins
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Training Autoencoders by Alternating Minimization
Under review as a conference paper at ICLR 2018 TRAINING AUTOENCODERS BY ALTERNATING MINI- MIZATION Anonymous authors Paper under double-blind review ABSTRACT We present DANTE, a novel method for training neural networks, in particular autoencoders, using the alternating minimization principle. DANTE provides a distinct perspective in lieu of traditional gradient-based backpropagation techniques commonly used to train deep networks. It utilizes an adaptation of quasi-convex optimization techniques to cast autoencoder training as a bi-quasi-convex optimiza- tion problem. We show that for autoencoder configurations with both differentiable (e.g. sigmoid) and non-differentiable (e.g. ReLU) activation functions, we can perform the alternations very effectively. DANTE effortlessly extends to networks with multiple hidden layers and varying network configurations. In experiments on standard datasets, autoencoders trained using the proposed method were found to be very promising and competitive to traditional backpropagation techniques, both in terms of quality of solution, as well as training speed. 1 INTRODUCTION For much of the recent march of deep learning, gradient-based backpropagation methods, e.g. Stochastic Gradient Descent (SGD) and its variants, have been the mainstay of practitioners. The use of these methods, especially on vast amounts of data, has led to unprecedented progress in several areas of artificial intelligence. On one hand, the intense focus on these techniques has led to an intimate understanding of hardware requirements and code optimizations needed to execute these routines on large datasets in a scalable manner. Today, myriad off-the-shelf and highly optimized packages exist that can churn reasonably large datasets on GPU architectures with relatively mild human involvement and little bootstrap effort. -
CS 189 Introduction to Machine Learning Spring 2021 Jonathan Shewchuk HW6
CS 189 Introduction to Machine Learning Spring 2021 Jonathan Shewchuk HW6 Due: Wednesday, April 21 at 11:59 pm Deliverables: 1. Submit your predictions for the test sets to Kaggle as early as possible. Include your Kaggle scores in your write-up (see below). The Kaggle competition for this assignment can be found at • https://www.kaggle.com/c/spring21-cs189-hw6-cifar10 2. The written portion: • Submit a PDF of your homework, with an appendix listing all your code, to the Gradescope assignment titled “Homework 6 Write-Up”. Please see section 3.3 for an easy way to gather all your code for the submission (you are not required to use it, but we would strongly recommend using it). • In addition, please include, as your solutions to each coding problem, the specific subset of code relevant to that part of the problem. Whenever we say “include code”, that means you can either include a screenshot of your code, or typeset your code in your submission (using markdown or LATEX). • You may typeset your homework in LaTeX or Word (submit PDF format, not .doc/.docx format) or submit neatly handwritten and scanned solutions. Please start each question on a new page. • If there are graphs, include those graphs in the correct sections. Do not put them in an appendix. We need each solution to be self-contained on pages of its own. • In your write-up, please state with whom you worked on the homework. • In your write-up, please copy the following statement and sign your signature next to it. -
Can Temporal-Difference and Q-Learning Learn Representation? a Mean-Field Analysis
Can Temporal-Difference and Q-Learning Learn Representation? A Mean-Field Analysis Yufeng Zhang Qi Cai Northwestern University Northwestern University Evanston, IL 60208 Evanston, IL 60208 [email protected] [email protected] Zhuoran Yang Yongxin Chen Zhaoran Wang Princeton University Georgia Institute of Technology Northwestern University Princeton, NJ 08544 Atlanta, GA 30332 Evanston, IL 60208 [email protected] [email protected] [email protected] Abstract Temporal-difference and Q-learning play a key role in deep reinforcement learning, where they are empowered by expressive nonlinear function approximators such as neural networks. At the core of their empirical successes is the learned feature representation, which embeds rich observations, e.g., images and texts, into the latent space that encodes semantic structures. Meanwhile, the evolution of such a feature representation is crucial to the convergence of temporal-difference and Q-learning. In particular, temporal-difference learning converges when the function approxi- mator is linear in a feature representation, which is fixed throughout learning, and possibly diverges otherwise. We aim to answer the following questions: When the function approximator is a neural network, how does the associated feature representation evolve? If it converges, does it converge to the optimal one? We prove that, utilizing an overparameterized two-layer neural network, temporal- difference and Q-learning globally minimize the mean-squared projected Bellman error at a sublinear rate. Moreover, the associated feature representation converges to the optimal one, generalizing the previous analysis of [21] in the neural tan- gent kernel regime, where the associated feature representation stabilizes at the initial one. The key to our analysis is a mean-field perspective, which connects the evolution of a finite-dimensional parameter to its limiting counterpart over an infinite-dimensional Wasserstein space. -
Survey on Reinforcement Learning for Language Processing
Survey on reinforcement learning for language processing V´ıctorUc-Cetina1, Nicol´asNavarro-Guerrero2, Anabel Martin-Gonzalez1, Cornelius Weber3, Stefan Wermter3 1 Universidad Aut´onomade Yucat´an- fuccetina, [email protected] 2 Aarhus University - [email protected] 3 Universit¨atHamburg - fweber, [email protected] February 2021 Abstract In recent years some researchers have explored the use of reinforcement learning (RL) algorithms as key components in the solution of various natural language process- ing tasks. For instance, some of these algorithms leveraging deep neural learning have found their way into conversational systems. This paper reviews the state of the art of RL methods for their possible use for different problems of natural language processing, focusing primarily on conversational systems, mainly due to their growing relevance. We provide detailed descriptions of the problems as well as discussions of why RL is well-suited to solve them. Also, we analyze the advantages and limitations of these methods. Finally, we elaborate on promising research directions in natural language processing that might benefit from reinforcement learning. Keywords| reinforcement learning, natural language processing, conversational systems, pars- ing, translation, text generation 1 Introduction Machine learning algorithms have been very successful to solve problems in the natural language pro- arXiv:2104.05565v1 [cs.CL] 12 Apr 2021 cessing (NLP) domain for many years, especially supervised and unsupervised methods. However, this is not the case with reinforcement learning (RL), which is somewhat surprising since in other domains, reinforcement learning methods have experienced an increased level of success with some impressive results, for instance in board games such as AlphaGo Zero [106]. -
Neural Networks Shuai Li John Hopcroft Center, Shanghai Jiao Tong University
Lecture 6: Neural Networks Shuai Li John Hopcroft Center, Shanghai Jiao Tong University https://shuaili8.github.io https://shuaili8.github.io/Teaching/VE445/index.html 1 Outline • Perceptron • Activation functions • Multilayer perceptron networks • Training: backpropagation • Examples • Overfitting • Applications 2 Brief history of artificial neural nets • The First wave • 1943 McCulloch and Pitts proposed the McCulloch-Pitts neuron model • 1958 Rosenblatt introduced the simple single layer networks now called Perceptrons • 1969 Minsky and Papert’s book Perceptrons demonstrated the limitation of single layer perceptrons, and almost the whole field went into hibernation • The Second wave • 1986 The Back-Propagation learning algorithm for Multi-Layer Perceptrons was rediscovered and the whole field took off again • The Third wave • 2006 Deep (neural networks) Learning gains popularity • 2012 made significant break-through in many applications 3 Biological neuron structure • The neuron receives signals from their dendrites, and send its own signal to the axon terminal 4 Biological neural communication • Electrical potential across cell membrane exhibits spikes called action potentials • Spike originates in cell body, travels down axon, and causes synaptic terminals to release neurotransmitters • Chemical diffuses across synapse to dendrites of other neurons • Neurotransmitters can be excitatory or inhibitory • If net input of neuro transmitters to a neuron from other neurons is excitatory and exceeds some threshold, it fires an action potential 5 Perceptron • Inspired by the biological neuron among humans and animals, researchers build a simple model called Perceptron • It receives signals 푥푖’s, multiplies them with different weights 푤푖, and outputs the sum of the weighted signals after an activation function, step function 6 Neuron vs. -
Foldit Gamers Improve Protein Design Through Crowdsourcing 25 January 2012, by Bob Yirka
Foldit gamers improve protein design through crowdsourcing 25 January 2012, by Bob Yirka chemical reactions. In earlier versions of the Foldit game, players were simply given existing proteins to play with and asked to find the minimal energy state for them by folding them in optimum ways, this latest version has gone much farther by giving players the opportunity to come up with a whole new protein design. To create the new design, gamers were given a Image: Nature Biotechnology (2012) simple beginning structure and some basic ideas doi:10.1038/nbt.2109 about the goal of the new protein, in this case to serve as a better catalyst for a class of Diels-Alder reactions, which are used to synthesize many commercial products. After offering some ideas (PhysOrg.com) -- Gamers on Foldit have such as remodeling certain sections to make them succeeded in improving the catalyst abilities of an behave in certain ways, the gamers went to work enzyme, making it 18-fold more active than the folding the proteins using the tools at hand. original version. The idea is the brainchild of University of Washington scientist Zoran Popovic The first go-round proved mostly futile, with few who is director of the Center for Game Science, gamers coming up with good improvements. To and biochemist David Baker. Together they have improve the results, the team took the best foldings created the Foldit site which is a video game from the first round and fed them back into the application that allows players to work with protein game allowing gamers to improve on them. -
Increasing Public Involvement in Structural Biology
Structure Commentary Increasing Public Involvement in Structural Biology Seth Cooper,1,* Firas Khatib,2 and David Baker2 1Department of Computer Science 2Department of Biochemistry University of Washington, Seattle, WA 98195, USA *Correspondence: [email protected] http://dx.doi.org/10.1016/j.str.2013.08.009 Public participation in scientific research can be a powerful supplement to more-traditional approaches. We discuss aspects of the public participation project Foldit that may help others interested in starting their own projects. It is now easier than ever for the public to We’re very excited about the possibility Openness to Collaboration get involved in science. The Internet has for games and other forms of public in a Variety of Forms made it feasible for research groups to involvement in science to help advance The core of the project has been a very easily connect with people all over the the field. To our knowledge, there have fruitful collaboration between the Com- world. Personal computers have also been a few other projects actively puter Science and Engineering Depart- become powerful enough to run compu- involving the public in structural biology, ment and the Biochemistry Department tationally intensive programs, giving the and we look forward to many more in at the University of Washington. Both public the opportunity to contribute to the future. Structural biology problems departments were able to bring their scientific research. Volunteer computing involving the analysis of existing mole- knowledge and skills together to make a allows the public to share their spare cules and the design of new ones are successful team. -
Dynamic Modification of Activation Function Using the Backpropagation Algorithm in the Artificial Neural Networks
(IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 10, No. 4, 2019 Dynamic Modification of Activation Function using the Backpropagation Algorithm in the Artificial Neural Networks Marina Adriana Mercioni1, Alexandru Tiron2, Stefan Holban3 Department of Computer Science Politehnica University Timisoara, Timisoara, Romania Abstract—The paper proposes the dynamic modification of These functions represent the main activation functions, the activation function in a learning technique, more exactly being currently the most used by neural networks, representing backpropagation algorithm. The modification consists in from this reason a standard in building of an architecture of changing slope of sigmoid function for activation function Neural Network type [6,7]. according to increase or decrease the error in an epoch of learning. The study was done using the Waikato Environment for The activation functions that have been proposed along the Knowledge Analysis (WEKA) platform to complete adding this time were analyzed in the light of the applicability of the feature in Multilayer Perceptron class. This study aims the backpropagation algorithm. It has noted that a function that dynamic modification of activation function has changed to enables differentiated rate calculated by the neural network to relative gradient error, also neural networks with hidden layers be differentiated so and the error of function becomes have not used for it. differentiated [8]. Keywords—Artificial neural networks; activation function; The main purpose of activation function consists in the sigmoid function; WEKA; multilayer perceptron; instance; scalation of outputs of the neurons in neural networks and the classifier; gradient; rate metric; performance; dynamic introduction a non-linear relationship between the input and modification output of the neuron [9]. -
Algorithm Discovery by Protein Folding Game Players
Algorithm discovery by protein folding game players Firas Khatiba, Seth Cooperb, Michael D. Tykaa, Kefan Xub, Ilya Makedonb, Zoran Popovićb, David Bakera,c,1, and Foldit Players aDepartment of Biochemistry; bDepartment of Computer Science and Engineering; and cHoward Hughes Medical Institute, University of Washington, Box 357370, Seattle, WA 98195 Contributed by David Baker, October 5, 2011 (sent for review June 29, 2011) Foldit is a multiplayer online game in which players collaborate As the players themselves understand their strategies better than and compete to create accurate protein structure models. For spe- anyone, we decided to allow them to codify their algorithms cific hard problems, Foldit player solutions can in some cases out- directly, rather than attempting to automatically learn approxi- perform state-of-the-art computational methods. However, very mations. We augmented standard Foldit play with the ability to little is known about how collaborative gameplay produces these create, edit, share, and rate gameplay macros, referred to as results and whether Foldit player strategies can be formalized and “recipes” within the Foldit game (10). In the game each player structured so that they can be used by computers. To determine has their own “cookbook” of such recipes, from which they can whether high performing player strategies could be collectively invoke a variety of interactive automated strategies. Players can codified, we augmented the Foldit gameplay mechanics with tools share recipes they write with the rest of the Foldit community or for players to encode their folding strategies as “recipes” and to they can choose to keep their creations to themselves. share their recipes with other players, who are able to further mod- In this paper we describe the quite unexpected evolution of ify and redistribute them. -
Games As a Platform for Student Participation in Authentic Scientific Research
Games as a Platform for Student Participation in Authentic Scientific Research Rikke Magnussen1, Sidse Damgaard Hansen2, Tilo Planke2 and Jacob Friis Sherson2 AU Ideas Center for Community Driven Research, CODER 1ResearchLab: ICT and Design for Learning, Department of Communication, Aalborg University, Denmark 2Department of Physics and Astronomy, Aarhus University, Denmark [email protected] [email protected] [email protected] [email protected] Abstract: This paper presents results from the design and testing of an educational version of Quantum Moves, a Scientific Discovery Game that allows players to help solve authentic scientific challenges in the effort to develop a quantum computer. The primary aim of developing a game-based platform for student-research collaboration is to investigate if and how this type of game concept can strengthen authentic experimental practice and the creation of new knowledge in science education. Researchers and game developers tested the game in three separate high school classes (Class 1, 2, and 3). The tests were documented using video observations of students playing the game, qualitative interviews, and qualitative and quantitative questionnaires. The focus of the tests has been to study players' motivation and their experience of learning through participation in authentic scientific inquiry. In questionnaires conducted in the two first test classes students found that the aspects of doing “real scientific research” and solving physics problems were the more interesting aspects of playing the game. However, designing a game that facilitates professional research collaboration while simultaneously introducing quantum physics to high school students proved to be a challenge. A collaborative learning design was implemented in Class 3, where students were given expert roles such as experimental and theoretical physicists. -
An Introduction to Deep Reinforcement Learning
An Introduction to Deep Reinforcement Learning Ehsan Abbasnejad Remember: Supervised Learning We have a set of sample observations, with labels learn to predict the labels, given a new sample cat Learn the function that associates a picture of a dog/cat with the label dog Remember: supervised learning We need thousands of samples Samples have to be provided by experts There are applications where • We can’t provide expert samples • Expert examples are not what we mimic • There is an interaction with the world Deep Reinforcement Learning AlphaGo Scenario of Reinforcement Learning Observation Action State Change the environment Agent Don’t do that Reward Environment Agent learns to take actions maximizing expected Scenario of Reinforcement Learningreward. Observation Action State Change the environment Agent Thank you. Reward https://yoast.com/how-t Environment o-clean-site-structure/ Machine Learning Actor/Policy ≈ Looking for a Function Action = π( Observation ) Observation Action Function Function input output Used to pick the Reward best function Environment Reinforcement Learning in a nutshell RL is a general-purpose framework for decision-making • RL is for an agent with the capacity to act • Each action influences the agent’s future state • Success is measured by a scalar reward signal Goal: select actions to maximise future reward Deep Learning in a nutshell DL is a general-purpose framework for representation learning • Given an objective • Learning representation that is required to achieve objective • Directly from raw inputs -
Modelling Working Memory Using Deep Recurrent Reinforcement Learning
Modelling Working Memory using Deep Recurrent Reinforcement Learning Pravish Sainath1;2;3 Pierre Bellec1;3 Guillaume Lajoie 2;3 1Centre de Recherche, Institut Universitaire de Gériatrie de Montréal 2Mila - Quebec AI Institute 3Université de Montréal Abstract 1 In cognitive systems, the role of a working memory is crucial for visual reasoning 2 and decision making. Tremendous progress has been made in understanding the 3 mechanisms of the human/animal working memory, as well as in formulating 4 different frameworks of artificial neural networks. In the case of humans, the [1] 5 visual working memory (VWM) task is a standard one in which the subjects are 6 presented with a sequence of images, each of which needs to be identified as to 7 whether it was already seen or not. Our work is a study of multiple ways to learn a 8 working memory model using recurrent neural networks that learn to remember 9 input images across timesteps in supervised and reinforcement learning settings. 10 The reinforcement learning setting is inspired by the popular view in Neuroscience 11 that the working memory in the prefrontal cortex is modulated by a dopaminergic 12 mechanism. We consider the VWM task as an environment that rewards the 13 agent when it remembers past information and penalizes it for forgetting. We 14 quantitatively estimate the performance of these models on sequences of images [2] 15 from a standard image dataset (CIFAR-100 ) and their ability to remember 16 and recall. Based on our analysis, we establish that a gated recurrent neural 17 network model with long short-term memory units trained using reinforcement 18 learning is powerful and more efficient in temporally consolidating the input spatial 19 information.