+
+
+
+Which regularization and hyperparameters? \( L_1 \) or \( L_2 \), soft classifiers, depths of trees and many other. Need to explore a large set of hyperparameters and regularization methods.
+
+
TEK5040 – Dyp læring for autonome systemer. The course addresses advanced algorithms and architectures for deep learning with neural networks. The course provides an introduction to how deep-learning techniques can be used in the construction of key parts of advanced autonomous systems that exist in physical environments and cyber environments.
+Based on multi-layer nonlinear neural networks, deep learning can
+learn directly from raw data, automatically extract and abstract
+features from layer to layer, and then achieve the goal of regression,
+classification, or ranking. Deep learning has made breakthroughs in
+computer vision, speech processing and natural language, and reached
+or even surpassed human level. The success of deep learning is mainly
+due to the three factors: big data, big model, and big computing.
+
+
+In the past few decades, many different architectures of deep neural
+networks have been proposed, such as
+
+
+
Convolutional neural networks, which are mostly used in image and video data processing, and have also been applied to sequential data such as text processing;
+
Recurrent neural networks, which can process sequential data of variable length and have been widely used in natural language understanding and speech processing;
+
Encoder-decoder framework, which is mostly used for image or sequence generation, such as machine translation, text summarization, and image captioning.
+Reinforcement learning is a sub-area of machine learning. It studies
+how agents take actions based on trial and error, so as to maximize
+some notion of cumulative reward in a dynamic system or
+environment. Due to its generality, the problem has also been studied
+in many other disciplines, such as game theory, control theory,
+operations research, information theory, multi-agent systems, swarm
+intelligence, statistics, and genetic algorithms.
+
+
+In March 2016, AlphaGo, a computer program that plays the board game
+Go, beat Lee Sedol in a five-game match. This was the first time a
+computer Go program had beaten a 9-dan (highest rank) professional
+without handicaps. AlphaGo is based on deep convolutional neural
+networks and reinforcement learning. AlphaGo’s victory was a major
+milestone in artificial intelligence and it has also made
+reinforcement learning a hot research area in the field of machine
+learning.
+
+
+The goal of transfer learning is to transfer the model or knowledge
+obtained from a source task to the target task, in order to resolve
+the issues of insufficient training data in the target task. The
+rationality of doing so lies in that usually the source and target
+tasks have inter-correlations, and therefore either the features,
+samples, or models in the source task might provide useful information
+for us to better solve the target task. Transfer learning is a hot
+research topic in recent years, with many problems still waiting to be
+solved in this space.
+
+
+The conventional deep generative model has a potential problem: the
+model tends to generate extreme instances to maximize the
+probabilistic likelihood, which will hurt its performance. Adversarial
+learning utilizes the adversarial behaviors (e.g., generating
+adversarial instances or training an adversarial model) to enhance the
+robustness of the model and improve the quality of the generated
+data. In recent years, one of the most promising unsupervised learning
+technologies, generative adversarial networks (GAN), has already been
+successfully applied to image, speech, and text.
+
+
+Dual learning is a new learning paradigm, the basic idea of which is
+to use the primal-dual structure between machine learning tasks to
+obtain effective feedback/regularization, and guide and strengthen the
+learning process, thus reducing the requirement of large-scale labeled
+data for deep learning. The idea of dual learning has been applied to
+many problems in machine learning, including machine translation,
+image style conversion, question answering and generation, image
+classification and generation, text classification and generation,
+image-to-text, and text-to-image.
+
+
+Distributed computation will speed up machine learning algorithms,
+significantly improve their efficiency, and thus enlarge their
+application. When distributed meets machine learning, more than just
+implementing the machine learning algorithms in parallel is required.
+
+
+Meta learning is an emerging research direction in machine
+learning. Roughly speaking, meta learning concerns learning how to
+learn, and focuses on the understanding and adaptation of the learning
+itself, instead of just completing a specific learning task. That is,
+a meta learner needs to be able to evaluate its own learning methods
+and adjust its own learning methods according to specific learning
+tasks.
+
+
+While there has been much progress in machine learning, there are also challenges.
+
+
+For example, the mainstream machine learning technologies are
+black-box approaches, making us concerned about their potential
+risks. To tackle this challenge, we may want to make machine learning
+more explainable and controllable. As another example, the
+computational complexity of machine learning algorithms is usually
+very high and we may want to invent lightweight algorithms or
+implementations. Furthermore, in many domains such as physics,
+chemistry, biology, and social sciences, people usually seek elegantly
+simple equations (e.g., the Schrödinger equation) to uncover the
+underlying laws behind various phenomena. In the field of machine
+learning, can we reveal simple laws instead of designing more complex
+models for data fitting? Although there are many challenges, we are
+still very optimistic about the future of machine learning. As we look
+forward to the future, here are what we think the research hotspots in
+the next ten years will be.
+
+
+Machine learning, especially deep learning, evolves rapidly. The
+ability gap between machine and human on many complex cognitive tasks
+becomes narrower and narrower. However, we are still in the very early
+stage in terms of explaining why those effective models work and how
+they work.
+
+
+What is missing: the gap between correlation and causation Most
+machine learning techniques, especially the statistical ones, depend
+highly on data correlation to make predictions and analyses. In
+contrast, rational humans tend to reply on clear and trustworthy
+causality relations obtained via logical reasoning on real and clear
+facts. It is one of the core goals of explainable machine learning to
+transition from solving problems by data correlation to solving
+problems by logical reasoning.
+
+
+Quantum machine learning is an emerging interdisciplinary research
+area at the intersection of quantum computing and machine learning.
+
+
+Quantum computers use effects such as quantum coherence and quantum
+entanglement to process information, which is fundamentally different
+from classical computers. Quantum algorithms have surpassed the best
+classical algorithms in several problems (e.g., searching for an
+unsorted database, inverting a sparse matrix), which we call quantum
+acceleration.
+
+
+When quantum computing meets machine learning, it can be a mutually
+beneficial and reinforcing process, as it allows us to take advantage
+of quantum computing to improve the performance of classical machine
+learning algorithms. In addition, we can also use the machine learning
+algorithms (on classic computers) to analyze and improve quantum
+computing systems.
+
+
Quantum machine learning algorithms based on linear algebra
+
+
+Many quantum machine learning algorithms are based on variants of
+quantum algorithms for solving linear equations, which can efficiently
+solve N-variable linear equations with complexity of O(log2 N) under
+certain conditions. The quantum matrix inversion algorithm can
+accelerate many machine learning methods, such as least square linear
+regression, least square version of support vector machine, Gaussian
+process, and more. The training of these algorithms can be simplified
+to solve linear equations. The key bottleneck of this type of quantum
+machine learning algorithms is data input—that is, how to initialize
+the quantum system with the entire data set. Although efficient
+data-input algorithms exist for certain situations, how to efficiently
+input data into a quantum system is as yet unknown for most cases.
+
+
+In quantum reinforcement learning, a quantum agent interacts with the
+classical environment to obtain rewards from the environment, so as to
+adjust and improve its behavioral strategies. In some cases, it
+achieves quantum acceleration by the quantum processing capabilities
+of the agent or the possibility of exploring the environment through
+quantum superposition. Such algorithms have been proposed in
+superconducting circuits and systems of trapped ions.
+
+
+Dedicated quantum information processors, such as quantum annealers
+and programmable photonic circuits, are well suited for building deep
+quantum networks. The simplest deep quantum network is the Boltzmann
+machine. The classical Boltzmann machine consists of bits with tunable
+interactions and is trained by adjusting the interaction of these bits
+so that the distribution of its expression conforms to the statistics
+of the data. To quantize the Boltzmann machine, the neural network can
+simply be represented as a set of interacting quantum spins that
+correspond to an adjustable Ising model. Then, by initializing the
+input neurons in the Boltzmann machine to a fixed state and allowing
+the system to heat up, we can read out the output qubits to get the
+result.
+
+
+Machine learning aims to imitate how humans
+learn. While we have developed successful machine learning algorithms,
+until now we have ignored one important fact: humans are social. Each
+of us is one part of the total society and it is difficult for us to
+live, learn, and improve ourselves, alone and isolated. Therefore, we
+should design machines with social properties. Can we let machines
+evolve by imitating human society so as to achieve more effective,
+intelligent, interpretable “social machine learning”?
+
+
+Early computer scientist Alan Kay said, The best way to predict the
+future is to create it. Therefore, all machine learning
+practitioners, whether scholars or engineers, professors or students,
+need to work together to advance these important research
+topics. Together, we will not just predict the future, but create it.
+
+
+
+
+
+
+
+
diff --git a/doc/pub/summary/html/reveal.js/plugin/leap/leap.js b/doc/pub/summary/html/reveal.js/plugin/leap/leap.js
new file mode 100644
index 000000000..48084ffb0
--- /dev/null
+++ b/doc/pub/summary/html/reveal.js/plugin/leap/leap.js
@@ -0,0 +1,159 @@
+/*
+ * Copyright (c) 2013, Leap Motion, Inc.
+ * All rights reserved.
+ *
+ * Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:
+ *
+ * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
+ * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.
+ * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ *
+ * Version 0.2.0 - http://js.leapmotion.com/0.2.0/leap.min.js
+ * Grab latest versions from http://js.leapmotion.com/
+ */
+
+!function(e,t,n){function i(n,s){if(!t[n]){if(!e[n]){var o=typeof require=="function"&&require;if(!s&&o)return o(n,!0);if(r)return r(n,!0);throw new Error("Cannot find module '"+n+"'")}var u=t[n]={exports:{}};e[n][0].call(u.exports,function(t){var r=e[n][1][t];return i(r?r:t)},u,u.exports)}return t[n].exports}var r=typeof require=="function"&&require;for(var s=0;s=this.size)return undefined;if(i>=this._buf.length)return undefined;return this._buf[(this.pos-i-1)%this.size]};CircularBuffer.prototype.push=function(o){this._buf[this.pos%this.size]=o;return this.pos++}},{}],3:[function(require,module,exports){var Connection=module.exports=require("./base_connection");Connection.prototype.setupSocket=function(){var connection=this;var socket=new WebSocket(this.getUrl());socket.onopen=function(){connection.handleOpen()};socket.onmessage=function(message){connection.handleData(message.data)};socket.onclose=function(){connection.handleClose()};return socket};Connection.prototype.startHeartbeat=function(){if(!this.protocol.sendHeartbeat||this.heartbeatTimer)return;var connection=this;var propertyName=null;if(typeof document.hidden!=="undefined"){propertyName="hidden"}else if(typeof document.mozHidden!=="undefined"){propertyName="mozHidden"}else if(typeof document.msHidden!=="undefined"){propertyName="msHidden"}else if(typeof document.webkitHidden!=="undefined"){propertyName="webkitHidden"}else{propertyName=undefined}var windowVisible=true;var focusListener=window.addEventListener("focus",function(e){windowVisible=true});var blurListener=window.addEventListener("blur",function(e){windowVisible=false});this.on("disconnect",function(){if(connection.heartbeatTimer){clearTimeout(connection.heartbeatTimer);delete connection.heartbeatTimer}window.removeEventListener(focusListener);window.removeEventListener(blurListener)});this.heartbeatTimer=setInterval(function(){var isVisible=propertyName===undefined?true:document[propertyName]===false;if(isVisible&&windowVisible){connection.sendHeartbeat()}else{connection.setHeartbeatState(false)}},this.opts.heartbeatInterval)}},{"./base_connection":1}],4:[function(require,module,exports){!function(process){var Frame=require("./frame"),CircularBuffer=require("./circular_buffer"),Pipeline=require("./pipeline"),EventEmitter=require("events").EventEmitter,gestureListener=require("./gesture").gestureListener,_=require("underscore");var Controller=module.exports=function(opts){var inNode=typeof process!=="undefined"&&process.title==="node";opts=_.defaults(opts||{},{inNode:inNode});this.inNode=opts.inNode;opts=_.defaults(opts||{},{frameEventName:this.useAnimationLoop()?"animationFrame":"deviceFrame",supressAnimationLoop:false});this.supressAnimationLoop=opts.supressAnimationLoop;this.frameEventName=opts.frameEventName;this.history=new CircularBuffer(200);this.lastFrame=Frame.Invalid;this.lastValidFrame=Frame.Invalid;this.lastConnectionFrame=Frame.Invalid;this.accumulatedGestures=[];if(opts.connectionType===undefined){this.connectionType=this.inBrowser()?require("./connection"):require("./node_connection")}else{this.connectionType=opts.connectionType}this.connection=new this.connectionType(opts);this.setupConnectionEvents()};Controller.prototype.gesture=function(type,cb){var creator=gestureListener(this,type);if(cb!==undefined){creator.stop(cb)}return creator};Controller.prototype.inBrowser=function(){return!this.inNode};Controller.prototype.useAnimationLoop=function(){return this.inBrowser()&&typeof chrome==="undefined"};Controller.prototype.connect=function(){var controller=this;if(this.connection.connect()&&this.inBrowser()&&!controller.supressAnimationLoop){var callback=function(){controller.emit("animationFrame",controller.lastConnectionFrame);window.requestAnimFrame(callback)};window.requestAnimFrame(callback)}};Controller.prototype.disconnect=function(){this.connection.disconnect()};Controller.prototype.frame=function(num){return this.history.get(num)||Frame.Invalid};Controller.prototype.loop=function(callback){switch(callback.length){case 1:this.on(this.frameEventName,callback);break;case 2:var controller=this;var scheduler=null;var immediateRunnerCallback=function(frame){callback(frame,function(){if(controller.lastFrame!=frame){immediateRunnerCallback(controller.lastFrame)}else{controller.once(controller.frameEventName,immediateRunnerCallback)}})};this.once(this.frameEventName,immediateRunnerCallback);break}this.connect()};Controller.prototype.addStep=function(step){if(!this.pipeline)this.pipeline=new Pipeline(this);this.pipeline.addStep(step)};Controller.prototype.processFrame=function(frame){if(frame.gestures){this.accumulatedGestures=this.accumulatedGestures.concat(frame.gestures)}if(this.pipeline){frame=this.pipeline.run(frame);if(!frame)frame=Frame.Invalid}this.lastConnectionFrame=frame;this.emit("deviceFrame",frame)};Controller.prototype.processFinishedFrame=function(frame){this.lastFrame=frame;if(frame.valid){this.lastValidFrame=frame}frame.controller=this;frame.historyIdx=this.history.push(frame);if(frame.gestures){frame.gestures=this.accumulatedGestures;this.accumulatedGestures=[];for(var gestureIdx=0;gestureIdx!=frame.gestures.length;gestureIdx++){this.emit("gesture",frame.gestures[gestureIdx],frame)}}this.emit("frame",frame)};Controller.prototype.setupConnectionEvents=function(){var controller=this;this.connection.on("frame",function(frame){controller.processFrame(frame)});this.on(this.frameEventName,function(frame){controller.processFinishedFrame(frame)});this.connection.on("disconnect",function(){controller.emit("disconnect")});this.connection.on("ready",function(){controller.emit("ready")});this.connection.on("connect",function(){controller.emit("connect")});this.connection.on("focus",function(){controller.emit("focus")});this.connection.on("blur",function(){controller.emit("blur")});this.connection.on("protocol",function(protocol){controller.emit("protocol",protocol)});this.connection.on("deviceConnect",function(evt){controller.emit(evt.state?"deviceConnected":"deviceDisconnected")})};_.extend(Controller.prototype,EventEmitter.prototype)}(require("__browserify_process"))},{"./circular_buffer":2,"./connection":3,"./frame":5,"./gesture":6,"./node_connection":16,"./pipeline":10,__browserify_process:18,events:17,underscore:20}],5:[function(require,module,exports){var Hand=require("./hand"),Pointable=require("./pointable"),createGesture=require("./gesture").createGesture,glMatrix=require("gl-matrix"),mat3=glMatrix.mat3,vec3=glMatrix.vec3,InteractionBox=require("./interaction_box"),_=require("underscore");var Frame=module.exports=function(data){this.valid=true;this.id=data.id;this.timestamp=data.timestamp;this.hands=[];this.handsMap={};this.pointables=[];this.tools=[];this.fingers=[];if(data.interactionBox){this.interactionBox=new InteractionBox(data.interactionBox)}this.gestures=[];this.pointablesMap={};this._translation=data.t;this._rotation=_.flatten(data.r);this._scaleFactor=data.s;this.data=data;this.type="frame";this.currentFrameRate=data.currentFrameRate;var handMap={};for(var handIdx=0,handCount=data.hands.length;handIdx!=handCount;handIdx++){var hand=new Hand(data.hands[handIdx]);hand.frame=this;this.hands.push(hand);this.handsMap[hand.id]=hand;handMap[hand.id]=handIdx}for(var pointableIdx=0,pointableCount=data.pointables.length;pointableIdx!=pointableCount;pointableIdx++){var pointable=new Pointable(data.pointables[pointableIdx]);pointable.frame=this;this.pointables.push(pointable);this.pointablesMap[pointable.id]=pointable;(pointable.tool?this.tools:this.fingers).push(pointable);if(pointable.handId!==undefined&&handMap.hasOwnProperty(pointable.handId)){var hand=this.hands[handMap[pointable.handId]];hand.pointables.push(pointable);(pointable.tool?hand.tools:hand.fingers).push(pointable)}}if(data.gestures){for(var gestureIdx=0,gestureCount=data.gestures.length;gestureIdx!=gestureCount;gestureIdx++){this.gestures.push(createGesture(data.gestures[gestureIdx]))}}};Frame.prototype.tool=function(id){var pointable=this.pointable(id);return pointable.tool?pointable:Pointable.Invalid};Frame.prototype.pointable=function(id){return this.pointablesMap[id]||Pointable.Invalid};Frame.prototype.finger=function(id){var pointable=this.pointable(id);return!pointable.tool?pointable:Pointable.Invalid};Frame.prototype.hand=function(id){return this.handsMap[id]||Hand.Invalid};Frame.prototype.rotationAngle=function(sinceFrame,axis){if(!this.valid||!sinceFrame.valid)return 0;var rot=this.rotationMatrix(sinceFrame);var cs=(rot[0]+rot[4]+rot[8]-1)*.5;var angle=Math.acos(cs);angle=isNaN(angle)?0:angle;if(axis!==undefined){var rotAxis=this.rotationAxis(sinceFrame);angle*=vec3.dot(rotAxis,vec3.normalize(vec3.create(),axis))}return angle};Frame.prototype.rotationAxis=function(sinceFrame){if(!this.valid||!sinceFrame.valid)return vec3.create();return vec3.normalize(vec3.create(),[this._rotation[7]-sinceFrame._rotation[5],this._rotation[2]-sinceFrame._rotation[6],this._rotation[3]-sinceFrame._rotation[1]])};Frame.prototype.rotationMatrix=function(sinceFrame){if(!this.valid||!sinceFrame.valid)return mat3.create();var transpose=mat3.transpose(mat3.create(),this._rotation);return mat3.multiply(mat3.create(),sinceFrame._rotation,transpose)};Frame.prototype.scaleFactor=function(sinceFrame){if(!this.valid||!sinceFrame.valid)return 1;return Math.exp(this._scaleFactor-sinceFrame._scaleFactor)};Frame.prototype.translation=function(sinceFrame){if(!this.valid||!sinceFrame.valid)return vec3.create();return vec3.subtract(vec3.create(),this._translation,sinceFrame._translation)};Frame.prototype.toString=function(){var str="Frame [ id:"+this.id+" | timestamp:"+this.timestamp+" | Hand count:("+this.hands.length+") | Pointable count:("+this.pointables.length+")";if(this.gestures)str+=" | Gesture count:("+this.gestures.length+")";str+=" ]";return str};Frame.prototype.dump=function(){var out="";out+="Frame Info: ";out+=this.toString();out+="
