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1. Using agent-based modelling to investigate intervention algorithms to reduce polarisation in online social networks

Author

Coates, Adam

Subjects
006.3
Abstract

Across much of the western world, political polarisation is on the rise. This has the effect of hindering political discourse, stifling open discussion, and in extreme cases has led to violence. The process of polarising and radicalising vulnerable individuals has migrated to social media websites, which have been implicated in several high profile terror attacks. Within this thesis we model and investigate various algorithms to prevent the spread of polarisation and extremist ideology by employing agent-based modelling techniques from the field of opinion dynamics. The contributions of our work include the following aspects. Firstly, we have developed a unified framework for opinion dynamics, allowing us to experiment easily on a number of different existing models and bringing together sometimes disparate innovations from across the field into one system. Secondly, this unified framework has been implemented in a modular simulator able to perfectly replicate results from purpose-built, stand-alone simulators for two widely used models, namely Relative Agreement and CODA, and then released to the public as the first general-purpose opinion dynamics simulator. Thirdly, we have developed two new intervention algorithms, along with a new metric for measuring the effectiveness of an intervention strategy, which aim to reduce the spread of polarisation across a network with low computational cost. These methods are compared to existing centrality-based methods upon a random network. The experimental results show our proposed approaches outperform centrality measures. We find that our ii iii algorithms are able to prevent up to 40% of non-extremist agents becoming extreme by removing only 10% of the network’s edges. Fourthly, we have investigated the efficacy of these intervention algorithms on polarisation under different scenarios (e.g. variable costs, different network structures). The experimental validation proves the proposed approach is robust and has performed favourably compared existing methods such as centrality-based methods especially on the second type of network. Finally, we have developed a broadcast-based communication system for agents, designed to mimic the one-way broadcast nature of a public social media post such as Twitter, in contrast to the existing model which emulates a two-way private conversation. The experimental result shows a lessening of the impact of our interventions, demonstrating the need for further investigation of such communication methods.

Published
2020

2. The Prevalence of Philosophical Assumptions Described in Mixed Methods Research in Education

Author

Coates, Adam

Abstract

Philosophical arguments are expected to provide the foundation of research and should be mentioned when reporting research. This study explores the presentation of philosophical assumptions in 1,026 mixed methods research (MMR) articles from the field of education. Eighty-one papers (7.9%) provided philosophical commitments, and 31 distinct stances were claimed. Philosophy was presented with different roles, including that philosophical stance can direct the decision to use mixed methods or result from that choice. This article contributes the field of MMR by highlighting an oversight in the reporting practices of the MMR community. Possible causes for the findings include weaknesses in research education and knowledge. Alternative models of the research process may be required to accurately match descriptions of research practice.

Published
2021
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3. Cold Fusion: Training Seq2Seq Models Together with Language Models

Author

Sriram, Anuroop, Jun, Heewoo, Satheesh, Sanjeev, and Coates, Adam

Subjects
Computer Science - Computation and Language
Abstract

Sequence-to-sequence (Seq2Seq) models with attention have excelled at tasks which involve generating natural language sentences such as machine translation, image captioning and speech recognition. Performance has further been improved by leveraging unlabeled data, often in the form of a language model. In this work, we present the Cold Fusion method, which leverages a pre-trained language model during training, and show its effectiveness on the speech recognition task. We show that Seq2Seq models with Cold Fusion are able to better utilize language information enjoying i) faster convergence and better generalization, and ii) almost complete transfer to a new domain while using less than 10% of the labeled training data.

Published
2017

4. Exploring Neural Transducers for End-to-End Speech Recognition

Author

Battenberg, Eric, Chen, Jitong, Child, Rewon, Coates, Adam, Gaur, Yashesh, Li, Yi, Liu, Hairong, Satheesh, Sanjeev, Seetapun, David, Sriram, Anuroop, and Zhu, Zhenyao

Subjects
Computer Science - Computation and Language, Computer Science - Neural and Evolutionary Computing
Abstract

In this work, we perform an empirical comparison among the CTC, RNN-Transducer, and attention-based Seq2Seq models for end-to-end speech recognition. We show that, without any language model, Seq2Seq and RNN-Transducer models both outperform the best reported CTC models with a language model, on the popular Hub5'00 benchmark. On our internal diverse dataset, these trends continue - RNNTransducer models rescored with a language model after beam search outperform our best CTC models. These results simplify the speech recognition pipeline so that decoding can now be expressed purely as neural network operations. We also study how the choice of encoder architecture affects the performance of the three models - when all encoder layers are forward only, and when encoders downsample the input representation aggressively.

Published
2017

5. Principled Hybrids of Generative and Discriminative Domain Adaptation

Author

Zhao, Han, Zhu, Zhenyao, Hu, Junjie, Coates, Adam, and Gordon, Geoff

Subjects
Computer Science - Learning, Computer Science - Artificial Intelligence
Abstract

We propose a probabilistic framework for domain adaptation that blends both generative and discriminative modeling in a principled way. Under this framework, generative and discriminative models correspond to specific choices of the prior over parameters. This provides us a very general way to interpolate between generative and discriminative extremes through different choices of priors. By maximizing both the marginal and the conditional log-likelihoods, models derived from this framework can use both labeled instances from the source domain as well as unlabeled instances from both source and target domains. Under this framework, we show that the popular reconstruction loss of autoencoder corresponds to an upper bound of the negative marginal log-likelihoods of unlabeled instances, where marginal distributions are given by proper kernel density estimations. This provides a way to interpret the empirical success of autoencoders in domain adaptation and semi-supervised learning. We instantiate our framework using neural networks, and build a concrete model, DAuto. Empirically, we demonstrate the effectiveness of DAuto on text, image and speech datasets, showing that it outperforms related competitors when domain adaptation is possible.

Published
2017

6. Reducing Bias in Production Speech Models

Author

Battenberg, Eric, Child, Rewon, Coates, Adam, Fougner, Christopher, Gaur, Yashesh, Huang, Jiaji, Jun, Heewoo, Kannan, Ajay, Kliegl, Markus, Kumar, Atul, Liu, Hairong, Rao, Vinay, Satheesh, Sanjeev, Seetapun, David, Sriram, Anuroop, and Zhu, Zhenyao

Subjects
Computer Science - Computation and Language
Abstract

Replacing hand-engineered pipelines with end-to-end deep learning systems has enabled strong results in applications like speech and object recognition. However, the causality and latency constraints of production systems put end-to-end speech models back into the underfitting regime and expose biases in the model that we show cannot be overcome by "scaling up", i.e., training bigger models on more data. In this work we systematically identify and address sources of bias, reducing error rates by up to 20% while remaining practical for deployment. We achieve this by utilizing improved neural architectures for streaming inference, solving optimization issues, and employing strategies that increase audio and label modelling versatility.

Published
2017

7. Convolutional Recurrent Neural Networks for Small-Footprint Keyword Spotting

Author

Arik, Sercan O., Kliegl, Markus, Child, Rewon, Hestness, Joel, Gibiansky, Andrew, Fougner, Chris, Prenger, Ryan, and Coates, Adam

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence, Computer Science - Learning
Abstract

Keyword spotting (KWS) constitutes a major component of human-technology interfaces. Maximizing the detection accuracy at a low false alarm (FA) rate, while minimizing the footprint size, latency and complexity are the goals for KWS. Towards achieving them, we study Convolutional Recurrent Neural Networks (CRNNs). Inspired by large-scale state-of-the-art speech recognition systems, we combine the strengths of convolutional layers and recurrent layers to exploit local structure and long-range context. We analyze the effect of architecture parameters, and propose training strategies to improve performance. With only ~230k parameters, our CRNN model yields acceptably low latency, and achieves 97.71% accuracy at 0.5 FA/hour for 5 dB signal-to-noise ratio., Comment: Accepted to Interspeech 2017

Published
2017

8. Deep Voice: Real-time Neural Text-to-Speech

Author

Arik, Sercan O., Chrzanowski, Mike, Coates, Adam, Diamos, Gregory, Gibiansky, Andrew, Kang, Yongguo, Li, Xian, Miller, John, Ng, Andrew, Raiman, Jonathan, Sengupta, Shubho, and Shoeybi, Mohammad

Subjects
Computer Science - Computation and Language, Computer Science - Learning, Computer Science - Neural and Evolutionary Computing, Computer Science - Sound
Abstract

We present Deep Voice, a production-quality text-to-speech system constructed entirely from deep neural networks. Deep Voice lays the groundwork for truly end-to-end neural speech synthesis. The system comprises five major building blocks: a segmentation model for locating phoneme boundaries, a grapheme-to-phoneme conversion model, a phoneme duration prediction model, a fundamental frequency prediction model, and an audio synthesis model. For the segmentation model, we propose a novel way of performing phoneme boundary detection with deep neural networks using connectionist temporal classification (CTC) loss. For the audio synthesis model, we implement a variant of WaveNet that requires fewer parameters and trains faster than the original. By using a neural network for each component, our system is simpler and more flexible than traditional text-to-speech systems, where each component requires laborious feature engineering and extensive domain expertise. Finally, we show that inference with our system can be performed faster than real time and describe optimized WaveNet inference kernels on both CPU and GPU that achieve up to 400x speedups over existing implementations., Comment: Submitted to ICML 2017

Published
2017

9. How Do Philosophical Positions Influence the Social Science Research Process? A Classification and Metaphor Analysis of Researchers' Descriptions.

Author

Coates, Adam

Subjects
*SOCIAL science research, *RESEARCH personnel, *SOCIAL status, *SOCIAL influence, *PERIODICAL articles, *ELECTRONIC textbooks
Abstract

Textbooks for social science research suggest that studies are necessarily grounded in foundational philosophies that shape the research process. However, the literature does not provide a consistent picture of how philosophical positions shape research, and few studies have investigated researchers' own ideas about this process. This study aimed to classify researchers' descriptions of how philosophical positions connect to aspects of the research process and to investigate metaphors used to describe these connections. From a sample of 1500 journal articles, 73 papers were found that directly mentioned a philosophical position. The analysis found that philosophical positions are most often connected with study design and methodology, or with concepts and theories. The connections include that philosophical positions result in, support, match and result from aspects of the research process, and that philosophical assumptions could be inherent in methodologies and theories. Metaphorically, philosophical positions were a tool, path, location, support, resource, person and seeing. These classification frameworks can be used in future research to understand how researchers conceptualize philosophical positions and how this influences their decision making. [ABSTRACT FROM AUTHOR]

Published
2024
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10. Active Learning for Speech Recognition: the Power of Gradients

Author

Huang, Jiaji, Child, Rewon, Rao, Vinay, Liu, Hairong, Satheesh, Sanjeev, and Coates, Adam

Subjects
Computer Science - Computation and Language, Computer Science - Learning, Statistics - Machine Learning
Abstract

In training speech recognition systems, labeling audio clips can be expensive, and not all data is equally valuable. Active learning aims to label only the most informative samples to reduce cost. For speech recognition, confidence scores and other likelihood-based active learning methods have been shown to be effective. Gradient-based active learning methods, however, are still not well-understood. This work investigates the Expected Gradient Length (EGL) approach in active learning for end-to-end speech recognition. We justify EGL from a variance reduction perspective, and observe that EGL's measure of informativeness picks novel samples uncorrelated with confidence scores. Experimentally, we show that EGL can reduce word errors by 11\%, or alternatively, reduce the number of samples to label by 50\%, when compared to random sampling., Comment: published as a workshop paper at NIPS 2016

Published
2016

12. Deep Speech 2: End-to-End Speech Recognition in English and Mandarin

Author

Amodei, Dario, Anubhai, Rishita, Battenberg, Eric, Case, Carl, Casper, Jared, Catanzaro, Bryan, Chen, Jingdong, Chrzanowski, Mike, Coates, Adam, Diamos, Greg, Elsen, Erich, Engel, Jesse, Fan, Linxi, Fougner, Christopher, Han, Tony, Hannun, Awni, Jun, Billy, LeGresley, Patrick, Lin, Libby, Narang, Sharan, Ng, Andrew, Ozair, Sherjil, Prenger, Ryan, Raiman, Jonathan, Satheesh, Sanjeev, Seetapun, David, Sengupta, Shubho, Wang, Yi, Wang, Zhiqian, Wang, Chong, Xiao, Bo, Yogatama, Dani, Zhan, Jun, and Zhu, Zhenyao

Subjects
Computer Science - Computation and Language
Abstract

We show that an end-to-end deep learning approach can be used to recognize either English or Mandarin Chinese speech--two vastly different languages. Because it replaces entire pipelines of hand-engineered components with neural networks, end-to-end learning allows us to handle a diverse variety of speech including noisy environments, accents and different languages. Key to our approach is our application of HPC techniques, resulting in a 7x speedup over our previous system. Because of this efficiency, experiments that previously took weeks now run in days. This enables us to iterate more quickly to identify superior architectures and algorithms. As a result, in several cases, our system is competitive with the transcription of human workers when benchmarked on standard datasets. Finally, using a technique called Batch Dispatch with GPUs in the data center, we show that our system can be inexpensively deployed in an online setting, delivering low latency when serving users at scale.

Published
2015

13. An Empirical Evaluation of Deep Learning on Highway Driving

Author

Huval, Brody, Wang, Tao, Tandon, Sameep, Kiske, Jeff, Song, Will, Pazhayampallil, Joel, Andriluka, Mykhaylo, Rajpurkar, Pranav, Migimatsu, Toki, Cheng-Yue, Royce, Mujica, Fernando, Coates, Adam, and Ng, Andrew Y.

Subjects
Computer Science - Robotics, Computer Science - Computer Vision and Pattern Recognition
Abstract

Numerous groups have applied a variety of deep learning techniques to computer vision problems in highway perception scenarios. In this paper, we presented a number of empirical evaluations of recent deep learning advances. Computer vision, combined with deep learning, has the potential to bring about a relatively inexpensive, robust solution to autonomous driving. To prepare deep learning for industry uptake and practical applications, neural networks will require large data sets that represent all possible driving environments and scenarios. We collect a large data set of highway data and apply deep learning and computer vision algorithms to problems such as car and lane detection. We show how existing convolutional neural networks (CNNs) can be used to perform lane and vehicle detection while running at frame rates required for a real-time system. Our results lend credence to the hypothesis that deep learning holds promise for autonomous driving., Comment: Added a video for lane detection

Published
2015

14. Deep Speech: Scaling up end-to-end speech recognition

Author

Hannun, Awni, Case, Carl, Casper, Jared, Catanzaro, Bryan, Diamos, Greg, Elsen, Erich, Prenger, Ryan, Satheesh, Sanjeev, Sengupta, Shubho, Coates, Adam, and Ng, Andrew Y.

Subjects
Computer Science - Computation and Language, Computer Science - Learning, Computer Science - Neural and Evolutionary Computing
Abstract

We present a state-of-the-art speech recognition system developed using end-to-end deep learning. Our architecture is significantly simpler than traditional speech systems, which rely on laboriously engineered processing pipelines; these traditional systems also tend to perform poorly when used in noisy environments. In contrast, our system does not need hand-designed components to model background noise, reverberation, or speaker variation, but instead directly learns a function that is robust to such effects. We do not need a phoneme dictionary, nor even the concept of a "phoneme." Key to our approach is a well-optimized RNN training system that uses multiple GPUs, as well as a set of novel data synthesis techniques that allow us to efficiently obtain a large amount of varied data for training. Our system, called Deep Speech, outperforms previously published results on the widely studied Switchboard Hub5'00, achieving 16.0% error on the full test set. Deep Speech also handles challenging noisy environments better than widely used, state-of-the-art commercial speech systems.

Published
2014

16. Deep learning for class-generic object detection

Author

Huval, Brody, Coates, Adam, and Ng, Andrew

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Learning, Computer Science - Neural and Evolutionary Computing
Abstract

We investigate the use of deep neural networks for the novel task of class generic object detection. We show that neural networks originally designed for image recognition can be trained to detect objects within images, regardless of their class, including objects for which no bounding box labels have been provided. In addition, we show that bounding box labels yield a 1% performance increase on the ImageNet recognition challenge.

Published
2013

18. Methods for ultra-broadband correlator development focusing on high-speed digital sampling techniques

Author

Coates, Adam Ross and Taylor, Angela

Subjects
522, Astrophysics, radio astronomy, correlator, digital sampling
Abstract

In radio astronomy, a key limiting factor to observations made is the available bandwidth of the system. This thesis looks at two different approaches to building ultra-broadband correlators for use in radio astronomy. The first was a 2-20GHz double-sideband complex analogue correlator that was constructed before the work of this thesis. Characterisation tests are performed and a basic calibration is attempted. Both these sets of experiments show good results, with the basic calibration successfully being able to compensate for gain difference between the lags over a reduced bandwidth range used in the testing. The second approach was the investigation into different techniques for high-speed digital sampling, capable of providing equivalent bandwidths to the analogue system. The use of FPGA high-speed serial interfaces as direct 1-bit 3.125 GS/s samplers was investigated. Single-frequency sampling showed that a signal-to-noise ratio close to the theoretical maximum across the band was achieved (≈ 0.8 effective bits). Techniques were also identified to use multiple transceivers to generate a single interleaved stream at higher effective sampling rate. Two different methods were also explored for producing greater-than 1-bit sampling. A hysteresis approach was shown not to produce the desired results and a reference based sampler in the end was adopted. Finally, the interleaving and multi-bit techniques were combined to generate a single 1.5-bit 6.25 GS/s sampler. This was seen to have reduced signal-to-noise compared to the expected values. This was believed to be caused by the poor method of RF signal injection causing cross-talk between the channels and large amounts of loss. As a comparison to the direct sampling method, an external 1-bit high-speed Hittite comparator was also examined. The single-frequency experiment was repeated with a slightly higher signal-to-noise ratio found compared to the direct sampling method. This was again believed to be due to the RF environments used. From the sampling setups a four-input, six-baseline lag correlator was constructed using the direct sampling method. The entire correlator, as well as the sampling transceivers, was incorporated into a single Xilinx Virtex 5 FPGA. This was shown to have the expected response to single-frequency, broadband and noise signals. The thesis concludes with a characterisation of the RF devices used throughout the testing procedures. Several new devices were developed through the course of the experiments with the designs being documented. All the necessary components to construct IF chains for both the analogue and digital correlators described are present. This leads to simulations being made of complete IF chains, with the expected responses shown.

Published
2013

22. Learning Feature Representations with K-Means

Author

Coates, Adam, Ng, Andrew Y., Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Montavon, Grégoire, editor, Orr, Geneviève B., editor, and Müller, Klaus-Robert, editor

Published
2012
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28. Autonomous helicopter tracking and localization using a self-surveying camera array

Author

Matsuoka, Masayoshi, Chen, Alan, Singh, Surya P.N., Coates, Adam, Ng, Andrew Y., and Thrun, Sebastian

Subjects
Aerial cameras -- Product development -- Research, Helicopters -- Control systems, Robots -- Control systems, Computers and office automation industries, Engineering and manufacturing industries, Research, Product development
Abstract

Abstract A Self-surveying Camera Array (SSCA) is a vision-based local-area positioning system consisting of multiple ground-deployed cameras that are capable of self-surveying their extrinsic parameters while tracking and localizing a [...]

Published
2007

29. Algorithm Evaluation

Author

Kakas, Antonis C., primary, Cohn, David, additional, Dasgupta, Sanjoy, additional, Barto, Andrew G., additional, Carpenter, Gail A., additional, Grossberg, Stephen, additional, Webb, Geoffrey I., additional, Dorigo, Marco, additional, Birattari, Mauro, additional, Toivonen, Hannu, additional, Timmis, Jon, additional, Branke, Jürgen, additional, Strehl, Alexander L., additional, Drummond, Chris, additional, Coates, Adam, additional, Abbeel, Pieter, additional, Ng, Andrew Y., additional, Zheng, Fei, additional, and Tadepalli, Prasad, additional

Published
2011
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30. Artificial Neural Networks

Author

Kakas, Antonis C., primary, Cohn, David, additional, Dasgupta, Sanjoy, additional, Barto, Andrew G., additional, Carpenter, Gail A., additional, Grossberg, Stephen, additional, Webb, Geoffrey I., additional, Dorigo, Marco, additional, Birattari, Mauro, additional, Toivonen, Hannu, additional, Timmis, Jon, additional, Branke, Jürgen, additional, Strehl, Alexander L., additional, Drummond, Chris, additional, Coates, Adam, additional, Abbeel, Pieter, additional, Ng, Andrew Y., additional, Zheng, Fei, additional, and Tadepalli, Prasad, additional

Published
2011
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31. Adaptive Real-Time Dynamic Programming

Author

Kakas, Antonis C., primary, Cohn, David, additional, Dasgupta, Sanjoy, additional, Barto, Andrew G., additional, Carpenter, Gail A., additional, Grossberg, Stephen, additional, Webb, Geoffrey I., additional, Dorigo, Marco, additional, Birattari, Mauro, additional, Toivonen, Hannu, additional, Timmis, Jon, additional, Branke, Jürgen, additional, Strehl, Alexander L., additional, Drummond, Chris, additional, Coates, Adam, additional, Abbeel, Pieter, additional, Ng, Andrew Y., additional, Zheng, Fei, additional, and Tadepalli, Prasad, additional

Published
2011
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32. Average-Cost Optimization

Author

Kakas, Antonis C., primary, Cohn, David, additional, Dasgupta, Sanjoy, additional, Barto, Andrew G., additional, Carpenter, Gail A., additional, Grossberg, Stephen, additional, Webb, Geoffrey I., additional, Dorigo, Marco, additional, Birattari, Mauro, additional, Toivonen, Hannu, additional, Timmis, Jon, additional, Branke, Jürgen, additional, Strehl, Alexander L., additional, Drummond, Chris, additional, Coates, Adam, additional, Abbeel, Pieter, additional, Ng, Andrew Y., additional, Zheng, Fei, additional, and Tadepalli, Prasad, additional

Published
2011
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33. AODE

Author

Kakas, Antonis C., primary, Cohn, David, additional, Dasgupta, Sanjoy, additional, Barto, Andrew G., additional, Carpenter, Gail A., additional, Grossberg, Stephen, additional, Webb, Geoffrey I., additional, Dorigo, Marco, additional, Birattari, Mauro, additional, Toivonen, Hannu, additional, Timmis, Jon, additional, Branke, Jürgen, additional, Strehl, Alexander L., additional, Drummond, Chris, additional, Coates, Adam, additional, Abbeel, Pieter, additional, Ng, Andrew Y., additional, Zheng, Fei, additional, and Tadepalli, Prasad, additional

Published
2011
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34. Average-Payoff Reinforcement Learning

Author

Kakas, Antonis C., primary, Cohn, David, additional, Dasgupta, Sanjoy, additional, Barto, Andrew G., additional, Carpenter, Gail A., additional, Grossberg, Stephen, additional, Webb, Geoffrey I., additional, Dorigo, Marco, additional, Birattari, Mauro, additional, Toivonen, Hannu, additional, Timmis, Jon, additional, Branke, Jürgen, additional, Strehl, Alexander L., additional, Drummond, Chris, additional, Coates, Adam, additional, Abbeel, Pieter, additional, Ng, Andrew Y., additional, Zheng, Fei, additional, and Tadepalli, Prasad, additional

Published
2011
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35. Agent-Based Modeling and Simulation

Author

Kakas, Antonis C., primary, Cohn, David, additional, Dasgupta, Sanjoy, additional, Barto, Andrew G., additional, Carpenter, Gail A., additional, Grossberg, Stephen, additional, Webb, Geoffrey I., additional, Dorigo, Marco, additional, Birattari, Mauro, additional, Toivonen, Hannu, additional, Timmis, Jon, additional, Branke, Jürgen, additional, Strehl, Alexander L., additional, Drummond, Chris, additional, Coates, Adam, additional, Abbeel, Pieter, additional, Ng, Andrew Y., additional, Zheng, Fei, additional, and Tadepalli, Prasad, additional

Published
2011
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36. Agent-Based Simulation Models

Author

Kakas, Antonis C., primary, Cohn, David, additional, Dasgupta, Sanjoy, additional, Barto, Andrew G., additional, Carpenter, Gail A., additional, Grossberg, Stephen, additional, Webb, Geoffrey I., additional, Dorigo, Marco, additional, Birattari, Mauro, additional, Toivonen, Hannu, additional, Timmis, Jon, additional, Branke, Jürgen, additional, Strehl, Alexander L., additional, Drummond, Chris, additional, Coates, Adam, additional, Abbeel, Pieter, additional, Ng, Andrew Y., additional, Zheng, Fei, additional, and Tadepalli, Prasad, additional

Published
2011
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37. Analytical Learning

Author

Kakas, Antonis C., primary, Cohn, David, additional, Dasgupta, Sanjoy, additional, Barto, Andrew G., additional, Carpenter, Gail A., additional, Grossberg, Stephen, additional, Webb, Geoffrey I., additional, Dorigo, Marco, additional, Birattari, Mauro, additional, Toivonen, Hannu, additional, Timmis, Jon, additional, Branke, Jürgen, additional, Strehl, Alexander L., additional, Drummond, Chris, additional, Coates, Adam, additional, Abbeel, Pieter, additional, Ng, Andrew Y., additional, Zheng, Fei, additional, and Tadepalli, Prasad, additional

Published
2011
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38. Analysis of Text

Author

Kakas, Antonis C., primary, Cohn, David, additional, Dasgupta, Sanjoy, additional, Barto, Andrew G., additional, Carpenter, Gail A., additional, Grossberg, Stephen, additional, Webb, Geoffrey I., additional, Dorigo, Marco, additional, Birattari, Mauro, additional, Toivonen, Hannu, additional, Timmis, Jon, additional, Branke, Jürgen, additional, Strehl, Alexander L., additional, Drummond, Chris, additional, Coates, Adam, additional, Abbeel, Pieter, additional, Ng, Andrew Y., additional, Zheng, Fei, additional, and Tadepalli, Prasad, additional

Published
2011
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39. Adaptive Control Processes

Author

Kakas, Antonis C., primary, Cohn, David, additional, Dasgupta, Sanjoy, additional, Barto, Andrew G., additional, Carpenter, Gail A., additional, Grossberg, Stephen, additional, Webb, Geoffrey I., additional, Dorigo, Marco, additional, Birattari, Mauro, additional, Toivonen, Hannu, additional, Timmis, Jon, additional, Branke, Jürgen, additional, Strehl, Alexander L., additional, Drummond, Chris, additional, Coates, Adam, additional, Abbeel, Pieter, additional, Ng, Andrew Y., additional, Zheng, Fei, additional, and Tadepalli, Prasad, additional

Published
2011
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40. Approximate Dynamic Programming

Author

Kakas, Antonis C., primary, Cohn, David, additional, Dasgupta, Sanjoy, additional, Barto, Andrew G., additional, Carpenter, Gail A., additional, Grossberg, Stephen, additional, Webb, Geoffrey I., additional, Dorigo, Marco, additional, Birattari, Mauro, additional, Toivonen, Hannu, additional, Timmis, Jon, additional, Branke, Jürgen, additional, Strehl, Alexander L., additional, Drummond, Chris, additional, Coates, Adam, additional, Abbeel, Pieter, additional, Ng, Andrew Y., additional, Zheng, Fei, additional, and Tadepalli, Prasad, additional

Published
2011
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41. Analogical Reasoning

Author

Kakas, Antonis C., primary, Cohn, David, additional, Dasgupta, Sanjoy, additional, Barto, Andrew G., additional, Carpenter, Gail A., additional, Grossberg, Stephen, additional, Webb, Geoffrey I., additional, Dorigo, Marco, additional, Birattari, Mauro, additional, Toivonen, Hannu, additional, Timmis, Jon, additional, Branke, Jürgen, additional, Strehl, Alexander L., additional, Drummond, Chris, additional, Coates, Adam, additional, Abbeel, Pieter, additional, Ng, Andrew Y., additional, Zheng, Fei, additional, and Tadepalli, Prasad, additional

Published
2011
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42. Average-Cost Neuro-Dynamic Programming

Author

Kakas, Antonis C., primary, Cohn, David, additional, Dasgupta, Sanjoy, additional, Barto, Andrew G., additional, Carpenter, Gail A., additional, Grossberg, Stephen, additional, Webb, Geoffrey I., additional, Dorigo, Marco, additional, Birattari, Mauro, additional, Toivonen, Hannu, additional, Timmis, Jon, additional, Branke, Jürgen, additional, Strehl, Alexander L., additional, Drummond, Chris, additional, Coates, Adam, additional, Abbeel, Pieter, additional, Ng, Andrew Y., additional, Zheng, Fei, additional, and Tadepalli, Prasad, additional

Published
2011
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43. Average-Reward Reinforcement Learning

Author

Kakas, Antonis C., primary, Cohn, David, additional, Dasgupta, Sanjoy, additional, Barto, Andrew G., additional, Carpenter, Gail A., additional, Grossberg, Stephen, additional, Webb, Geoffrey I., additional, Dorigo, Marco, additional, Birattari, Mauro, additional, Toivonen, Hannu, additional, Timmis, Jon, additional, Branke, Jürgen, additional, Strehl, Alexander L., additional, Drummond, Chris, additional, Coates, Adam, additional, Abbeel, Pieter, additional, Ng, Andrew Y., additional, Zheng, Fei, additional, and Tadepalli, Prasad, additional

Published
2011
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44. Anytime Algorithm

Author

Kakas, Antonis C., primary, Cohn, David, additional, Dasgupta, Sanjoy, additional, Barto, Andrew G., additional, Carpenter, Gail A., additional, Grossberg, Stephen, additional, Webb, Geoffrey I., additional, Dorigo, Marco, additional, Birattari, Mauro, additional, Toivonen, Hannu, additional, Timmis, Jon, additional, Branke, Jürgen, additional, Strehl, Alexander L., additional, Drummond, Chris, additional, Coates, Adam, additional, Abbeel, Pieter, additional, Ng, Andrew Y., additional, Zheng, Fei, additional, and Tadepalli, Prasad, additional

Published
2011
Full Text
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45. Artificial Immune Systems

Author

Kakas, Antonis C., primary, Cohn, David, additional, Dasgupta, Sanjoy, additional, Barto, Andrew G., additional, Carpenter, Gail A., additional, Grossberg, Stephen, additional, Webb, Geoffrey I., additional, Dorigo, Marco, additional, Birattari, Mauro, additional, Toivonen, Hannu, additional, Timmis, Jon, additional, Branke, Jürgen, additional, Strehl, Alexander L., additional, Drummond, Chris, additional, Coates, Adam, additional, Abbeel, Pieter, additional, Ng, Andrew Y., additional, Zheng, Fei, additional, and Tadepalli, Prasad, additional

Published
2011
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46. Associative Reinforcement Learning

Author

Kakas, Antonis C., primary, Cohn, David, additional, Dasgupta, Sanjoy, additional, Barto, Andrew G., additional, Carpenter, Gail A., additional, Grossberg, Stephen, additional, Webb, Geoffrey I., additional, Dorigo, Marco, additional, Birattari, Mauro, additional, Toivonen, Hannu, additional, Timmis, Jon, additional, Branke, Jürgen, additional, Strehl, Alexander L., additional, Drummond, Chris, additional, Coates, Adam, additional, Abbeel, Pieter, additional, Ng, Andrew Y., additional, Zheng, Fei, additional, and Tadepalli, Prasad, additional

Published
2011
Full Text
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47. Autonomous Helicopter Flight Using Reinforcement Learning

Author

Kakas, Antonis C., primary, Cohn, David, additional, Dasgupta, Sanjoy, additional, Barto, Andrew G., additional, Carpenter, Gail A., additional, Grossberg, Stephen, additional, Webb, Geoffrey I., additional, Dorigo, Marco, additional, Birattari, Mauro, additional, Toivonen, Hannu, additional, Timmis, Jon, additional, Branke, Jürgen, additional, Strehl, Alexander L., additional, Drummond, Chris, additional, Coates, Adam, additional, Abbeel, Pieter, additional, Ng, Andrew Y., additional, Zheng, Fei, additional, and Tadepalli, Prasad, additional

Published
2011
Full Text
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48. Attribute Selection

Author

Kakas, Antonis C., primary, Cohn, David, additional, Dasgupta, Sanjoy, additional, Barto, Andrew G., additional, Carpenter, Gail A., additional, Grossberg, Stephen, additional, Webb, Geoffrey I., additional, Dorigo, Marco, additional, Birattari, Mauro, additional, Toivonen, Hannu, additional, Timmis, Jon, additional, Branke, Jürgen, additional, Strehl, Alexander L., additional, Drummond, Chris, additional, Coates, Adam, additional, Abbeel, Pieter, additional, Ng, Andrew Y., additional, Zheng, Fei, additional, and Tadepalli, Prasad, additional

Published
2011
Full Text
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49. Averaged One-Dependence Estimators

Author

Kakas, Antonis C., primary, Cohn, David, additional, Dasgupta, Sanjoy, additional, Barto, Andrew G., additional, Carpenter, Gail A., additional, Grossberg, Stephen, additional, Webb, Geoffrey I., additional, Dorigo, Marco, additional, Birattari, Mauro, additional, Toivonen, Hannu, additional, Timmis, Jon, additional, Branke, Jürgen, additional, Strehl, Alexander L., additional, Drummond, Chris, additional, Coates, Adam, additional, Abbeel, Pieter, additional, Ng, Andrew Y., additional, Zheng, Fei, additional, and Tadepalli, Prasad, additional

Published
2011
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50. Associative Bandit Problem

Author

Kakas, Antonis C., primary, Cohn, David, additional, Dasgupta, Sanjoy, additional, Barto, Andrew G., additional, Carpenter, Gail A., additional, Grossberg, Stephen, additional, Webb, Geoffrey I., additional, Dorigo, Marco, additional, Birattari, Mauro, additional, Toivonen, Hannu, additional, Timmis, Jon, additional, Branke, Jürgen, additional, Strehl, Alexander L., additional, Drummond, Chris, additional, Coates, Adam, additional, Abbeel, Pieter, additional, Ng, Andrew Y., additional, Zheng, Fei, additional, and Tadepalli, Prasad, additional

Published
2011
Full Text
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