Machine Learning
  1. Projects
  2. PhD Topics
  3. Resources
  4. Seminars / Reading Group
  5. Data Bites
Machine Learning

Seminars / Reading Group

The Research Centre for Machine Learning has regular reading group meetings and seminars, taking place at Northampton Square, College Building. The group also maintains an external mailing list for announcing seminars; you can subscribe yourself to the mailing list at: http://maillists.city.ac.uk/mailman/listinfo/ml-seminars/

Seminars

11/16Dr Ernest KamavuakoSub-chronic recordings for myoelectric control of prostheses: paving the way for a better understanding of the language of the brain
10/16Prof Alan BundyReformation: a generic algorithm for repairing faulty representations
06/16Dr Luis LambLearning and Reasoning in AI and Cognitive Computation
06/16Dr Lucian BusoniuPlanning Methods for Near-Optimal Nonlinear Control
03/16Dr Greg WayneDifferentiable Neural Computers for Memory-Based Control
09/15Dr Carlos Eduardo ThomazA Photo-Realistic Generator of Most Expressive and Discriminant Changes in 2D Face Images
04/15 Dr Luke Dickens (UCL)
Part 1: Efficient Knowledge Aquisition in Crowdsourcing ; Part 2: The Human Gamma Project
02/15 Dr Nikos Deligiannis (Vrije Universiteit Brussel, Belgium)
12/14 Dr Régis Riveret (Imperial College London) Probabilistic Abstract Argumentation and Boltzmann Machines
09/14 Dan Stowell (Queen Mary University of London) Machine learning for bird sounds: at large scale and fine detail
06/14 Sepehr Jalali Inspirations from human visual cortex for image classification
05/14 Suresh Veluru Correlated Community Estimation Models over a Set of Names
04/14 Siddharth Sigtia (Queen Mary University of London) Improved Music Feature Learning with Deep Neural Networks
04/14 Hazrat Ali Hybrid Features Combination for Audio Data Classification
03/14 Emmanouil Benetos and Srikanth Cherla Latent Dirichlet Allocation - Probabilistic Topic Models
11/13 Peter Smith Mutation Melts The Landscape: The Visualisation of Evolutionary Processes
10/13 Tarek Besold (University of Osnabrück) Analogy and AGI: Towards a Framework Supporting Human-like Reasoning
10/13 Srikanth Cherla A Distributed Model for Multiple-viewpoint Melodic Prediction
05/13 Muhammad Asad Hand gesture recognition using Kinect
03/13 Roland Badeau (Télécom ParisTech) Probabilistic Modelling of Time-frequency Representations with Application to Music Signals
03/13 Emmanouil Benetos Non-negative Matrix Factorization: Algorithms, Extensions, and Applications
11/12 Son Tran Logic Extraction from Deep Belief Networks
10/12 Daniel Wolff Culture-Aware Music Information Retrieval: Modelling Music Similarity
09/12Alan Perotti (University of Turin) Neural-Symbolic Rule-based Monitoring
05/12Greg Slabaugh Medical Image Processing
03/12Manoel Franca Introduction to Inductive Logic Programming
02/12Artur Garcez Neural-Symbolic Systems for Cognitive Reasoning

Reading group

  • Rahhal et al., "Deep Learning Approach for Active Classification of Electrocardiogram Signals" Information Sciences 2016.
  • Suk et al., "State-space Model with Deep Learning for Functional Dynamics Estimation in Resting-state fMRI," NeuroImage 2016.
  • Donadello et al., "Integration of Numeric and Symbolic Information for Semantic Image Segmentation".
  • G. Hinton, O. Vinyals and J. Dean, Distilling Knowledge in a Neural Network, In Deep Learning and Representation Learning Workshop (NIPS), March 2015.
  • Noh et al., "Learning Deconvolution Network for Semantic Segmentation," ICCV 2015.
  • LeCun et al., "Deep Learning," Nature 2015.
  • Long et al., "Fully Convolutional Networks for Semantic Segmentation," CVPR 2015.
  • Kontschieder et al., "Deep Neural Decision Forests," ICCV 2015.
  • S. Schulter, Alternating decision forests. Computer Vision and Pattern Recognition (CVPR), IEEE Conference, February 2015.
  • Zheng et al., "Conditional Random Fields as Recurrent Neural Networks," ICCV 2015.
  • Fanello et al., "Filter Forests for Learning Data-Dependent Convolutional Kernels," CVPR 2014.
  • Collins et al., "Hybrid Stochastic / Deterministic Optimization for Tracking Sports Players and Pedestrians," ECCV 2014.
  • P. Norvig, On Chomsky and the Two Cultures of Statistical Learning.
  • S. Tran and A. Garcez, Logic Extraction from Deep Belief Networks, ICML Workshop, July 2012.
  • D. Wolff et al., A Systematic Comparison of Music Similarity Adaptation Approaches, ISMIR 2012.
  • S. Barry Cooper, Turing's Titanic Machine, CACM 2012.
  • D. Blei. Probabilistic topic models. Communications of the ACM, 55(4):77-84, 2012.
  • M. Hoffmann and R. Pfeifer, The Implications of Embodiment for Behavior and Cognition: Animal and Robotic Case Studies, arXiv:1202.0440, 2011.
  • P. Thagard and T. C. Stewart, The AHA! Experience: Creativity through Emergent Binding in Neural Networks, Cognitive Science, 2011.
  • Lankton et al., "Soft Plaque Detection and Automatic Vessel Segmentation," MICCAI 2009.
  • A. Yu and J. Cohen, Sequential effects: Superstition or Rational Behavior, NIPS 2008.
  • G. Hinton, Learning multiple layers of representation, Trends in Cognitive Science, 2007.
  • J. De Ruiter, Strong AI and the Chinese Room Argument: Four views, 2006.
  • R. Collobert and S. Bengio, Links between Perceptrons, MLPs and SVMs, 2004.
  • Nummairo et al., "An Adaptive Color-based Particle Filter," IVC 2003.
  • L. Breiman. Statistical Modeling: The Two Cultures. Statistical Science 16(3):199-231, 2001.
  • Sederberg et al., "Non-Uniform Recursive Subdivision Surfaces," SIGGRAPH 1998.

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