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/
|11/16||Dr Ernest Kamavuako||Sub-chronic recordings for myoelectric control of prostheses: paving the way for a better understanding of the language of the brain|
|10/16||Prof Alan Bundy||Reformation: a generic algorithm for repairing faulty representations|
|06/16||Dr Luis Lamb||Learning and Reasoning in AI and Cognitive Computation|
|06/16||Dr Lucian Busoniu||Planning Methods for Near-Optimal Nonlinear Control|
|03/16||Dr Greg Wayne||Differentiable Neural Computers for Memory-Based Control|
|09/15||Dr Carlos Eduardo Thomaz||A 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|
|09/12||Alan Perotti (University of Turin)||Neural-Symbolic Rule-based Monitoring|
|05/12||Greg Slabaugh||Medical Image Processing|
|03/12||Manoel Franca||Introduction to Inductive Logic Programming|
|02/12||Artur Garcez||Neural-Symbolic Systems for Cognitive Reasoning|
- 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.