Updates on the activities around deep learning are discussed in a specific distribution list. If you would like to be included, please contact aurelio (dot) ruiz upf (dot) edu

  • Deep Learning Study Group
  • Deep Learning Seminars. Several seminars are organised around Machine Learning in general, and Deep Learning in specific, within the DTIC Seminars, hosting activities of the BCN Machine Learning Study Group or organised ad-hoc. Unless explicitly stated, they are open to anybody interested in joining, and often recorded.

 

Past seminars and associated materials

Deep Learning Study Group

Deep Learning and Data Science Seminars

  • 15/06/2017 12:30h, room 55.410 (DTIC-UPF Invited Seminars)

Generative Models of Drawing and Sound. Douglas Eck, Google

I'll give an overview talk about Magenta, a project investigating music and art generation using deep learning and reinforcement learning. I'll discuss some of the goals of Magenta and how it fits into the general trend of AI moving into our daily lives. I'll talk about two specific recent projects. First I'll discuss our research on Teaching Machines to Draw with SketchRNN, a LSTM recurrent neural network able to construct stroke-based drawings of common objects. SketchRNN is trained on thousands of crude human-drawn images representing hundreds of classes. Second I'll talk about NSynth, a deep neural network that learns to make new musical instruments via a WaveNet-style temporal autoencoder. Trained on hundreds of thousands of musical notes, the model learns to generalize in the space of musical timbres, allowing musicians to explore new sonic spaces such as sounds that exist somewhere between a bass guitar and a flute. This will be a high-level overview talk with no need for prior knowledge of machine learning models such as LSTM or WaveNet.

Short bio

Doug is a Research Scientist at Google leading Magenta, a Google Brain project working to generate music, video, image and text using deep learning and reinforcement learning. A main goal of Magenta is to better understanding how AI can enable artists and musicians to express themselves in innovative new ways. Before Magenta, Doug led the Google Play Music search and recommendation team. From 2003 to 2010 Doug was an Associate Professor in Computer Science at the University of Montreal's MILA Machine Learning lab, where he worked on expressive music performance and automatic tagging of music audio.

  • 15/06/2017 13:30h. Melià Sky Hotel. 22@ Network Agora (lunch seminars)

Machine learning, predict what you want. Ricardo Baeza, CTO CTENT and Prof. DTIC-UPF

  • 22/03/2017 19:00h, room 52.033 (BCN ML Study Group)

H20 Deep Water - Making Deep Learning Accessible to Everyone. Jo-fai, data scientist at H20.ai

More than words: Machine learning applied to Marketing. Cristina Aranda. Intelygenz & MujeresTech.

Machine learning in industry: textbooks vs real life. José A. Rodríguez. BBVA Big Data Research

  • 02/11/2016 19:00h (BCN ML study group). Room 52.023

What matters and what doesn't? The machine learning challenges in online adds. Nicolas le Roux, Criteo.

Data and Algorithmic Bias in the Web. Ricardo Baeza-Yates (NTENT & DTIC-UPF Web Research Group)

  • 15/06/2016, 19:00h (BCN ML study group)

Big Crisis Data - an exciting frontier for applied computing. Carlos Castillo, Director of Research for Data Mining, Eurecat (Video)

  • 13/06/2016, 11:00h (room 55.410). Internal seminar

Experimenting with Musically Motivated Convolutional Neural Networks General communication. Jordi Pons, Music Technology Group, DTIC-UPF

  • 24/05/2016, 19:00h (BCN ML study group)

The Good, the Bad and the Ugly in Deep Learning. Joan Bruna, UC Berkeley (Video and slides)

  • 10/05/2016, 15:30h (DTIC Seminar)

Deep Learning for Transition-based Natural Language Processing. Miguel Ballesteros, Natural Language Processing (TALN) group, DTIC-UPF

  • 29/04/2016, 11:00h (GRIB at CEXS-UPF Seminar)

Incremental Unsupervised Training of Deep Architectures. Davide Maltoni, University of Bologna

  • 25/04/2016, 15:30h (room 55.230, internal DTIC-UPF seminar)

Deep learning for learning optimal controllers. Vicenç Gómez, Artificial Intelligence group, DTIC-UPF

Suggested reading:

Real-Time Stochastic Optimal Control for Multi-agent Quadrotor Systems
http://arxiv.org/abs/1502.04548

  • 03/03/2016, 15:30h (DTIC Seminars)

Deep Learning. Alexandros Karatzoglou, Telefónica Research

  • 26/02/2016, 11:00h (GRIB at CEXS-UPF Seminar)

Deep Neural Networks and Reinforcement Learning for Building Intelligent Machines. Silvia Chiappa, Google Deep Mind, UK.

  • 25/02/2016, 19:00h (BCN ML Study Group)

Lessons Learned from Building Real-Life ML Systems. Xavier Amatriain, Quora. (Video)