IBM Research is a pioneer across many aspects of AI. At the 6th International Conference on Learning Representations (ICLR 2018), our team will share recent discoveries in learning data representations, techniques that are key to the success of machine learning algorithms. These techniques enable machine learning systems to automatically discover how to represent raw data for subsequent analysis. Learning data representations is an important learning task that powers computer vision, speech recognition, natural language processing, drug design, and other advances in AI. At ICLR 2018, IBM Research will present technical papers on adversarial learning, self-organizing networks for multi-task learning, open-domain question answering, disentanglement of representations, reinforcement learning, and deep learning for graphical data.
In one paper at ICLR 2018, IBM Research will be introducing the concept of a “routing network,” a kind of self-organizing neural network consisting of two components: a router and a set of function blocks. Given an input, the router makes a routing decision, choosing a function block to apply and passing the output back to the router recursively. This allows similar tasks to share layers, while enabling differing tasks to choose distinct layers. Routing networks perform much better than alternative approaches on a set of multi-task learning benchmarks.
IBM Research is a gold sponsor of ICLR 18. We will be at the conference expo at booth #500, and below are details on the papers IBM Research will be presenting.
Accepted papers at ICLR 2018
Tsui-Wei Weng, Huan Zhang, Pin-Yu Chen, Jinfeng Yi, Dong Su, Yupeng Gao, Cho-Jui Hsieh, Luca Daniel
Youssef Mroueh, Chun-Liang Li, Tom Sercu, Anant Raj, Yu Cheng
Clemens Rosenbaum, Tim Klinger, Matthew Riemer
Shuohang Wang, Mo Yu, Jing Jiang, Wei Zhang, Xiaoxiao Guo, Shiyu Chang, Zhiguo Wang, Tim Klinger, Gerald Tesauro, Murray Campbell
Abhishek Kumar, Prasanna Sattigeri, Avinash Balakrishnan
Marlos C. Machado, Clemens Rosenbaum, Xiaoxiao Guo, Miao Liu, Gerald Tesauro, Murray Campbell
Jie Chen, Tengfei Ma, Cao Xiao
via IBM Blog Research https://ibm.co/2cYaEHU
April 30, 2018 at 02:03PM