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

Evaluating the Robustness of Neural Networks: An Extreme Value Theory Approach

Tsui-Wei Weng, Huan Zhang, Pin-Yu Chen, Jinfeng Yi, Dong Su, Yupeng Gao, Cho-Jui Hsieh, Luca Daniel

Sobolev GAN

Youssef Mroueh, Chun-Liang Li, Tom Sercu, Anant Raj, Yu Cheng

Routing Networks: Adaptive Selection of Non-linear Functions for Multi-Task Learning

Clemens Rosenbaum, Tim Klinger, Matthew Riemer

Evidence Aggregation for Answer Re-Ranking in Open-Domain Question Answering

Shuohang Wang, Mo Yu, Jing Jiang, Wei Zhang, Xiaoxiao Guo, Shiyu Chang, Zhiguo Wang, Tim Klinger, Gerald Tesauro, Murray Campbell

Variational Inference of Disentangled Latent Concepts from Unlabeled Observations

Abhishek Kumar, Prasanna Sattigeri, Avinash Balakrishnan

Eigenoption Discovery through the Deep Successor Representation

Marlos C. Machado, Clemens Rosenbaum, Xiaoxiao Guo, Miao Liu, Gerald Tesauro, Murray Campbell

FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling

Jie Chen, Tengfei Ma, Cao Xiao

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April 30, 2018 at 02:03PM