Efficient Methods for Deep Neural Networks

Deep Neural Networks have been revolutionizing several application domains in artificial intelligence: Computer Vision, Speech Recognition and Natural Language Processing. Concurrent to the recent progress in deep learning, significant progress has been happening in virtual reality, augmented reality, and smart wearable devices. These advances create unprecedented opportunities for researchers to tackle fundamental challenges in deploying deep learning systems to portable devices with limited resources (e.g. Memory, CPU, Energy, Bandwidth). Efficient methods in deep learning can have crucial impacts in using distributed systems, embedded devices, and FPGA for several AI tasks. Achieving these goals calls for ground-breaking innovations on many fronts: learning, optimization, computer architecture, data compression, indexing, and hardware design.

This workshop is sponsored by Allen Institute for Artificial Intelligence (AI2). We offer partial travel grant and registration for limited number of people participating in the workshop.

The goal of this workshop is providing a venue for researchers interested in developing efficient techniques for deep neural networks to present new work, exchange ideas, and build connections. The workshop will feature keynotes and invited talks from prominent researchers as well as a poster session that fosters in depth discussion. Further, in a discussion panel the experts discuss about the possible approaches (hardware, software, algorithm, ...) toward designing efficient methods in deep learning.

We invite submissions of short papers and extended abstracts related to the following topics in the context of efficient methods in deep learning:

-Network compression
-Quantized neural networks (e.g. Binary neural networks)
-Hardware accelerator for neural networks
-Training and inference with low-precision operations.
-Real-time applications in deep neural networks (e.g. Object detection, Image segmentation, Online language translation, ...)
-Distributed training/inference of deep neural networks
-Fast optimization methods for neural networks