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Gokhan Tur, Piero Molino, Yi-Chia Wang, Chandra Khatri, Mahdi Namazifar, Alexandros Papangelis - 2020
As the field of Spoken Dialogue Systems and Conversational AI grows, so does the need for tools and environments that abstract away implementation details in order to expedite the development process, lower the barrier of entry to the field, and offer a common test-bed for new ideas. In this paper, we present Plato, a flexible Conversational AI p...
Ji-Rong Wen, Tao Xiang, Zhiwu Lu, Jiechao Guan - 2020
To recognize the unseen classes with only few samples, few-shot learning (FSL) uses prior knowledge learned from the seen classes. A major challenge for FSL is that the distribution of the unseen classes is different from that of those seen, resulting in poor generalization even when a model is meta-trained on the seen classes. This class-differe...
Lee Redden, Ramitha Sundar, Kirill Polzounov - 2020
We provide a simple hardware wrapper around the Quanser's hardware-in-the-loop software development kit (HIL SDK) to allow for easy development of new Quanser hardware. To connect to the hardware we use a module written in Cython. The internal QuanserWrapper class handles most of the difficult aspects of interacting with hardware, including the t...
Eytan Bakshy, Akshara Rai, Roberto Calandra, Benjamin Letham - 2020
Bayesian optimization (BO) is a popular approach to optimize expensive-to-evaluate black-box functions. A significant challenge in BO is to scale to high-dimensional parameter spaces while retaining sample efficiency. A solution considered in existing literature is to embed the high-dimensional space in a lower-dimensional manifold, often via a r...
Kiho Hong, Taeryun Won, Jungkyu Lee - 2020
Recent studies in image classification have demonstrated a variety of techniques for improving the performance of Convolutional Neural Networks (CNNs). However, attempts to combine existing techniques to create a practical model are still uncommon. In this study, we carry out extensive experiments to validate that carefully assembling these techn...
Fatih Porikli, Ling Shao, Jianbing Shen, Chao Ma, Wenguan Wang, Xiankai Lu - 2020
We introduce a novel network, called CO-attention Siamese Network (COSNet), to address the unsupervised video object segmentation task from a holistic view. We emphasize the importance of inherent correlation among video frames and incorporate a global co-attention mechanism to improve further the state-of-the-art deep learning based solutions th...
Wang-Chiew Tan, Xiaolan Wang, Yuliang Li, Zhengjie Miao - 2020
Online services are interested in solutions to opinion mining, which is the problem of extracting aspects, opinions, and sentiments from text. One method to mine opinions is to leverage the recent success of pre-trained language models which can be fine-tuned to obtain high-quality extractions from reviews. However, fine-tuning language models st...
Yonghong Tian, Yongjian Wu, Baochang Zhang, Yuxin Zhang, Rongrong Ji, Mingbao Lin - 2020
Channel pruning is among the predominant approaches to compress deep neural networks. To this end, most existing pruning methods focus on selecting channels (filters) by importance/optimization or regularization based on rule-of-thumb designs, which defects in sub-optimal pruning. In this paper, we propose a new channel pruning method based on ar...
Jürgen Brauer, Bonifaz Stuhr - 2020
This work combines Convolutional Neural Networks (CNNs), clustering via Self-Organizing Maps (SOMs) and Hebbian Learning to propose the building blocks of Convolutional Self-Organizing Neural Networks (CSNNs), which learn representations in an unsupervised and Backpropagation-free manner. Our approach replaces the learning of traditional convolut...
Zhiguo Cao, Chunhua Shen, Liang Liu, Chengxin Liu, Hao Lu, Haipeng Xiong - 2020
Visual counting, a task that aims to estimate the number of objects from an image/video, is an open-set problem by nature, i.e., the number of population can vary in [0, inf) in theory. However, collected data and labeled instances are limited in reality, which means that only a small closed set is observed. Existing methods typically model this ...
