Lele Cao

Lele Cao
EQT

Doctor of Engineering

About

31
Publications
22,068
Reads
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230
Citations

Publications

Publications (31)
Conference Paper
Full-text available
Robotic tactile recognition aims at identifying target objects or environments from tactile sensory readings. The advancement of unsupervised feature learning and biological tactile sensing inspire us proposing the model of 3T-RTCN that performs spatio-temporal feature representation and fusion for tactile recognition. It decomposes tactile data in...
Preprint
Full-text available
Deep clustering (DC) has become the state-of-the-art for unsupervised clustering. In principle, DC represents a variety of unsupervised methods that jointly learn the underlying clusters and the latent representation directly from unstructured datasets. However, DC methods are generally poorly applied due to high operational costs, low scalability,...
Conference Paper
Full-text available
Mobile gaming has become increasingly popular due to the growing usage of smartphones in day to day life. In recent years, this advancement has led to an interest in the application of in-game recommendation systems. However, the in-game recommendation is more challenging than common recommendation scenarios, such as e-commerce, for a number of rea...
Preprint
Full-text available
Sentence embedding refers to a set of effective and versatile techniques for converting raw text into numerical vector representations that can be used in a wide range of natural language processing (NLP) applications. The majority of these techniques are either supervised or unsupervised. Compared to the unsupervised methods, the supervised ones m...
Preprint
Full-text available
Investment professionals rely on extrapolating company revenue into the future (i.e. revenue forecast) to approximate the valuation of scaleups (private companies in a high-growth stage) and inform their investment decision. This task is manual and empirical, leaving the forecast quality heavily dependent on the investment professionals' experience...
Preprint
Full-text available
Many adaptations of transformers have emerged to address the single-modal vision tasks, where self-attention modules are stacked to handle input sources like images. Intuitively, feeding multiple modalities of data to vision transformers could improve the performance, yet the inner-modal attentive weights may also be diluted, which could thus under...
Chapter
Deep clustering (DC) has become the state-of-the-art for unsupervised clustering. In principle, DC represents a variety of unsupervised methods that jointly learn the underlying clusters and the latent representation directly from unstructured datasets. However, DC methods are generally poorly applied due to high operational costs, low scalability,...
Article
Full-text available
Tactile recognition enables robots identify target objects or environments from tactile sensory readings. The recent advancement of deep learning and biological tactile sensing inspire us proposing an end-to-end architecture ROTConvPCE-mv that performs tactile recognition using residual orthogonal tiling and pyramid convolution ensemble. Our approa...
Conference Paper
Full-text available
We present an approach to learn and deploy human-like playtesting in computer games based on deep learning from player data. We are able to learn and predict the most “human” action in a given position through supervised learning on a convolutional neural network. Furthermore, we show how we can use the learned network to predict key metrics of new...
Conference Paper
Haptic perception is to identify different targets from haptic input. Haptic data have two prominent features: sequentially real-time and temporally correlated, which calls for a fixed-budget and recurrent perception procedure. Based on an efficient-robust spatio-temporal feature representation, we handle the problem with a bounded online-sequentia...
Article
Full-text available
Tactile recognition aims at identifying target objects according to tactile sensory readings. Tactile data have two salient properties: 1) sequentially real-time and 2) temporally correlated, which essentially calls for a real-time (i.e., online fixed-budget) and recurrent recognition procedure. Based on an efficient and robust spatio-temporal feat...
Article
Full-text available
Encoding time-series with Linear Dynamical Systems (LDSs) leads to rich models with applications ranging from dynamical texture recognition to video segmentation to name a few. In this paper, we propose to represent LDSs with infinite-dimensional subspaces and derive an analytic solution to obtain stable LDSs. We then devise efficient algorithms to...
Article
Full-text available
Robot recognition tasks usually require multiple homogeneous or heterogeneous sensors which intrinsically generate sequential, redundant, and storage demanding data with various noise pollution. Thus, online machine learning algorithms performing efficient sensory feature fusion have become a hot topic in robot recognition domain. This paper propos...
Conference Paper
To address the difficulties of “data noise sensitivity” and “cluster center variance” in mainstream clustering algorithms, we propose a novel robust approach for identifying cluster centers unambiguously from data contaminated with noise; it incorporates the strength of homophilic degrees and graph kernel. Exploiting that in-degrees can breed the h...
Article
Robotic tactile recognition aims at identifying target objects or environments from tactile sensory readings. The advancement of unsupervised feature learning and biological tactile sensing inspire us proposing the model of 3T-RTCN that performs spatio-temporal feature representation and fusion for tactile recognition. It decomposes tactile data in...
Article
Full-text available
The random-hidden-node extreme learning machine (ELM) is a much more generalized cluster of single-hidden-layer feed-forward neural networks (SLFNs) which has three parts: random projection, non-linear transformation, and ridge regression (RR) model. Networks with deep architectures have demonstrated state-of-the-art performance in a variety of set...
Conference Paper
Full-text available
In this paper, a novel method for slip detection using a capacitive sensor is proposed. We perform the Discrete Wavelet Transform (DWT) on the original signals of sensor. By comparing different wavelets, we find that the Haar wavelet is the most suitable to separate different frequency components. After performing the DWT by using the Haar wavelet,...
Article
Full-text available
Multi-agent microsimulation, as a third way of doing science other than induction and deduction methods, is explored to aid subway carriage design in this paper. Realizing that passenger behavior shapes the environment and in turn is shaped by the environment itself, we intend to model this interaction and examine the effectiveness and usability of...
Chapter
The random-hidden-node based extreme learning machine (ELM) is a much more generalized cluster of single-hidden-layer feed-forward neural networks (SLFNs) whose hidden layer do not need to be adjusted, and tends to reach both the smallest training error and the smallest norm of output weights. Deep belief networks (DBNs) are probabilistic generativ...
Article
Full-text available
This paper describes the design concept of Music Waggon which aims to promoting the use of public transportation in Stockholm. We introduce our design approach, design problem analysis and design solution. Finally we present the evaluation and conceptual improvements that might be done in the future.

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