Date/Time : 22 Oct 2021 10:00
Venue : via Zoom
Speaker : Lee Hwee Kuan, Bioinformatics Institute, Agency for Science, Technology and Research

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Meeting ID: 977 560 6531
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Abstract:  A novel weakly supervised tasks has been developed inspired by an application on cancer diagnosis. Given a bag of instances, we ask the question of how many unique classes of objects there are in the bag. This task was presented for the first time in our recent paper. Developing this task was inspired by the need to ask this question. “Given a cancer tissue sample, how many type of cancer cells there are in this sample”. We call this unique class count and formally proved that unique class count problem can be solved using a deep neural network in the multiple instance learning paradigm. We further apply our algorithm to prediction tumour purity in a cancer tissue sample. Tumour purity is the proportion of cancer cells in the tumour tissue. An accurate tumour purity estimation is crucial for accurate pathologic evaluation and for sample selection to minimize normal cell contamination in high throughput genomic analysis. Moreover, spatial tumour purity maps can help better understand the tumour microenvironment as a key determinant in tumour formation and therapeutic response.

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Date/Time : 15 Oct 2021 10:00
Venue : via Zoom
Speaker : Ju Sun, University of Minnesota

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Meeting ID: 977 560 6531
Passcode: 123321

Abstract:  Phase retrieval (PR) concerns the recovery of a signal (1D, 2D, or 3D) from its Fourier magnitudes, and is central to numerous areas of scientific imaging. Over the past decade, there have been numerous breakthroughs in understanding simplified or modified versions of the problem, but the existence of effective and robust methods for the original PR problem remains elusive and so theoretical insights are out of the question. In this talk, I will discuss why PR is difficult, the strength and limitation of conventional formulations, the promise and perils of deep learning approaches, as well as our recent methods that solve practical PR problems in an unprecedented regime.

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Date/Time : 08 Oct 2021 10:00
Venue : via Zoom
Speaker : Peter Mueller, University of Texas at Austin

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Meeting ID: 977 560 6531
Passcode: 123321

Abstract: Nonparametric Bayesian methods allow for principled model-based inference on unknown functions, distributions and structures without restriction to specific parametric models. The flexible prediction rules and the more realistic and complete description of related uncertainties are useful in many examples of clinical trial design and related data analysis. In this talk we review four related examples. The first example involves the comparison of two event time distributions for the time until resolution of air leaks after lung surgery. The second example is about inference for dynamic treatment regimes. Another example is a Bayesian basket trial design with a nonparametric Bayesian survival regression to find benefiting subpopulations. A last example is about the construction of a synthetic control arm to supplement a single-arm trial using real world data.

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Date/Time : 01 Oct 2021 10:00
Venue : via Zoom
Speaker : Li Cheng, University of Alberta

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Meeting ID: 977 560 6531
Passcode: 123321

Abstract:  Recent advancement of visual behavior analysis has opened door to many interesting applications. In this talk, I will discuss our research efforts toward addressing the related tasks of 3-D pose and shape estimation, action quality assessment, and motion synthesis of humans and beyond, from images and videos. Looking forward, our results could be applied to everyday life scenarios such as natural user interface, AR/VR, robotics, and gaming, among others.

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Date/Time : 24 Sep 2021 10:00
Venue : via Zoom
Speaker : Weijie Su, University of Pennsylvania

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Meeting ID: 977 560 6531
Passcode: 123321

Abstract:  The remarkable development of deep learning over the past decade relies heavily on sophisticated heuristics and tricks. To better exploit its potential in the coming decade, perhaps a rigorous framework for reasoning deep learning is needed, which however is not easy to build due to the intricate details of modern neural networks. For near-term purposes, a practical alternative is to develop a mathematically tractable surrogate model that yet maintains many characteristics of deep learning models.

This talk introduces a model of this kind as a tool toward understanding deep learning. The effectiveness of this model, which we term the Layer-Peeled Model, is evidenced by two use cases. First, we use this model to explain an empirical pattern of deep learning recently discovered by David Donoho and his students. Moreover, this model predicts a hitherto unknown phenomenon that we term Minority Collapse in deep learning training. This is based on joint work with Cong Fang, Hangfeng He, and Qi Long.

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Date/Time : 17 Jun 2021 10:00
Venue : via Zoom
Speaker : Rui Wang, University of California at San Diego

Join Zoom Meeting https://nus-sg.zoom.us/j/88213418159?pwd=K2lPSXEvemZGSXhiVGYxTVUzQ0d3QT09 Meeting ID: 882 1341 8159 Password: pcasvd Modelling complex physical dynamics is a fundamental task in science and engineering. Traditional physics-based models are interpretable but usually require significant computational resources and expertise. While deep learning (DL) provides alternatives for efficiently emulating nonlinear dynamics, it does not necessarily obey the physics laws or generalize well across different systems. Thus, there is a growing need for incorporating prior physics knowledge into DL models so that we can take the best of both types of approaches. We present three physics-guided DL approaches for spatiotemporal forecasting. We first present TF-Net, which marries the numerical techniques with custom-designed neural networks, that can predict accurate physical fields that obey desirable physical characteristics. We also provide two approaches to improve generalization across different systems. One is to design deep equivariant models that respect given symmetries of physical dynamics, and the other one is to infer shared time-invariant characteristics by leveraging meta-learning techniques.

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Date/Time : 03 Jun 2021 16:00
Venue : via Zoom
Speaker : Liu Yong, Institute of High Performance Computing

Join Zoom Meeting https://nus-sg.zoom.us/j/88213418159?pwd=K2lPSXEvemZGSXhiVGYxTVUzQ0d3QT09 Meeting ID: 882 1341 8159 Password: pcasvd Transformer has been developed as one of most impactful deep neural network architecture for Natural Language Processing (NLP) tasks such as machine translation, question & answer, and reading comprehension. Its effectiveness has been demonstrated by the impressive performance of large language model such as GPT-3 to produce convincing stream of human-like text. This talk will cover the mechanism of transformer and how it has been used for computer vision tasks such as image classification, followed by our recent work on transformer-based medical image segmentation for multiple diseases. Compared with representative existing methods, the proposed method consistently achieved the highest segmentation accuracy, and exhibited good cross-domain generalization capabilities.

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Date/Time : 27 May 2021 16:00
Venue : via Zoom
Speaker : Niangjun Chen, Singapore University of Technology and Design

Join Zoom Meeting https://nus-sg.zoom.us/j/88213418159?pwd=K2lPSXEvemZGSXhiVGYxTVUzQ0d3QT09 Meeting ID: 882 1341 8159 Password: pcasvd Many problems in data science and cloud computing can be cast as online optimization problems. In this talk, we study general online optimization problems with uncertainties, where the payoff or cost information is only known to the decision-maker up to some bounded uncertainty set. We develop an algorithmic framework, CRT, that can be applied to both online cost minimization problems with revenue constraints and online revenue maximization problems with inventory constraints. In both scenarios, we design online algorithms based on the CRT framework and prove their optimality in terms of competitive ratio. Furthermore, while prior works on online nonlinear optimization with inventory constraint require the revenue functions to be concave, we show that CRT-based algorithms have optimal competitive ratios in general online optimization settings with non-convex cost functions or non-concave revenue functions. We demonstrate our proposed algorithms’ practical performance via trace-driven simulations in a cloud computing application against prominent existing algorithms.

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Date/Time : 20 May 2021 16:00
Venue : via Zoom
Speaker : Yuan FANG, Singapore Management University

JJoin Zoom Meeting https://nus-sg.zoom.us/j/88213418159?pwd=K2lPSXEvemZGSXhiVGYxTVUzQ0d3QT09 Meeting ID: 882 1341 8159 Password: pcasvd Heterogeneous Information Networks (HIN) often model complex graph-structured data with multi-typed relationships between different types of objects. The heterogeneity gives rise to the rich semantics on HINs, such as different user interactions on social networks, and different functional associations between proteins on a biological network. Metagraphs, which model recurring subgraph structures on HINs, emerge as a powerful tool to capture the rich semantics. Compared to their simpler variant metapaths, metagraphs are higher-order structures with stronger expressiveness and can be regarded as nonlinear functions of metapaths. Not surprisingly, metagraphs can be used to derive effective graph representations on HINs, to enable various downstream tasks such as node classification and link prediction. The efficacy of metagraph-based representations can be demonstrated in various domains including social, cyberphysical and biological networks.

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