Events

Eng Wei Koo (Keysight) – Sky to Lab – design challenges and testing considerations leading to successful deployments

Seow Chun Yong (Ensign InfoSecurity) – Hosting Applications on the Cloud and Cloud Security Challenges

ARISE Program – Validate

ARISE Program – Validate

Progressively applying Gaussian noise transforms complex data distributions to approximately Gaussian. Reversing this dynamic defines a generative model. When the forward noising process is given by a Stochastic Differential Equation (SDE), Song et al. (2021) demonstrate how the time inhomogeneous drift of the associated reverse-time SDE may be estimated using score-matching. A limitation of this approach is that the forward-time SDE must be run for a sufficiently long time for the final distribution to be approximately Gaussian. In contrast, solving the Schrödinger Bridge problem (SB), i.e. an entropy-regularized optimal transport problem on path spaces, yields diffusions which generate samples from the data distribution in finite time. We present Diffusion SB (DSB), an original approximation of the Iterative Proportional Fitting (IPF) procedure to solve the SB problem, and provide theoretical analysis along with generative modeling experiments. The first DSB iteration recovers the methodology proposed by Song et al. (2021), with the flexibility of using shorter time intervals, as subsequent DSB iterations reduce the discrepancy between the final-time marginal of the forward (resp. backward) SDE with respect to the prior (resp. data) distribution. Beyond generative modeling, DSB offers a widely applicable computational optimal transport tool as the continuous state-space analogue of the popular Sinkhorn algorithm (Cuturi, 2013).

HASS Colloquium Series: Achieving the digital equity for students with special educational needs by Dr. Sin Kuen Fung Kenneth

James Chong (The Tesseract, NCS Group) – “How to Sell a Better Mousetrap” – How Advanced Analytics and Generative AI is changing the world and how to convince people to use it

According to a UN report from 2022, time is running out to limit temperature rises to 1.5C by 2050. Around $3.5 trillion climate investment per year is needed to build the net zero economy by 2050. This investment is possible only with significant contribution from the private financial sector. This is specifically the case in emerging markets, where public investments aren’t growing as quickly as needed to meet climate targets. There are a quite a few issues related to data disclosures, transparency and complexities, which needs to be tackled in order to efficiently manage the private sustainable investment need. Data Science and Innovation can help tackle these issues and assist the community is making quick and accurate investment decisions.

ARISE Program – Validate

Pye Sone Kyaw (Government Technology Agency) – Leveraging AI/ML for Transformative Solutions in Diverse Industries