AI/ML Seminar Series

Standard

Weekly Seminar in AI & Machine Learning
Sponsored by Cylance

Apr 8
No Seminar

Apr 15
Bren Hall 4011
1 pm
Daeyun Shin
PhD Candidate
Dept of Computer Science
UC Irvine

In this presentation, I will present our approach to the problem of automatically reconstructing a complete 3D model of a scene from a single RGB image. This challenging task requires inferring the shape of both visible and occluded surfaces. Our approach utilizes viewer-centered, multi-layer representation of scene geometry adapted from recent methods for single object shape completion. To improve the accuracy of view-centered representations for complex scenes, we introduce a novel “Epipolar Feature Transformer” that transfers convolutional network features from an input view to other virtual camera viewpoints, and thus better covers the 3D scene geometry. Unlike existing approaches that first detect and localize objects in 3D, and then infer object shape using category-specific models, our approach is fully convolutional, end-to-end differentiable, and avoids the resolution and memory limitations of voxel representations. We demonstrate the advantages of multi-layer depth representations and epipolar feature transformers on the reconstruction of a large database of indoor scenes.

Project page: https://www.ics.uci.edu/~daeyuns/layered-epipolar-cnn/

Apr 22
Bren Hall 4011
1 pm
Mike Pritchard
Assistant Professor
Dept. of Earth System Sciences
University of California, Irvine

I will discuss machine-learning emulation of O(100M) cloud-resolving simulations of moist turbulence for use in multi-scale global climate simulation. First, I will present encouraging results from pilot tests on an idealized ocean-world, in which a fully connected deep neural network (DNN) is found to be capable of emulating explicit subgrid vertical heat and vapor transports across a globally diverse population of convective regimes. Next, I will demonstrate that O(10k) instances of the DNN emulator spanning the world are able to feed back realistically with a prognostic global host atmospheric model, producing viable ML-powered climate simulations that exhibit realistic space-time variability for convectively coupled weather dynamics and even some limited out-of-sample generalizability to new climate states beyond the training data’s boundaries. I will then discuss a new prototype of the neural network under development that includes the ability to enforce multiple physical constraints within the DNN optimization process, which exhibits potential for further generalizability. Finally, I will conclude with some discussion of the unsolved technical issues and interesting philosophical tensions being raised in the climate modeling community by this disruptive but promising approach for next-generation global simulation.
Apr 29
Bren Hall 4011
1 pm
Nick Gallo
PhD Candidate
Department of Computer Science
University of California, Irvine

Large problems with repetitive sub-structure arise in many domains such as social network analysis, collective classification, and database entity resolution. In these instances, individual data is augmented with a small set of rules that uniformly govern the relationship among groups of objects (for example: “the friend of my friend is probably my friend” in a social network). Uncertainty is captured by a probabilistic graphical model structure. While theoretically sound, standard reasoning techniques cannot be applied due to the massive size of the network (often millions of random variable and trillions of factors). Previous work on lifted inference efficiently exploits symmetric structure in graphical models, but breaks down in the presence of unique individual data (contained in all real-world problems). Current methods to address this problem are largely heuristic. In this presentation we describe a coarse to fine approximate inference framework that initially treats all individuals identically, gradually relaxing this restriction to finer sub-groups. This produces a sequence of inference objective bounds of monotonically increasing cost and accuracy. We then discuss our work on incorporating high-order inference terms (over large subsets of variables) into lifted inference and ongoing challenges in this area.