1,417 research outputs found
Fat Boy Can\u27t Fly
āFat Boy Canāt Flyā is a 3 minutes 52 seconds long animated graduate thesis film. It tells an inspiring story about how a boy whose particular flaw prevents him from flying his sword to the mountaintop along with the others, but turns his weakness in to a strength to finally reach the peak of the mountain.
By combining 2D and 3D techniques, this film creates a relatively unique visual effect with vivid animation. It helps audiences go into the world to understand the characters and story.
This paper outlines the whole film creation process from the very beginning of an idea development stage though to the screening, critique and response. It describes all my inventions, obstacles, failures and successes
Capturing Topology in Graph Pattern Matching
Graph pattern matching is often defined in terms of subgraph isomorphism, an
NP-complete problem. To lower its complexity, various extensions of graph
simulation have been considered instead. These extensions allow pattern
matching to be conducted in cubic-time. However, they fall short of capturing
the topology of data graphs, i.e., graphs may have a structure drastically
different from pattern graphs they match, and the matches found are often too
large to understand and analyze. To rectify these problems, this paper proposes
a notion of strong simulation, a revision of graph simulation, for graph
pattern matching. (1) We identify a set of criteria for preserving the topology
of graphs matched. We show that strong simulation preserves the topology of
data graphs and finds a bounded number of matches. (2) We show that strong
simulation retains the same complexity as earlier extensions of simulation, by
providing a cubic-time algorithm for computing strong simulation. (3) We
present the locality property of strong simulation, which allows us to
effectively conduct pattern matching on distributed graphs. (4) We
experimentally verify the effectiveness and efficiency of these algorithms,
using real-life data and synthetic data.Comment: VLDB201
A Closer Look at the Adversarial Robustness of Deep Equilibrium Models
Deep equilibrium models (DEQs) refrain from the traditional layer-stacking
paradigm and turn to find the fixed point of a single layer. DEQs have achieved
promising performance on different applications with featured memory
efficiency. At the same time, the adversarial vulnerability of DEQs raises
concerns. Several works propose to certify robustness for monotone DEQs.
However, limited efforts are devoted to studying empirical robustness for
general DEQs. To this end, we observe that an adversarially trained DEQ
requires more forward steps to arrive at the equilibrium state, or even
violates its fixed-point structure. Besides, the forward and backward tracks of
DEQs are misaligned due to the black-box solvers. These facts cause gradient
obfuscation when applying the ready-made attacks to evaluate or adversarially
train DEQs. Given this, we develop approaches to estimate the intermediate
gradients of DEQs and integrate them into the attacking pipelines. Our
approaches facilitate fully white-box evaluations and lead to effective
adversarial defense for DEQs. Extensive experiments on CIFAR-10 validate the
adversarial robustness of DEQs competitive with deep networks of similar sizes.Comment: Accepted at NeurIPS 2022. Our code is available at
https://github.com/minicheshire/DEQ-White-Box-Robustnes
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