AI Technology & Industry Review
“Automated failure attribution” is a crucial component in the development lifecycle of Multi-Agent systems. It has the potential to transform the challenge of identifying “what went wrong and who is to blame” from a perplexing mystery into a quantifiable and analyzable problem
This paper uncovers the secrets of highly efficient swimming at varying speeds which could inform the design of next generation underwater drones.
A novel module that effectively and efficiently propagates information through an arbitrarily long video, with constant complexity w.r.t. number of frames and linear complexity w.r.t. resolution.
The research develop machine learning methods that enable virtual agents (such as avatars from a computer game) to communicate non-verbally.
UmlsBERT is a deep Transformer network architecture that incorporates clinical domain knowledge from a clinical Metathesaurus in order to build ‘semantically enriched’ contextual representations that will benefit from both the contextual learning and domain knowledge.
This paper is the first to systematically study the security of Multi-Sensor Fusion (MSF) based localization in high-autonomy Autonomous Vehicles (AVs). Researchers design FusionRipper, a novel and general attack that opportunistically captures and exploits take-over vulnerabilities.
This research proposes an efficient and cost-effective solution for multi-frame video interpolation.
The paper outlines a predictive model we’ve developed that has the potential to help significantly reduce wasteful healthcare spending.
This paper presents a novel contrastive framework for unsupervised graph representation learning.
A new simple baseline for few-shot learning that achieves state-of-the-art performance.
We present a general framework for exemplar-based image translation, which synthesizes a photo-realistic image from the input in a distinct domain, given an exemplar image.
The proposed method outperforms supervised methods and unsupervised translation methods on restoring real photos.
Researchers introduce the notion of deflecting adversarial attacks, which presents a step towards ending the battle between attacks and defenses.
Researchers investigate how different ImageNet models affect transfer accuracy on domain adaptation problems.
Batchboost is a simple technique to accelerate ML model training by adaptively feeding mini-batches with artificial samples which are created by mixing two examples from the previous step – in favor of pairing those that produce the difficult one.